We present a new approach to the design and optimization of colloidal and nanoparticle assemblies that is generalizable to many types of materials problems. We present a rigorous statistical thermodynamic framework underlying "digital alchemy," in which material features are optimized computationally for target structures and function in an automated fashion. We demonstrate the usefulness of this approach for the self assembly of hard polyhedra, as well as colloidal spheres interacting via oscillating pair potentials, into complex crystals. We discuss extensions of digital alchemy to other materials design problems.
*in collaboration with G. van Anders, D. Klotsa and P. Dodd

Crystalline nanoparticles (CNPs) covered by ligands can self-organize into highly periodic supercrystals (SCs), which possess a range of crystal symmetries (e.g., face-centered cubic FCC, body centered cubic BCC, simple hexagonal SH etc.). These SCs exhibit a variety of exotic properties (e.g, reversible metal-to-insulator transitions, enhanced conductivity), which make them suitable for various energy, technology and device applications. Despite the widespread efforts in synthesis of SCs, a fundamental understanding of the physical forces underlying the self-assembly process is still in its infancy. In particular, the key role played by ligands in driving the self-organization of CNPs into SCs with a particular crystal symmetry, and the influence of their nano-scale dynamics on the physical properties of SCs are still unclear. Here, using coarse grained molecular dynamics (CGMD) based on the well-known MARTINI force field, we demonstrate that the self-assembly of CNPs is critically controlled by the decorating ligands via their (a) self-interaction, (b) mobility on the CNP surface, (c) re-organization/inter-digitation, and associated dynamics. Our quasi-hydrostatic high-pressure small-angle X-ray scattering (SAXS) and X-ray diffraction (XRD) studies showed that the size of CNP building blocks control the symmetry of self assembled SC. Smaller (~4.7) nm PbS particles decorated by oleic acid ligands organize into BCC SC, while at larger sizes (~7 nm) they assemble into FCC SC. Furthermore, the BCC SCs show near-perfect elastic behavior under applied pressures ~55 GPa, while FCC SC show some hysteresis during the loading-unloading cycles without losing its structural integrity. Our CGMD simulations predict pressure behavior during the loading-unloading cycles consistent with our experiments. In addition, these simulations indicate the ligands strongly govern the pressure behavior of SCs via (a) re-arranging on the CNP surface, and (b) inter-digitating with neighboring CNPs without being locked in permanently. These enable the SCs to retain their structural organization even at extremely high pressures by merely reducing the distance between neighboring CNPs. We will discuss these results in the context of engineering novel ordered CNP architectures with prescribed mechanical, electronic, and physical properties.

The thiolate group (SR) protected gold nanoclusters (AuNCs) have attracted intense concerns due to the potential applications in catalysis, sensor and biomedicine. A series of Au_n(SR)_m nanoclusters were synthesized in experiment with size-dependent physical and chemical properties. The chemical formula of Au_n(SR)_m can be identified by mass spectrum analysis into matched n and m. For example, n = 18 is corresponding to m = 14 as Au₁₈(SR)₁₄. However, determination of the atomic structure of Au_n(SR)_m is still a challenge in experiments largely because most single crystals of which are difficult to be obtained. To identify the exact structure of Au_n(SR)_m, the theoretical scientists want to make contributions from the numerical study together with the experimental characterization. Till now, there are some independent predicted or theoretical and experimental consistent structure of Au_n(SR)_m have been achieved, including Au₁₈(SR)₁₄, Au₂₀(SR)₁₆, Au₂₄(SR)₂₀, [Au₂₅(SR)₁₈]^-, and Au₃₈(SR)₂₄. In the theoretical prediction of Au_n(SR)_m, the density functional theory (DFT) calculations are important tools to give a deep insight into the molecular structure of Au_n(SR)_m. The general structure of these nanoclusters includes a high-symmetric Au-core and a series of semiring staple motifs. The super atom model is utilized to identify the high stable structure of Au_n(SR)_m by calculating the total number of free valence electron.
Recently, we successfully synthesize the stable Au₂₄Peptide₈ complex (Peptide = CysCys-6peptides). As a biomedical probe, Au₂₄Peptide₈ is utilized to achieve the quantitation of membrane protein integrin for cancer early diagnosis. However, the atomic structure of Au₂₄Peptide₈ is unknown. Especially, it is critical to identify the dominant coupling structure Au₂₄(SCys-CysS)₈ for the probe characteristics. Although there are 24 Au atoms in both Au₂₄(SCys-CysS)₈ and Au₂₄(SR)₂₀, the ratio of Au:S in amino acid coated Au₂₄(SCys-CysS)₈ is unmatched to that in SR coated Au₂₄(SR)₂₀ with the known molecular structure. In this work, we focus on identifying the atomic structure of Au₂₄(SCys-CysS)₈. According to the super atom model, the total number of Au₂₄(SCys-CysS)₈ is 8e, which satisfies the number of electrons to fill closed electron shell within the framework of spherical jellium model. The atomic structure of Au₂₄(SCys-CysS)₈ is resolved by the numerical calculations into Au₁₃ cubocatahedral core and protected -Au-SCys- motifs. For the lowest-energy isomer, we both theoretically compute and experimentally measure its UV-vis absorption spectrum, which is in the good agreement to each other. It is the new knowledge for the atomic structure of amino acid coated AuNCs. Meanwhile, the atomic details of amino acid coated AuNCs support us to applicate the biocompatibility more effectively in biomedicine.

Although the advent of modern theoretical techniques and high performance computing have provided us with a range of sophisticated tools for simulating nanoscale materials, modeling realistic systems to reproduce real experiments remains challenging. Each of the applications for nanoparticles, such as energy conversion and storage, communications, electronics or medicine, all have different material needs; but share one thing in common. All involve large collections of small particles, the properties and stability of which will depend on how and where they are used. Consider for example the case of a non-toxic nanoparticle, such as nanodiamond, being used in nanomedicine the treatment of cancer. Individual particles using for this purpose need to be mechanically stable, able to adsorb and desorb drugs in a controlled fashion, capable of forming porous meso-structures to moderate doses and dose rates, and able operate reliably under relevant thermochemical conditions (not “clean”, and in a vacuum). This means that simulating this application transcends length scales, from molecular, to nanoscale and even micron scale; must capture the complexity of the individual particles and their interactions; and must include the influence of the relevant environments and the proximity of large numbers of particles. In this presentation we will see how quantum mechanical simulations can be used to underpin higher level models that enable the direct simulation of distributions and mixtures of hundreds of thousands of faceted colloidal particles.

Globally ordered colloidal lattices have broad utility in a wide range of novel optical and catalytic devices, for example, as photonic bandgap materials. However, the self-assembly of stereospecific structures is often confounded by defects. Small free energy differences different crystal polymorphs, making it difficult to produce a single morphology at will. Current techniques to handle this problem usually rely on energy minimization; many colloids have been computationally engineered with anisotropic pairwise interactions to achieve morphological control. However, the complexity of these designs often makes experimental realization difficult. In this presentation, recent computer simulation and theoretical work [1-3] on the effects of polymeric co-solutes on crystallizing colloidal suspensions is summarized. These can be used to direct colloidal structures by relying upon the polymer's entropic interactions resulting from the interplay between the polymer's internal degrees of freedom and the void structure of a material. This represents a novel design paradigm that has the potential to significantly simplify control over colloidal polymorphism. I will elaborate on how to rationally design the co-solute structure to thermodynamically stabilize a single desired polymorph in a binary mixture, and the consequences that thermal perturbations have on this effect [2]. I will then offer insights into how to design temperature-dependent co-solute “switches” that allow the stability of a polymorph to be controlled via experimentally accessible parameters [3].
[1] N. A. Mahynski, A. Z. Panagiotopoulos, D. Meng, and S. K. Kumar; Nature Comm., 5: 4472, 8 pp (2014).
[2] N. A. Mahynski, S. K. Kumar, and A. Z. Panagiotopoulos; Soft Matter, 11: 280-9(2015).
[3] N. A. Mahynski, S. K. Kumar, and A. Z. Panagiotopoulos; Soft Matter, DOI: 10.1039/c5sm00631g(2015).

In the active field of colloidally self assembled materials, simulation of large systems can be necessary to understand phase coexistence and nucleation, or to observe certain novel behaviors such as hexatic phases. One of the basic tools to understanding the thermodynamics of any system is to measure the state functions, but there are computational challenges for the non-differentiable potentials of the simplest (hard) particle models, particularly as system size is increased.
Computing the pressure -- volume state function is historically expensive in hard particle Monte Carlo simulations and traditional methods are inadequate for studying large systems. NpT simulations are far more computationally expensive than simulations in the NVT ensemble and become impossible for large system sizes. Pressure measurement in NVT simulations is analytic for few particle shapes. Perturbative numerical techniques, historically underutilized, are unclear or insufficient as previously published for general application to large systems of arbitrary particle shapes.
We combine and extend previous methods of pressure calculation from a perturbative thermodynamics approach and present an efficient and highly scalable implementation. This has allowed us to extract previously elusive thermodynamic data with the same computational efficiency as the rest of our highly parallel hard particle Monte Carlo code in studies of unprecedentedly large systems.

The self-assembly of finite clusters of colloidal particles into crystalline objects is a topic of technological interest, as a route to produce photonic crystals and other meta-materials. Quantitatively accurate models of the thermodynamics and dynamics of these systems are essential to producing defect-free crystals. Robust methods for controlling the assembly of these crystals would require reduced dimension process-models that link the particle-level dynamics of the colloids to the actuator states. In this paper, we describe the building of such models for two systems comprising 10-100 micron sized silica particles in aqueous solution that employ either a temperature-tunable depletion interaction potential or externally applied electric field as a mechanism to promote the assembly process. We model the assembly process using coarse-grained representations, based on the Fokker-Planck equation, which can capture both the dynamics and the equilibrium properties of these small clusters. We use diffusion maps (DMaps), a machine learning technique to identify the slow, low-dimensional manifolds in these systems. The DMap coordinates are correlated against a set of candidate order parameters (OPs) to identify a suitable choice of observables. The DMap technique is sensitive to the nature of defects observed in these two systems and this is manifest in the correlations with OPs. We construct free energy and diffusivity landscapes in the chosen OPs that serve as reduced order models for process control policy maps providing an optimal route to defect-free crystals.
To quantify the phase behavior, we have also conducted Monte Carlo simulations of these small colloidal clusters and generated potential energy histograms for various levels of the osmotic pressure (for the depletion potential system) that controls the strength of the interactions. We have used potential energy as the histogram variable to identify the fluid-like and solid-like phases. By carefully tuning the osmotic pressure, we observed bimodal distributions in the potential energy space that is indicative of coexistence between fluid-like and solid-like configurations. We report comparisons of phase behavior for these colloidal clusters obtained from the thermodynamic approach outlined here with results from above mentioned Fokker-Planck order parameter approach.

Most colloidal interactions can be tuned by changing properties of the medium. Here we show that activating the colloidal particles with random self-propulsion can induce giant effective interactions between large objects immersed in such a suspension. By performing Brownian dynamics simulations, we systematically study the effective force between two hard walls in a 2D suspension of self-propelled (active)colloidal hard spheres [1]. We find that at relatively high densities, the active colloidal hard spheres can form a dynamic crystalline bridge, which induces a strong oscillating long range dynamic wetting repulsion between the walls. With decreasing the density of active colloids, the dynamic bridge gradually breaks, and an intriguing long range dynamic depletion attraction starts dominating the effective interaction between the two walls. The two long range forces oppose each other, and the effective interaction can be tuned from a long range repulsion into a long range attraction by reducing the density of active particles. Our results open up new possibilities to manipulate the motion and assembly of microscopic objects by using active matter.
[1] R. Ni et al, Phys. Rev. Lett., 114, 018302 (2015)

We investigate a class of “shape allophiles” that fit together like puzzle pieces as a method to access and stabilize desired structures by controlling directional entropic forces. Squares are cut into rectangular halves, which are shaped in an allophilic manner with the goal of re-assembling the squares while self-assembling the square lattice. We examine the assembly characteristics of this system via the potential of mean force and torque, and the fraction of particles that entropically bind. We generalize our findings and apply them to self-assemble triangles into a square lattice via allophilic shaping. Through these studies we show how shape allophiles can be useful in assembling and stabilizing desired phases with appropriate allophilic design.
*This work currently in press in Soft Matter

Many biomolecular systems such as cell membranes and actin filaments are organized as complex and dynamic mesoscopic structures. However, underlying this fascinating degree of organization are molecular-scale features such as protein structural elements and/or local chemistry (e.g., protonation equilibria and nucleotide hydrolysis) that can greatly affect the mesoscopic behavior. Multiscale simulation can help to reveal these molecular features along with their coupling to the mesoscale behavior. Such results for the membrane remodeling by proteins and ATP hydrolysis in actin filaments will be presented in this talk. The lessons learned from these multiscale simulations can also help to guide in the development of novel soft materials and to predict their mesoscopic behavior via systematic coarse-graining of the molecular-scale interactions.

In molecular self-assembly process, molecules spontaneously and reversibly form ordered aggregates through a number of attractive and repulsive forces. It has been shown that some dipeptide systems have the ability to self-assemble into various structures. We found that valine-alanine (Val-Ala) dipeptide molecules produce various self-assembled micro-/nano-structures, including square- and rectangular-plates, wires, and rods, depending on the solvents used, whereas alanine-valine (Ala-Val) dipeptides did not self-assemble into any distinct ordered structure under the same experimental conditions. Experimental as well as modeling studies towards the understanding of peptide self-assembly are still in their infancy. Thus, the fabrication of new peptide structures with desired properties mainly depends on our ability to understand/control peptide-peptide interactions in various solvents. In this work, we investigated the self-assembly of Val-Ala and Ala-Val dipeptides by using molecular modeling in aqueous environment. Molecular modeling studies revealed the decisive role of dipeptide/dipeptide and dipeptide/solvent H-bonding interactions on the final structures formed. Simulations also revealed that each one of the 2- and 4-mers of Val-Ala dipeptides have equally distributed intra- and inter-molecular H-bonds; however, this is not the case for Ala-Val peptides. This structural difference may be the reason for the formation of long-range ordered structures, which observed experimentally for Val-Ala dipeptides contrary to Ala-Val dipeptides. Supported by TUBA GEBIP awards to Demirel G. and Oren EE.

We develop a computational model by coupling implicit solvent coarse-grained dry martini model with a lattice-Boltzmann fluid in order to design and characterize nanostructured soft materials. The long range hydrodynamic effects are included in the system through the implementation of the Lattice-Boltzmann fluid. This removes the necessity of using explicit solvent molecules. The particle dynamics is resolved via the Molecular Dynamics simulation method. Our objective is to generate a stable vesicle composed of single (DPPC) and multiple (DPPC and DOPC) phospholipid species through the use of implicit solvent coarse grained model with long range hydrodynamics in order to investigate physiological processes occurring on the mesoscopic spatio-temporal scales. By using a four to one mapping scale of Martini model, the DPPC molecule is represented by four head beads and eight tail beads. The twelve beads are further divided into four types, each with different interaction energies and length scales. Analogous models are developed for the other phospholipids. The vesicle is initially equilibrated using the Dry Martini force field without Lattice-Boltzmann fluid. In order to couple Dry Martini model with Lattice-Boltzmann fluid, 36 nodes are built on the surface of the hydrophilic head beads of the phospholipid molecules. These nodes and the surrounding head beads are treated as a rigid particle so that hydrodynamic forces can be transferred from fluid to lipid molecules. We investigate the dynamical, structural and morphological properties of the resulting hybrid aggregates. The results of our investigations can be used for the design and prediction of novel hybrid soft and bio-materials at the mesoscale for various applications in medicine, sensing and energy.

Spiders spin their silk from an aqueous solution to a solid fiber in ambient conditions. However, to date the assembly mechanism in the spider gland has not been satisfactorily explained. By means of molecular dynamics simulations, we investigated structural transitions during the assembly of N. Clavipes MaSP1 dragline silk. These shear induced transitions in the core sequence ((AAAAAAGGAGQGGYGGLGSQGAGRGGLGGQGAG)_n; n=2-6) are important for the formation of aligned β-sheet nanostructures in the fiber. We found that a critical shear stress of 300 - 800 MPa induces an α-β-transition in the core poly-alanine region of the MaSP1 structure that is maintained after relaxation in aqueous media. While the transition stress is independent of the number of MaSP1 repeats, the β-crystal is stable only in larger configurations. The minimum size for a stable structure was shown to be six poly-alanine regions for a single chain and four poly-alanine regions for simulations performed on assemblages of multiple chains. This marks the smallest molecule size that gives rise to a ‘silk-like&’ structure. In addition, the presence of water was found to stimulate the formation of additional β-sheet secondary structure components not present after the shear induced transition.
Using a probability analysis of the secondary structure allowed us to identify specific amino acids that transition from α-helix to β-sheet. In addition to portions of the poly-alanine section these amino acids include glycine, leucine and glutamine. The stability of the β-sheet structure appears to arise from a close proximity in space of helices in the initial spidroin state. Our results are in agreement with the forces exerted by spiders in the silking process and the experimentally determined global secondary structure of spidroin and pulled MaSp1 silk. This is the first time that the shear stress has been quantified in connection with a secondary structure transition. Our study emphasizes the role of shear in the assembly process of silk and can guide the design of microfluidic devices that attempt to mimic the natural spinning process.

Many applications of fibrous materials, such as scaffolds for tissue engineering or applications in fuel cells, rely on the control of network properties such as void size distributions. Fibrous networks formed in magnetorheological fluids offer the possibility of tuning such fibrous structures. A deep understanding of the mechanisms that lead to network formation is a prerequisite for controlling network architecture, potentially giving rise to novel devices.
Here, we use super-paramagnetic polystyrene particles and optical video microscopy to investigate field-induced structure formation at the mesoscale and single particle level. First we address the chain formation at low packing fractions and use simple statistical considerations to fully predict the distribution of coordination numbers for individual particles. The validity of our approach is confirmed using computer simulations in which the starting configuration of the chain forming system is systematically varied from random to deterministic. We show that chain growth proceeds via a compound Poisson process, such that at all times the coordination number distribution fully characterises the chain length distribution and vice versa.
We then investigate the formation of fibrous networks at higher packing fractions. We use statistical geometry and statistical modelling to quantify and rationalise the observed structures. We also apply our approach of analysing self-assembly through coordination number statistics to these network forming systems.
Finally, we explore the possibilities of network formation in mixtures of super-paramagnetic and non-magnetic particles suspended in a ferrofluid. In the presence of a magnetic field, these magnetic holes and excesses act as dipoles pointing in opposite directions and assemble into structures distinct from those of the single component system.

In this talk I'll discuss the use of molecular simulation techniques to probe the interaction between nanoparticle and biomolecules. This is motivated by the increasing interest in the environmental and health impact of nano materials. Molecular simulations are able to provide a molecular level understanding of physical factors that dictate the strength and specificity of such interactions. I'll discuss the challenges associated with such simulations and the relevant developments made in our group, which touch upon both sampling methodologies and model potentials at different resolutions. Applications to nano-particle interactions with peptides and lipids will be presented as examples.

Nucleic acid based nanotechnology and gene theraphy approaches depend on the compaction, or packaging, of the nucleic acids DNA and RNA. Though there has been much experimental work on the interactions of DNA with proteins, the atomic details of DNA- nanoparticle binding remain to be comprehensively elucidated. Even less is known about the binding of double stranded RNA with cationic molecules. Here, we report the results of a comprehensive large scale all-atom simulation investigation of the binding ligand-functionalized gold nanoparticles (NPs) binding to the nucleic acids double stranded DNA and RNA as a function of NP charge and solution salt con- centration. Our simulations show that low charge NPs bind to DNA and cause little distortion of the DNA helix, however, nanoparticles with charges of +30 or higher cause DNA to bend and wrap in a way similar to nucleosome. Moreover, shape of the NP ligand corona plays an essential role in quality of DNA wrapping. The nanoparticles cause different behavior with short segments of RNA in that they are not able to induce bending for even the most highly charged nanoparticles in 0.1M NaCl. To compact RNA, a combination of highly charged nanoparticles with low salt concentration is required.

First, we discuss our collaborative studies of colloidal nanoparticles with bio-active ligands. In our modeling of protein corona, we examine how different types of ligands control the adsorption, configuration and activity of proteins on nanoparticle surfaces [1]. Next, we investigate how nanoparticles couple to and affect the functions of larger cellular and biological units [2]. In our collaborative modeling of nanomedicines, we discuss the stability of micelles, their ability to carry drugs, and their interaction with membranes and receptors [3]. Many different types of monomeric units are considered and compared in these nanomedicines.
[1] W. Lyn et al., submitted.
[2] S. Sen, in preparation.
[3] L. Vukovicacute; et al., JACS 133, 13481 (2011); Langmuir 29, 15747 (2013); H.-J. Hsu et al., Macromol., 47, 6911 (2014); C. R. James et al., JACS 136, 11316 (2014); A. Lote et al., Macro Lett. 3, 829 (2014) & submitted.

Gold nanoparticles (NPs) are versatile materials with surface properties that can be chemically modified to mimic typical biological macromolecules. Recently, NPs protected by a binary monolayer of purely hydrophobic and anionic, end-functionalized alkanethiol ligands were found to embed within lipid bilayers as a precursor to non-disruptive, non-endocytic cellular uptake (1). This behavior is highly promising for applications in drug delivery, bioimaging, or biosensing, but optimizing NP coatings for such applications requires an improved understanding of the NP-bilayer insertion mechanism. In this work, we use atomistic molecular dynamics simulations to further elucidate the mechanism by which amphiphilic NPs establish a transmembrane configuration previously predicted by implicit modeling methods (1). We show that NP-bilayer insertion is initiated by the stochastic protrusion of aliphatic lipid tails into solvent and into contact with hydrophobic material in the amphiphilic NP monolayer. Hydrophobic ligands are then shielded within the bilayer, providing a driving force for the insertion process. In contrast, charged ligands face additional barriers to cross the low dielectric constant bilayer core region and stabilize a transmembrane NP configuration. We quantify the free energy cost for translocating charged end groups across the bilayer and show that the process mimics transbilayer lipid flip-flop. Together, our simulations reveal a mechanism for NP-bilayer insertion that is consistent with prior experimental results. Based on this mechanism, we suggest design guidelines for novel NP coatings that maximize NP-bilayer insertion, enabling the creation of novel nano-bio hybrid structures for biomedical applications.
(1) R. C. Van Lehn et al, “Effect of particle diameter and surface composition on the spontaneous fusion of monolayer-protected gold nanoparticles with lipid bilayers,” Nano Letters 13 (2013).

The mechanisms of interactions between inorganic nanoparticles and biomolecules are ubiquitously and critically related to the applications as diverse as biomaterials, photonics, and catalysis. The binding of biomolecules to inorganic calcium phosphate (CaP) phases was demonstrated to mediate the nucleation and growth of hydroxyapatite (HAP, Ca₁₀(PO₄)₆(OH)₂) during bone formation.^{1, 2} It has been previously shown that HAP can induce conformational changes between solution and surface-bound ensembles of the peptide.³ However, as a critical intermediate phase for HAP crystallization, little is known about the interaction mechanisms between amorphous calcium phosphate (ACP) and biomolecules. Molecular simulation has proven to be invaluable in this aspect.⁴ In the present work, we focus on understanding how ACP may affect the conformational behavior of a synthetic CaP-binding peptide VTK (VTKHLNQISQSY), by applying advanced simulation methods and benchmarking to circular dichroism spectra (CD). Taking advantage of the recently implemented parallel-tempering metadynamics⁵, we are able to sample exhaustively the conformational transition of VTK between solution state and ACP-bound state. To examine how ACP nanoparticle surface may influence VTK conformation, detailed analysis of the solution and the surface-bound ensembles of the peptide was performed. The comparison of the respective 2-D free energy landscapes suggests that, for the surface-bound peptide, the α-helix is favored over other conformations, which is reminiscent of the conformational selection mechanism discovered in many biomolecular recognition systems⁶. Interestingly, ACP can affect the relative distribution of peptide conformations in solution by dynamic binding-unbinding process. These two possible scenarios combine to contribute to an increase of random coil ratio in the solution ensemble as a consequence of binding to ACP. These results are confirmed by CD. The present work demonstrates, for the first time, that ACP can induce conformational transitions between solution and bound states of the peptide, which may lead to altering the role of the peptide in mediating HAP crystallization. The dynamic conformational selection and redistribution involved in ACP-peptide binding reveal a new molecular recognition paradigm for inorganic-organic interactions, potentially impacting the strategies in drug design and biomolecular engineering.
References
1. Flade, K.; Lau, C.; Mertig, M.; Pompe, W. Chem. Mater. 2001, 13, 3596-3602.
2. Glimcher, M. J. In Medical Mineralogy and geochemistry, Sahai, N.; Schoonen, M. A. A., Eds. 2006; Vol. 64.
3. Capriotti, L. A.; Beebe, T. P., Jr.; Schneider, J. P. J. Am. Chem. Soc. 2007, 129, 5281-7.
4. Yang, Y.; Zhijun, X.; Qiang, C.; Nita, S. In Biomineralization Sourcebook, DiMasi, E.; Gower, L. B., Eds. 2014; 265-283.
5. Deighan, M.; Pfaendtner, J. Langmuir 2013, 29, 7999-8009.
6. Boehr, D. D.; Nussinov, R.; Wright, P. E. Nat. Chem. Biol. 2009, 5, 789-96.

Drawing a bright line between molecular structure and physical behaviour remains a goal of considerable interest and importance in the study of soft materials. To this end we have created two strategies: A simple, analytic model, derived from fundamental principles (the Locally Correlated Lattice, or LCL, theory), and a coarse-grained simulation approach (the Limited Mobility, or LM, model). The latter method has allowed us to study dynamic heterogeneity and glassy arrest in the bulk and in thin films, for single and for layered multicomponent systems. The LCL theory has been applied to model equibrium properties of complex fluids, solutions and blends. In recent work using the LCL approach we have shown that two pure-component metrics are valuable in predicting complex mixture miscibility: the percent free volume and the cohesive energy density. We have calculated the percent free volume for over fifty polymer melts and find that its value correlates strongly with a number of experimentally-determined quantities, for example the polymer's glass transition. This trend becomes strikingly linear when the percent free volume is calculated at the polymer glass transition temperature. Indeed, we have used the correlation to propose a melt-to-glass 'phase diagram', revealing that both percent free volume and temperature play important roles in understanding this transition. In this talk I will discuss some of aforementioned trends, along with our most current, new results.

Self-consistent field theory (SCFT) is a leading modeling paradigm for predicting the thermodynamic equilibrium behavior of thin film block copolymers (BCPs) under a variety of directed self-assembly (DSA) boundary conditions [1]. BCP DSA is one of the most promising fabrication methods for nanoscale devices with sub-10 nm feature sizes. Accurately predicting the self-assembly behavior of these BCP thin films is important for designing DSA templates. Although there has been a great deal of qualitative agreement between the theory and observed experimental morphological behavior, exact quantitative matching of experimental control variables and simulation parameters has been approximate at best. This issue is in part due to most experimental characterization methods of BCPs being limited to surface characterization methods that only give basic geometric quantitative data with little detail on the nanoscopic local density details that are essential for quantitative comparison of SCFT data. Some successful quantitative comparison of SCFT results has occurred using 3D transmission electron tomography imaging of PS-PDMS thin films with accurate measures of distinct features through the sample [2], but the method still does not give accurate local density information. Better quantitative matching of SCFT results with experiments is necessary for the model to be used as a true DSA template design tool.
Recent progress in characterization techniques using resonant soft X-ray scattering has demonstrated the ability of determining the morphology of BCP thin films through inverse structure determination algorithms. These algorithms used simple trapezoid slabs to model the shape of the block copolymer domains [3]. These shape models both require a large number of parameters and constrain the allowable shapes. BCP SCFT only requires upwards of ten parameters to be optimized for line patterning templates (e.g. chi;, N, f, film thickness, stripe widths and spacings, etc.) allowing more complex and realistic shapes than the trapezoid slab model while decreasing the parameter space. Here we demonstrate that by incorporating a realistic physical model such as SCFT into the methodology, precise quantitative prediction of the morphologies formed is possible. These results can then be used to develop the optimal template conditions necessary for a given BCP DSA application. Experimental scattering data taken of PS-PMMA lamellae thin films patterned by DSA chemoepitaxy templates of different commensurations are used for quantitative comparison between experiment and theory. The results of the best fits to the experimental data are used to enhance the SCFT model and help the design of desired BCP morphologies by giving optimal experimental processing conditions.
References:
[1] Hannon, A.F. 2014, MIT http://dspace.mit.edu/handle/1721.1/89842
[2] Gotrik, K.W. et al. Advanced Functional Materials2014, 24 (48), 7689-7697
[3] Sunday, D.F. et al. ACS Nano2014, 8 (8), 8426-8437

We present theoretical and simulation results informing the development of polymer-based optically switchable materials. This is an extension and generalization of our prior study of composites of paraffin wax and polydimethylsiloxane (PDMS), which have been shown to switch reversibly between a transparent and an opaque state due to a thermal phase transition of the wax. Due to the variety of control mechanisms available (e.g. electrical, thermal, optical), many applications may be viable in various fields. For example, such materials may be used to construct light-trapping geometries with a small input aperture that moves to track the position of the sun. They may therefore be incorporated into PV systems or solar concentrators to enhance photon utilization by the cells. In general, the class of materials being studied have a particle-in-matrix structure that strongly scatters light at low temperature but becomes non-scattering at high temperature, when the ‘particle&’ component melts and swell the matrix. In the present work we take a general approach to this class of materials, providing theoretical results that guide the developments of composites optimized for particular applications, using results from models of light scattering, phase evolution and the kinetics of diffusion in the particle-in-matrix system. We discuss means of optimizing the material specifically in the areas of 1) increasing the scattering power of the opaque state and 2) improving the long term mechanical stability of the composite, and present preliminary experimental results.

Materials which exhibit nanoscale self-assembly have been a significant focus for both fundamental and applied research over the past few decades. Recent advances in microscopy and imaging of surfaces at nanoscale resolution have produced a sizable set of high-quality images of surface self-assembly, in particular. Analysis of these images and, subsequently, determination of relations between self-assembled pattern structure and desired physicochemical properties has not advanced at an equally rapid pace. Researchers predominantly rely on qualitative techniques such as visual inspection in order to interpret imaging data and, ultimately, determining qualitative structure/property relationships. Robust quantitative analysis is required to make further advancements in both determining these relationships and, subsequently, applying optimization and control methods. The primary existing approach for quantitative surface self-assembly analysis involves the use of bond-orientational order theory (BOO), but this method is limited with respect to its generality and robustness in the presence of measurement uncertainty.
In this work, a fundamentally different approach is presented for the analysis of surface self-assembled imaging (Suderman et al, 2015, Phys. Rev. E). It utilizes a set of localized orthogonal functions called "shapelets", which were originally developed for analysis of images of galaxies (~10^20 m). Here they are used to analyze images of nanoscale surface self-assembly (~10^-9 m). The method is applied to simulation data of self-assembled surfaces resulting from heteroepitaxial pattern formation and shown to robustly and efficiently determine local pattern characteristics using an appropriate set of shapelets and steerable filter theory. Local pattern measurements are enabled including pattern type (stripe, square, hexagonal, etc), pattern strength (degree of order), local orientation, and the presence of defects which enables researchers to quantitatively determine structure/property relationships. Furthermore, the method is robust in the presence of measurement noise, pattern degeneracies (defects), and the presence of heterogeneity in the image, such as multiple patterns being present.

Many body implantable devices are made of synthetic polymer which upon insertion absorb water, causing the polymer to swell and forming a gel (mixture of solid and fluid). Since the swelling leads to an expansion of the polymer, a gel is considered as a compressible material. A high concentration of stress due to the swelling leads to the nucleation and growth of cavities within the gel, likely to cause the debonding of the material from a support it is attached to. In this research, we focus on the cavitation in a gel occupying a spherical domain, applying a uniform extensional stress at the boundary of the domain. We consider a total energy of the gel which accounts for the compressible elasticity of the polymer and the mixing between polymer and fluid, called Flory-Huggins energy. In addition to penalizing gel deformation, the energy presents competing effects of entropy that favours mixing, polymer-polymer and fluid-fluid interaction forces. We study material properties necessary to allow for a nucleation, characterization of radially symmetric deformations and stability analysis of the cavity.

Materials innovation for consumer packaged goods involves engineering to the edge, the lightest possible bottle weights, formula / package compatibility, adhesives formulation to meet multiple performance criteria from making to long term shelf life, fiber spinning for non-woven web, battery performance materials, tooth enamel health, skin health. We&’re developing methods to enable material composition - performance relationships to be modeled to streamline development efforts and progress toward effective virtual design. Several common capability gaps illustrate the challenges: solubility and partitioning, migration, solid/liquid phase equilibria, nano- to micro-structural features, polymer rheological and mechanical properties.

Phase-separated morphologies of binary polymer mixtures are used as photo-active material in thin-film organic solar cells. These 100 nm thin films are usually spin-coated from solution, where solvent evaporation drives liquid-liquid phase separation. The fact that the mean composition of the mixture is time dependent gives rise to a coupling between the kinetics of evaporation and demixing. From a generalized diffusion equation accounting for evaporation, we find that evaporation influences the early-stage emergence of the dominant spinodal length scale, as well as the kinetics of late-stage coarsening. Consequently, for both regimes a clear difference is observed when blends are thermally quenched in the unstable region. In the early stages of demixing the typical structural length scale of the emerging morphology decreases with one over the square root of time, whereas in the late stages the rate of coarsening does not obey the classical Lifshitz-Slyozov-Wagner power law. The length scale at the cross-over from early to late stage dynamics itself exhibits a one-sixth power-law behavior with the evaporation rate that is set by the spin speed of the spin-coating process.
This research forms part of the research programme of the Dutch Polymer Institute (DPI), project #734.

While advances in quantum chemistry have rendered the accurate prediction of band alignment relatively straightforward for soft materials, the ability to forecast multimolecule electronic properties of soft, noncrystalline molecular or polymeric systems possesses no simple computational form. Adapting the theory of classical resistor networks, we develop an index for quantifying charge transport in bulk organic semiconducting systems, without the requirement of crystallinity. The basic behavior of this index is illustrated through its application to simple lattices, clusters of common organic photovoltaic molecules, and single-chain aggregates of high-performance conjugated polymers. Using accurately parameterized atomistic simulations we directly simulate molecular and polymeric morphologies, establishing robust correlations between synthetically tunable molecular attributes and multimolecule charge transport characteristics in soft materials. Extensions to exciton transport are presented and the role of single-chain folding in the development of robust polymeric optoelectronic devices is highlighted. These developments provide a quantitative computational means for determining a priori the bulk charge transport properties of soft molecular and polymeric materials.

Bicontinuous phases such as the double or single gyroid structure now have a firm place in self-assembly and biological formation of nanostructured materials. In this talk, we discuss recent progress towards the self-assembly of more complicated network phases based on a triplet of intergrown network-like domains, and provide some indication of their potential for photonic materials.
First, we use self-consistent field theory to show that a tricontinuous structure with monoclinic symmetry, called 3ths(5), based on the intergrowth of three distorted ths nets, is an equilibrium phase of triblock star-copolymer melts when an extended molecular core is introduced. The introduction of the core enhances the role of chain stretching by enforcing larger structural length scales, thus destabilizing the hexagonal columnar phase in favor of morphologies with less packing frustration. This study further demonstrates that the introduction of molecular cores is a general concept for tuning the relative importance of entropic and enthalpic free energy contributions, hence providing a tool to stabilize an extended repertoire of nanostructured phases (see Reference: Fischer et al, Macromolecules 47, 7424-7430, 2014)
Second, we demonstrate that a related phase, based on the intergrowth of three so-called etc nets and recently detected in gemini surfactants (see Sorenson et al, Soft Matter 10, 8229-8235, 2014), is close to thermodynamic stability even in a simple AB diblock copolymer system (see Fischer et al, Soft Matter 11, 1226-1227, 2015). This emphasizes the likely broader role of these tricontinuous geometries in soft materials.
Finally, we briefly consider the photonic properties of a hypothetical multiply-intergrown grown gyroid-geometry, known as 4-srs and 8-srs structures. These structures, which conceivably could form in self-assembly processes related to the above (see Schroeder-Turk et al, Faraday Discussions, 161, 215-247, 2013), can currently be nanofabricated by direct laser writing technologies. Theory and experiments demonstrate that these highly-chiral materials (composed of 4 or 8 equal-handed chiral gyroid networks) have strong circular-polarisation properties which could make them useful as chiro-optical photonic material designs (Saba et al, Phys. Rev. Lett. 106, 103902, 2011; Saba et al, Phys. Rev. B 88, 245116, 2013)

In connective tissues and in many other soft materials the structural function is performed by a fiber network. These networks are composite, meaning that they are made from fibers of different properties or from fiber bundles of various sizes. In this work we study the elasticity of such structures with the broad goal of learning how to design fiber networks for desired effective properties. We show that the composite networks are in average softer than the equivalent ‘homogeneous&’ networks (made from fibers of same type) and the amount of softening scales linearly with the variance of the fiber properties distribution. We also investigate the effect of fiber properties distribution on the non-linear deformation of the network under large deformations. Other types of composite networks, such as those resulting upon the addition of rigid fibers to a homogeneous base network, are also discussed

Today, computational and theoretical materials science play important role in product development. Computer models help researchers to better understand structure-property relationships on atomistic, molecular, meso- and macroscales. One specific example showing the integration of modeling in product development is Directed Self-Assembly (DSA) -- a potentially promising method of writing lithographic patterns on a sub-40 nm length-scale. While the utility of DSA has been demonstrated in principle, there are many challenges that need to be solved before its wide adoption in the semiconductor industry. Computational modeling is crucial in addressing many of those challenges, e.g., optimizing polymer formulations, producing a better pattern, or predicting the defect density. We utilize mesoscale modeling, and more specifically Self-Consistent Field Theory (SCFT) to predict phase behavior of block copolymers in thin films (undirected self-assembly), as well as in cases of chemo- and graphoepitaxy (directed self-assembly). All in all, DSA modeling is becoming an integral part of formulation design and screening process, as will be shown in several examples.

Lamellar microdomains with perpendicular orientation are useful in block copolymer nanolithography. However, it is challenging to produce perpendicularly oriented microdomains in high interaction parameter block copolymers (BCPs) due to the difference in surface energies of the blocks, which promotes in-plane orientation. We show here both by self consistent field theory (SCFT) and experimentally that perpendicular orientation can be obtained using high aspect-ratio grating substrates in which the grating dimensions and substrate chemistry are optimized based on SCFT calculations. One block is preferentially attracted to the vertical sidewalls leading to perpendicular lamellae. The grating substrates were fabricated by laser interference lithography with period 300 nm - 1 µm and a lamella-forming polystyrene-block-polydimethylsiloxane BCP with molecular weight 43 kg/mol was used for demonstration. The SCFT simulation and an analytic model were used to map the energies of lamellae of different orientations as a function of the grating aspect ratio and the surface energies of the sidewalls and top and bottom surfaces. The optimized grating dimensions were tested experimentally by solvent vapor annealing in toluene/heptane vapor mixtures and by thermal annealing. Both processes produced perpendicular lamellae in agreement with the model, with the number of lamellae in the trench determined by the ratio between the trench width and the equilibrium period of the BCP. A slight taper of the trench sidewalls produced a defect (branched lamella) in each trench.

The self-assembly of block copolymers BCPs proves to be a valuable tool for fabricating intricate nano-patterns. The natural ability of BCPs to micro-phase separate results in harmonic morphologies that have geometries controlled by the chain characteristics. The untreated morphology of BCPs typically exhibit local order that fades away as the sample size increases. Hence, generating complex engineering patterns with multiple morphologies, meshed structures, or variable periodicities in a single BCP system requires extra processes of surface patterning and chemical treatment. Here, a bilayer system of BCPs is studied to understand the effect of the underlying topography on the behavior of the top layer&’s self-assembly.
A self-consistent field theoretic simulation SCFT is employed to investigate the role of several key parameters including the commensurability between layers&’ periodicities, the magnitude of topography height, and the chemical treatment of the substrate and the bottom layer of the BCP behavior. We test four periodic templates that have been used in the literature namely: step trenches, cosine shaped trenches, saw tooth trenches, and cosine flat tranches.
The findings hint to the fact that chemical treatment of the underlying layer is crucial to transfer the bottom pattern to the one forming on top. More specifically, stable topography is found to be created, and guided by the lower one in the presence of chemical functionality of the bottom layer. Similarly, prominent topography forces the upper layer to conform, however the effect is shadowed by the role of chemical treatment.
The SCFT findings are further confirmed experimentally on a PS-b-PDMS system self-assembled between HSQ lines created through E-beam lithography. This serves as a guideline for multilayer polymer templating to create multistacked 3D structures.

Our goal is to design polymer-based nano-sized drug delivery systems through the self-assembly of ABA triblock copolymers in aqueous medium. Individual molecular species of Pluronic polymers (PEG-PPO-PEG) and Tyrosine-derived PEG-b-oligo(DTO-SA)-b-PEG block copolymers are represented by two segments of hydrophilic beads and one segment of hydrophobic beads. PEG-b-oligo(DTO-SA)-b-PEG is modeled to possess alternate rigid and flexible components along its hydrophobic chain. We use a molecular dynamics-based mesoscopic simulation technique called Dissipative Particle Dynamics (DPD) to simultaneously resolve the aggregation dynamics, structure and morphology of the initial aggregation of polymer molecules in aqueous medium. We demonstrate the self-assembled aggregates to have core-corona structure with individual polymers bending into v-shape, which agrees with the experimental observations. We also determine the sizes of core and corona for the aggregates composed of PEG-oligo(DTO-SA)-PEG, Pluronic F68 and F127. In addition, we measure the critical aggregation concentration (CAC) and compare our results with corresponding results obtained using experimental approach. CAC measurements for Pluronic polymers F68, F88, F108, and F127 obtained using simulations are found to be in a good agreement with experimental measurements. For PEG-b-oligo(DTO-SA)-b-PEG block polymers, the CAC values determined and predicted from simulations are found to be one order of magnitude higher than those measured experimentally. Our results can be used to determine structural and dynamical properties of polymer-based drug delivery systems and build a virtual library of numerous ABA block polymers. These libraries can be utilized to optimize the design of drug delivery systems by simulating the interactions between ABA block copolymers and various drug molecules, and predict their drug encapsulation and release features.

Polyelectrolyte block copolymers, which combine the properties of polyelectrolytes (i.e., sensitive to changes in solvent ionic strength and pH) with those of surfactants, can self-assemble in an aqueous environment into a variety of responsive morphologies, including spherical micelles, cylindrical micelles, vesicles, lamellar mesophases, micellar networks and micellar aggregates. Such assemblies are promising candidates as carriers for drug and gene delivery, where the morphology and the size of the assemblies determine their transport properties and delivery capabilities. The morphology and size of formed aggregates are determined by the characteristically complex equilibrium of noncovalent forces (electrostatic, steric, hydrogen bonding, Van der Waals, and hydrophobic interactions). The strength of repulsive Coulomb interactions between the polyelectrolyte segments can be efficiently tuned by variations in ionic strength or/and pH in the aqueous solution. In order to explore the self-assembly process of polyelectrolyte block copolymers, we developed implicit solvent ionic strength (ISIS) model for use with the Dissipative Particle Dynamics (DPD) method to simulate the behavior of polyelectrolyte block copolymers with incorporated electrostatic interactions to achieve a good balance between reasonable physical description and computational feasibility. We applied this coarse-grained model to explore the influence of block length, block architecture, and solvent quality on the properties of the assemblies formed in aqueous solutions. Our DPD model enables us to obtain the main characteristics of the micelles formed from the self-assembly of polyelectrolyte diblock/triblock copolymers as a function of the block length and salt concentration. Based on a comprehensive set of data obtained we constructed a morphological diagram of polyelectrolyte block copolymers in aqueous solution. The coarse-grained modeling and simulation, which is demonstrated as a complimentary approach in addition to experimental and theoretical methods, can deliver insight into self-assembly processes of diblock/triblock copolymers and provide evaluation of the size of aggregates obtained along with their scaling relation representation. The simulation results suggest that this coarse-grained simulation scheme gives a route wherein one can effectively and efficiently capture the self-assembly behaviors of polyelectrolyte block copolymers, such as DNA, RNA and other natural and synthetic polycations and polyanions. This study can provide key knowledge for the rational design and preparation of polyelectrolyte block copolymers materials with desired morphological, mechanical, and rheological properties.

PEG-based hydrogels conjugated with biomolecules (e.g., bioactive peptides, enzymes, nucleotides) are being widely investigated for tissue engineering, regenerative medicine, and biosensor applications. The bioactivity of bioconjugated hydrogels depends on the structure and activity of the bioactive component of the system. Experimental methods alone are quite limited in their ability to provide molecular-level insights into how a given design could be optimized to enhance its performance. We are therefore developing molecular modeling methods to visualize, predict, and understand the molecular structure and behavior of these types of complex molecular systems using a three-level multiscale approach. (1) A coarse-grained (CG) model of the bioconjugated hydrogel is first generated at experimentally determined cross-linked density on a 3-D lattice using the bond-fluctuation Monte-Carlo method. (2) The lattice is then removed, CG parameters are generated based on all-atom models of hydrogel segments using the polymer consistent force field (PCFF), and the hydrogel system equilibrated off-lattice using the TIGER2 advanced sampling algorithm. (3) The equilibrated CG model is then reverse-mapped back into an all-atom model, which is then hydrated with explicit TIP3P water and re-equilibrated to represent the final all-atom bioconjugated hydrogel structure. Structure factor plots of S(q) vs. q are then calculated for comparison with experimentally determined structure factor plots determined by X-ray diffraction and/or neutron scattering for model validation. Once validated, the resulting predicted structures are available for bioactivity assessment and evaluation and to serve as a guide for design optimization for enhanced bioactivity.

Hydrogel materials have been receiving a great deal of attention over the last decades due to their applications in tissue scaffolding, cell culture platforms, in-vivo bio-sensing, and drug delivery. When exposed to external stimuli such as temperature, pH levels, light intensity, and electric/magnetic fields hydrogels undergo a reversible and sometimes discontinuous volume transition. The micromechanics and kinetics of gels near this critical transition point is not fully understood which hinders developments in some of the aforementioned areas. In our study, we utilize dissipative particle dynamics (DPD) to develop a mesoscale model for responsive polymer networks. We use this model to examine how the bulk modulus of a hydrogel changes as a function of the Flory-Huggins parameter. This allows us to probe the micromechanics of these networks near the critical volume transition point. Our simulations show that the bulk modulus is minimized for theta solvent conditions. Additionally, for good solvent we demonstrate that the bulk modulus scales with the polymer volume fraction as: K ~ (1/phi;_p)^-9/4. At large strains and large values of Flory-Huggins parameter the network exhibits phase separation leading to abnormal variation of the modulus.
Support from NSF CAREER Award (DMR-1255288) is gratefully acknowledged

A Bicontinuous Interfacially Jammed Emulsion-geL, or bijel, is a canonical example of the use of computational techniques to design a new soft material [1]. In the lattice Boltzmann simulations, the material is created by trapping the spinodal pattern of two fluid domains using a layer of interfacial colloidal particles. Jamming of the particles arrests the phase separation of the fluids and gives the composite sample a yield stress.
By translating simulation variables into the real world, it can be shown that the simulations can only explore the fate of such a material for about a microsecond. Evidently part of the design process is finding out what happens after that! Experiments first showed that this new soft material could really be made and that it exhibits long-term stability [2]. The key step (which may ultimately prove to be unnecessary [3]) is tuning the wettability of the particle surfaces.
Curiously, subsequent experiments showed that the bijel does not always fall apart when the interfacial tension is removed. This inspired a return to simulations: molecular dynamics were used to demonstrate that a plausible combination of short-range attractive and long-range repulsive inter-particle interactions would hold the structure together [4]. The resulting colloidal scaffold was termed a monogel.
Using combinations of liquids and particles which form a conventional bijel but cannot form a monogel we have now been investigating the response of a bijel to centrifugal compression. The spinodal pattern is transformed into a layered structure perpendicular to the compression direction [5]. Our results leave us still grappling with the question: is the bijel actually stable?
References:
[1] K. Stratford, R. Adhikari, I. Pagonabarraga, J.-C. Desplat and M.E. Cates, Science 309, 2198 (2005).
[2] E.M. Herzig, K.A. White, A.B. Schofield, W.C.K. Poon and P.S. Clegg, Nat. Mater. 6, 966 (2007).
[3] M. Cui, T. Emrick, T.P. Russell, Science 342, 460 (2013).
[4] E. Sanz, K.A. White, P.S. Clegg and M.E. Cates, Phys. Rev. Lett. 103, 255502 (2009).
[5] K.A. Rumble, J.H.J. Thijssen and P.S. Clegg, in preparation.

Here we present the results of mesoscale simulations of bijels and trijels in confined thin film geometries. We use a hybrid Cahn-Hilliard Brownian dynamics method which couples the diffusion driven phase separation of two or three immiscible liquids with the Brownian motion of nanoparticles disperesed in those liquids. The bijel simulations show how different metastable film morphologies can be achieved and tuned by varying the nanoparticle size and volume fraction, film thickness, liquid blend ratio, and by the use of applied external electric fields. Trijels are a novel extension of bijels and our simulations illustrate how even more functionality can be achieved in their thin film systems by the variation of similar system parameters. For example, with three fluids, two different fluid interfaces are present that can be controlled by the wetting properties of the fluids and one or more nanoparticle types. This results in a wider variety of metastable morphologies, compared to those of bijels, with new degrees of freedom available for tuning the stabilized morphology. We expect these simulations to encourage the design of new bijel and trijel materials with unmatched functionality for energy, environmental, and other industry applications.

Self-assembly of amphiphilic molecules provides a well-known way to make nanoscale (and larger) supramolecular structures including micelles, ribbons, sheets and aggregates. Recently there has been growing interest in the coupling of this self-assembly chemistry with silver and gold nanoparticles, and with dye chromophores, leading to a new generation of materials of interest for optical devices and biodetection. This talk describes the self-assembly modeling and optical properties of two classes of these materials: DNA-linked nanoparticle superlattices and peptide amphiphile fibers and ribbons with embedded dyes or nanoparticles. The presentation will present a novel coarse-graining strategy for describing the assembly of DNA-linked superlattices, including studies of linkers in which many DNA sticky ends connect to one DNA attachment to the nanoparticles. We will use these models to study the plasmonic optical properties of the resulting structures, leading to new classes of plasmonic metamaterials, plasmonic polymers and photonic nanowires.

Gold nanoparticles (AuNPs) that are functionalized with nucleic acids (such as DNA or RNA aptamers) have substantial potential for increased exploitation in the nanotechnology arena, for example in the area of aptamer-based sensing.¹ However, for this potential to be realized, detailed molecular-level structural data regarding the interface between aptamers and citrate-capped AuNPs are much needed. However, availability of these data in the literature is limited at present. Here, we use molecular dynamics simulations to investigate the structural preferences of a DNA hairpin interacting with a citrate covered^2,3 aqueous Au(111) planar surface⁴ at both 300 and 400 K. The level of disorder and the adsorption of the DNA hairpin was compared to data obtained for a DNA hairpin adsorbed at a planar aqueous Au(111) interface in the absence of citrate.⁵ We also investigated the influence of interfacial shape via simulations of the DNA hairpin interacting with a citrate-caped AuNP⁵ in aqueous solution. Our findings provide essential molecular-level insights that can help advance the rational design of functional nanomaterials that exploit the abiotic/biotic interface.
References
[1] H. Li and L. Rothberg, Proc. Natl. Acad. Sci. U. S. A., 2004, 101, 14036 -14039.
[2] L. B. Wright, P. M. Rodger and T. R. Walsh, Langmuir, 2014, 30, 15171- 15180.
[3] K. L. M. Drew, J. P. Palafox-Hernandez, Z. E. Hughes and T. R. Walsh, The Effects on a DNA Aptamer in the Presence of Citrate, at Varying Temperatures and Aqueous Gold Interfaces. (in preparation)
[4] L. B. Wright, P. M. Rodger, S. Corni and T. R. Walsh, J. Chem. Theory Comput., 2013, 9, 1616-1630.
[5] K. L. M. Drew, J. P. Palafox-Hernandez and T. R. Walsh, Modulation of DNA Adsorption and Temperature-Driven Unfolding at Aqueous Gold Surfaces (in preparation)

Soft robotic systems could be potentially useful in a variety of applications in microfluidics, drug delivery, and nanotechnology. In our study we utilize dissipative particle dynamics (DPD) to design a soft self-propelling micro-swimmer made of a bi-layered hydrogel sheet. The two polymeric layers from which the swimmer is comprised have identical material properties but exhibit different sensitivities to external stimuli: one layer responds by swelling, whereas the other layer remains passive. When an outside stimulus is introduced the responsive layer swells which causes the body of our X-shaped micro-swimmer to bend. Upon removal of the external stimulus the responsive layer contracts and the micro-swimmer straightens, recovering its initial flat shape. Our simulations show that periodical exposure to the stimulus causes this swimmer to exhibit a time-irreversible motion leading to rapid swimmer propulsion through a viscous solvent. We examine the effects of swimmer geometry and material properties on the swimming speed.
Financial support by NSF CAREER Award DMR-1255288 is gratefully acknowledged.

Solids respond to stress with localized particle rearrangements, which generally occur at structural defects such as dislocations. In crystals, these defects can be identified easily, as they break periodic structural order. In amorphous solids, finding these defects has been significantly more difficult, since structural order is often hard to quantify. We show that machine learning can be used to develop a scalar field that controls the dynamics in disordered solids, ranging from the thermal 3D Lennard-Jones glass to 2D granular pillars that are compressed experimentally. Our model can be trained on simulations or experiments to predict locations of rearrangements with unprecedented accuracy. Using this scalar field, many interesting properties of supercooled liquids can be explained from a structural perspective, including the onset temperature of glassy behavior, dynamic heterogeneities, and rapidly increasing relaxation times at lower temperatures. Within this framework, fracture, shear banding, and failure can be modeled from a structural perspective. Our work is an example of how machine learning can be used to construct physical theories that would not have been possible without such a data-scientific approach.

Creation of self-assembled 2-D membranes at air-water interface that mimic biological membranes can be useful for a range of biomedical and energy applications. We recently reported that a sub-nanometer asymmetry in ligand distribution leads to formation of Janus-like nanoparticle membranes. We present mesoscale simulations to probe the ligand dynamics during this self-assembly process. We hypothesize that the ligand asymmetry can originate from either a ligand conformational change or due to ligand re-organization. The former assumes ligands to be immobile whereas the latter can arise from ligands being mobile. To-date, the correlation between ligand mobility and ligand conformation in a self-assembled state is not clear. We have conducted meso-scale simulations of self-assembly of ligand-modified gold nanoparticles at the air-water interface to elucidate the exact role of ligands that contributes to their asymmetric distribution during the membrane formation process. Our simulations suggest that the experimentally observed anisotropic distribution of ligands originates from ligand re-distribution that is greatly facilitated by the mobility of ligands at gold surface. The mobility of the ligands in turn is a strong function of their surface coverage. Fully ligated nanoparticles have much lower ligand mobility and do not display an asymmetric distribution in the self-assembled membranes. On the other hand, for lower ligand coverages, we find the ligands to be highly mobile which reorganize to form Janus-like membranes. This asymmetry is shown to have interesting consequences on the mechanical and bending properties of these 2-D membranes.
Reference:
1. Zhang Jiang, Jinbo He, Sanket A. Deshmukh, Pongsakorn Kanjanaboos, Ganesh Kamath, Yifan Wang, Subramanian K. R. S. Sankaranarayanan, Jin Wang, Heinrich M. Jaeger and Xiao-Min Lin. “Subnanometre ligand-shell asymmetry leads to Janus-like nanoparticle membranes.” Nature Materials, doi:10.1038/nmat4321, 2015.

We perform Brownian Dynamics simulations to study the gelation of suspensions of attractive, rod-like particles. We show that details of the particle-particle interactions can dramatically affect the dynamics of gelation and the structure and mechanics of the networks that form. If the attraction between the rods is perfectly smooth along their length, they will collapse into compact bundles. If the attraction is sufficiently corrugated or patchy, over time, a rigid space spanning network forms. We study the structure and mechanical properties of the networks that form as a function of the fraction of the surface that is allowed to bind. Surprisingly, the structural and mechanical properties are non-monotonic in the surface coverage. At low coverage, there are not a sufficient number of cross-linking sites to form networks. At high coverage, rods bundle and form disconnected clusters. At intermediate coverage, robust networks form. The elastic modulus and yield stress are both non-monotonic in the surface coverage. The stiffest and strongest networks show an essentially homogeneous deformation under strain with rods re-orienting along the extensional axis. Weaker, clumpy networks at high surface coverage exhibit relatively little re-orienting with strong non-affine deformation. These results suggest design strategies for tailoring surface interactions between rods to yield rigid networks with optimal properties.

Clusters of colloidal particles exhibit a broad range of emergent, size dependent, properties. Tuningsuch properties requires a strong fundamental understanding of the thermodynamics and kinetics governing the self-assembly of these clusters into crystalline arrangements of varying morphology. A first step in developing this understanding is to accurately describe the equilibrium structure and morphology of these colloidal assemblies. In this presentation, we report the results of a new computational approach, which we term the “generalized Wulff construction,” that can accurately describe the equilibrium, i.e., of minimum free energy, shape of a crystalline assembly of colloidal particles. The colloidal system that we consider is modeled according to an inter-particle interaction potential consisting of two terms, an electrostatic repulsion and an Asakura-Oosawa (AO) depletion attraction. This inter-particle potential has been validated experimentally and used for accurate analyses of the phase behavior of these colloidal clusters and corresponding phase transitions, including disorder-to-order transitions as well as polymorphic transitions of crystalline clusters.
Our implementation of the generalized Wulff construction is based on lattice site exchange-Monte Carlo (LSE-MC) simulations performed within a parallel tempering framework. These LSE-MC simulations are carried out for the face-centered cubic as well as the hexagonally close-packed crystal structures. This approach differs from the conventional Wulff construction, which considers surface free energy effects from surface facets alone, in that itinherently includes contributions from all other surface features (such as edges and vertices). The result of ourgeneralized approach is a minimum-free-energy configuration for given crystal size (volume or number of atoms). We carry out these calculations over a range of crystal sizes to systematically analyzethe size effects on the morphology and structure of colloidal clusters. Based on the findings of this analysis, we can determine “magic” cluster sizes, for which improved colloidal cluster stability is exhibited, i.e., clusters which have lower internal energy compared to other clusters of similar size. Such analyses also provide useful information for the development of coarse-grained theories of colloidal crystal nucleation and growth.

Copolyesters are a subset of polymers that have the desirable properties of rigidity and clarity, while retaining chemical resistance. These properties make the polymers good candidates for use in lamination films for soda-lime glass that are impact resistant. The modified glass is often used in areas where the glass is under constant exposure to high temperature and humidity. Hydration of the polymer and surface can lead to delamination, which impairs the favorable impact properties. In this study, we used molecular dynamics simulations to examine the influence of the hydration levels on adhesion to a soda-lime glass surface. The interaction energy between the polymer and the glass was chosen because it directly relates to the impact performance of the system, as well as the longevity of the polymer-glass bond. We investigated which chemical groups were key components to the interaction energy, and how the addition of water molecules impacted these interactions. The systems were simulated at a temperature consistent with warm summer conditions under a range of polymer hydration levels. The resulting interaction energy values demonstrate how the polymer-glass interaction changes with the addition of an increasing number of water molecules. Other factors that could impact the interaction energy will also be discussed.

Small molecule organic solar cells (OSCs) are promising sources for solar energy harvesting because of their low production costs, mechanical flexibilities, and light weight comparing with their pure inorganic counterparts. The key toward OSC device performance is the nanomorphology of the bulk hererojunction (BHJ) layer - the photoactive layer comprising an interpenetrating, bicontinuous network of electron donor/acceptor materials. In small molecule OSCs, the electron donor materials are usually semiconducting small molecules, whereas electron acceptor materials are usually fullerenes or their derivatives such as PCBM. The formation of BHJ layer relies on phase separation between electron donor and acceptor phases, which critically depends on the device fabrication conditions. Hence, comprehensive insights into the correlations between device fabrication conditions and resultant BHJ nanomorphologies are important for optimizing device performances. Nevertheless, experimental characterization of the nanomorphologies of the BHJ layer is never trivial. In this work, we will present our works in developing multiscale coarse-grained model to simulate the morphology evolution of the BHJ layer during solution processing and vacuum co-deposition processes, with system size compatible with those in experiments. Both ellipsoidal and spherical coarse-grained beads are employed to mimic small molecules with different molecular geometries. Our simulation results can provide multi-resolution morphological details - ranging from mesoscale morphological properties to molecular scale packings, which are not yet available from experiments. Firthermore, by employing charge carrier transport kinetic Monte Carlo simulations based on the BHJ nanomorphologies from our coarse-grained simulations, the correlations between BHJ nanomorphologies and carrier transport properties can be in silico revealed. Hence, the multiscale molecular simulation platform we constructed can potentially be helpful to reveal the correlations between device fabrication protocols and resultant deveice performance, thereby helping experimental teams optimize OSC fabrication protocols to further promote device performance of next-generation OSC devices.

Self-assembling nanotechnologies are currently being studied around the world as a result of their potential applications as smart materials which build themselves from the bottom up. Some of these such materials can even change their resulting structure in response to external stimuli, such as polyelectrolytes and their sensitivity to ionic strength and pH. However, it was previously difficult to simulate relatively large polyelectrolyte systems under varying ionic strength and pH, due to the explicit consideration of the long-range electrostatic interactions which occur. Using the recently developed implicit solvent ionic strength (ISIS) DPD model, we have been able to simulate mesoscopic polyelectrolyte systems under various salt concentrations in a short period of time. For this study, we investigated the effect of polyelectrolyte block charge ratio and salt concentration on long polyelectrolytes accompanied at both ends by short hydrophobic tails. These copolymers formed micellar gels at concentrations lower than their neutral counterparts, and can have morphological transitions induced by increasing ionic strength and decreasing polyelectrolyte charge ratio, altering the polyelectrolyte chain dynamics. We observed the resulting morphologies transition from bridged flowerlike micelles, to multicompartmental and worm-like structures, and eventually to “big-burger”, lamellar micelles by manipulating the experimental parameters, with a greater degree of control in decreasing charge ratio for longer polyelectrolyte block lengths. We calculated different morphological properties, such as the aggregation number of the micellar cores, the distance between bridged cores, and the bridge fraction, as a function of charge ratio and implicit ionic strength for several block lengths to demonstrate how the self-assembly could be tailored for possible applications, including drug delivery and pollution remediation.

Nature employs self-assembly processes to formulate structures on the size scale of angstroms to kilometers. Although there is a great consistency in the natural self-assembly processes, the underlying principles and mechanism guiding a priori design of complex structures even those that are defect free are not very well understood. Lack of understanding of these principles is a big hurdle for creation of multifunctional 3-D structures for development of advanced materials with novel optical and mechanical properties, tissue engineering scaffolds, and parts of micro- and nano-electronic devices. In the present study, we have conducted meso-scale all-atom and coarse-grained simulations to model manipulation of 2-D materials to 3-D hollow structures. The simulation trajectories are analyzed to study the mechanical and structural properties of 3-D materials and to calculate the persistent length. Our simulations suggest that both long- and short-range non-bonded interactions play a crucial role in manipulation of 2-D materials to 3-D structures. Results obtained from this study will allow experimental scientist to create quick, reversible, diverse size, and reproducible 3-D hollow objects with controlled chemical properties and morphology of both the exterior and interior.

A combined theoretical and experimental study on the morphological, structural, and optical properties of molibdate silver (β-Ag₂MoO₄) microcrystals is presented. β-Ag₂MoO₄ samples were prepared by a co-precipitation method and characterized by X-ray diffraction (XRD) spectroscopy with Rietveld refinement, micro-Raman (MR) spectroscopy, and in situ field emission scanning electron microscopy (FE-SEM). Optical properties were investigated by UV-Vis and photoluminescence (PL) spectroscopy. Also, the nucleation and formation of Ag nanoparticles on β-Ag₂MoO₄ during electron beam irradiation were analyzed as a function of electron beam dose. These events were directly monitored in real-time using in situ FE-SEM. The thermodynamic equilibrium shape of the β-Ag₂MoO₄ crystals was built with low-index surfaces (001), (011), and (111) through a Wulff construction. This shape suggests that the (011) face is the dominating surface in the ideal morphology. A significant increase in the values of the surface energy for the (011) face versus those of the other surfaces was observed, which allowed us to find agreement between the experimental and theoretical morphologies. Our investigation of the different morphologies and structures of the β-Ag₂MoO₄ crystals provided insight into how the crystal morphology can be controlled so that the surface chemistry of β-Ag₂MoO₄ can be tuned for specific applications. The presence of structural disorder in the tetrahedral [MoO₄] and octahedral [AgO₆] clusters, the building blocks of β-Ag₂MoO₄, was used to explain the experimentally measured optical properties.

Flexible polyamide sensors are selectively patterned with barrier coatings to control fluid confinement and reduce detection volume. Lateral flow of barrier coatings into active sensing area and the problem of adhesion between coating and substrate interferes with sensing performance. Unfortunately, these cause severe heat loss and power dissipation in flexible sensors, and influence of barrier coatings on electrical performance is not well understood. Optimization of off-shelf barrier coating materials has not been very successful. In this work, we address some of these key issues through numerical simulation and finite element analysis of sensor manufacturing and baseline performance. A three-dimensional steady state model is employed to evaluate electric field distribution, resistive heat loses and temperature as coating thickness and distance are varied. Initially the dynamics of fluid distribution on barrier coated flexible substrate is evaluated using time-dependent analysis. Results with fluid interaction are used as initial boundary conditions for the steady-state model. Our results indicate the influence of structural inhomogeneities on uniformity of barrier coatings and increase in effective heat loss with coating distance. Simulation results are in good agreement with experimental results.

We provides a microscopic understanding of the role of halides in controlling the anisotropic growth
of gold nanorods through a combined computational and experimental study.
Atomistic molecular dynamics simulations unveil that Br- adsorption is not only responsible for surface passivation, but also
acts as the driving force for the CTAB micelle adsorption and stabilization on the gold surface in a facet-dependent way.
Partial replacement of Br- by Cl- decreases the difference between facets and the surfactant density. Finally
in CTAC solution no halides or micellar structure protect the gold surface and further gold reduction should be uniformly possible.
Particles growth in different CTAB/CTAC mixtures confirms a more uniform and faster growth as the amount of Cl- increases.
The surfactant bilayer thickness measured on nanorods exposed to CTAB and CTAC quantitatively agrees with the simulation results.

Macromolecular organic matrices with ordered arrays of charged functional groups play an important role in determining the characteristic shapes, structures and properties of biominerals by providing specific intermolecular interactions for controlling nucleation and crystal growth¹. For example, the remarkable properties of bone and teeth are derived from a highly organized arrangement of aligned nanometer-scale crystals of hydroxyapatite (idealized stoichiometry of Ca₁₀(PO₄)₆(OH)₂) within the macromolecular matrix. Therefore, understanding the crystallization pathways of calcium phosphate is important in unraveling the processes of both bone and tooth mineralization. Previous cryoTEM study² has shown that the hydroxyapatite formation at a Langmuir monolayer of arachidic acid proceeds through the structural transformation of an intermediate amorphous calcium phosphate (ACP) phase, the structural building units of which are the stable calcium phosphate solute species, named pre-nucleation clusters (PNCs). Here, we use molecular dynamics simulations to provide direct computational view on the formation of PNCs followed by their assembly into ACP with arachidic acid monolayer as the matrix model. Simulations show that the binding of calcium to the charged groups of the monolayer is a key step in the assembly of PNCs. We also investigate the thermodynamics properties of the monolayer-templated cluster, such as the hydration, stability and its fluid characteristics and compare to existing experimental studies³. These clusters feature rapid dynamics with the configuration changing on timescales for molecular rearrangements in solution, and thus no significant phase boundary between the clusters and the surrounding solution is observed throughout the simulation, confirming the existence of dissolved PNCs. Our results will benefit the design of inorganic-organic composite biomaterials by using self-assembled monolayers as attractive templates for controlling mineral nucleation and crystal growth.
References
1. Editor: Estroff, L. A., Special issue on Biomineralization. Chem Rev 2008,108 (11), 4329-4978.
2. Dey, A.; Bomans, P. H. H.; Müller, F. A.; Will, J.; Frederik, P. M.; With, G. d.; Sommerdijk, N. A. J. M., The role of prenucleation clusters in surface-induced calcium phosphate crystallization. Nat Mater 2010,9 (12), 1010-1014.
3. Gebauer, D.; Kellermeier, M.; Gale, J. D.; Bergströmc, L.; Cölfen, H., Pre-nucleation clusters as solute precursors in crystallisation. Chem Soc Rev 2014,43, 2348-2371.

Polyelectrolyte block copolymers may self-assemble in a variety of nanoaggregates in aqueous environment, such as micelles, vesicles, lamellar mesophases, or micellar aggregates. The strength of repulsive Coulomb interactions between the polyelectrolyte segments can be efficiently tuned by variations in ionic strength or/and pH in the aqueous solution. In order to explore the self-assembly process of polyelectrolyte block copolymers, we developed implicit solvent ionic strength (ISIS) model for use with the Dissipative Particle Dynamics (DPD) method to simulate the behavior of polyelectrolyte block copolymers with incorporated electrostatic interactions to achieve a good balance between reasonable physical description and computational feasibility. We applied this coarse-grained model to explore the influence of block length, block architecture, and solvent quality on the properties of the assemblies formed in aqueous solutions. Our DPD model enables us to obtain the main characteristics of the micelles formed from the self-assembly of polyelectrolyte diblock/triblock copolymers as a function of the block length and salt concentration. Based on a comprehensive set of data obtained we constructed a morphological diagram of polyelectrolyte block copolymers in aqueous solution. The coarse-grained modeling and simulation, which is demonstrated as a complimentary approach in addition to experimental and theoretical methods, can deliver insight into self-assembly processes of diblock/triblock copolymers and provide evaluation of the size of aggregates obtained along with their scaling relation representation. The simulation results suggest that this coarse-grained simulation scheme gives a route wherein one can effectively and efficiently capture the self-assembly behaviors of polyelectrolyte block copolymers, such as DNA, RNA and other natural and synthetic polycations and polyanions.

Polyvinyl chloride (PVC) is one of the most commonly used thermoplastics worldwide and is utilized for building construction, automobile interiors, medical supplies, packaging, and electronics. For many of these applications, PVC is plasticized to afford flexibility, toughness, and easier processability. The global annual utilization of plasticizers is ~14 billion pounds, 95% of which are used with PVC. The most prevalent plasticizers are ortho-phthalates such as DOP (dioctyl phthalate) and DINP (diisononyl phthalate) that account for 81% of the global plasticizer market; however, non-ortho-phthalate plasticizers such as DOTP (dioctyl terephthalate) are being sought due to environmental and health concerns regarding ortho-phthalate plasticizers. Here, we report on a molecular dynamics (MD) simulation study to investigate the suitability of a general all-atom force field for PVC, DOP, and DOTP and to subsequently gain insights into atomistic-level differences and similarities between DOP and DOTP-plasticized PVC. A total of nine systems were modeled, namely, three pure systems (PVC, DOP, or DOTP) and six binary systems (PVC + DOP or PVC + DOTP, each at 28, 35, or 42 wt% plasticizer), using the General Amber Force Field (GAFF). MD simulations of these systems were used to predict density at 300 K (ρ), plasticizer diffusivity at 300 K, activation energy for diffusion (E_a), glass transition temperature (T_g), and thermal expansion coefficient at 20^oC (α_v). For pure DOP/DOTP, predictions of ρ and E_a were within 2.5% and 6% of experimental values, respectively, while diffusion coefficients were underestimated by a factor of ~18x. Underestimated diffusivity is consistent with other organic solvents such as benzene but was more pronounced for our modeled DOP and DOTP. For pure PVC, ρ, T_g, and α_v were predicted within 3%, 5%, and 10% of experimental values, respectively; this prediction accuracy is comparable to that reported for various polymers with force fields such as COMPASS and CVFF. Predictions for binary systems were mostly either overestimated (T_g: 1.4x) or underestimated (α_v: 2x); however, strong correlations were observed with plasticizer wt% (R² = 0.89 for T_g; R² > 0.97 for α_v). Furthermore, E_a was well predicted for the binary systems (within 3.3%). With improvement, these simulations could be used to gain insights into structure-function relationships of plasticizers in plasticized PVC, which could in-turn aid the development of novel plasticizers. This research was sponsored by Eastman Chemical Company.

The solvation behavior of C₆₀ fullerene and its substituted derivatives has received much attention due to their primary electronic application being solution-processable devices; the performance of such devices is heavily linked to the aggregation behavior of the fullerene species in solvent-fullerene-polymer mixtures. We are employing all-atom molecular dynamics simulation techniques with a rigorously vetted force field to gain an unparalleled level of environmental control and insight into the dynamic structure of the solvation shell formed around C₆₀ and its derivatives in nine aromatic solvents. A novel method was developed to quantify the distribution of solvent molecule orientation in the solvation shell which revealed a strong positive correlation between the regularity of solvent molecule orientation in the solvation shell and experimentally obtained solubility limits. This correlation holds true for solvents with C₆₀ solubility higher than predicted by previous theoretical models. Moreover, this correlation extends to the solvation of phenyl-C₆₁-butyric acid methyl ester (PC₆₁BM), the most ubiquitous C₆₀ derivative in photovoltaic devices. This method of solvation shell structure quantification is applied to additional fullerene derivatives developed for photovoltaic applications, though experimental solubility studies are not available for comparison. The application of solvent shell order to the prediction of fullerene solubility can provide valuable predictions for solvent selection in organic photovoltaic device fabrication and be extended to predict aggregation in deposited polymer-fullerene materials.

Polyethylene glycol (PEG) is a polymer with a vast range of applications, including medical and biochemical applications. Notwithstanding the widespread use of PEG to improve the therapeuthic efficacy of drugs, proteins, liposomes or nanoparticles through the PEGylation process, the molecular factors at the basis of this behaviour have not been clearly identified, yet. Here we use molecular dynamics simulations to investigate the non-covalent interactions taking place between PEG and several blood proteins. The simulations are used to measure the preferential binding coefficient of PEG for proteins, and reveal recurring patterns of interaction involving specific aminoacids. The latter could be used for the development of coarse grained representations of protein-PEG interactions and may provide the basis for understanding the properties of protein coronas formed around PEGylated nanoparticles.

Recent interests in photo-electrochemical catalysis of water splitting have boosted studies on p-type dye-sensitized photo-cathodes for the hydrogen evolution reaction [1]. In these devices, the sunlight harvesting is carried out by p-type dye-sensitized solar cell (p-DSSC), which is the complementary photocathode to the well-studied n-type DSSCs (Grätzel cells) [2]. However, their low performances have hindered the development of convenient tandem solar cells based on cost-effective n- and p-type DSSCs [3]. Experimental investigations have demonstrated that electronic processes at the dye-electrode interface are responsible of the low p-DSSC efficiencies [4]. The most exploited p-type semiconductor used as p-DSSC solid electrode is nickel oxide (NiO), where the electronic features at the dye-electrode interface are strongly dependent on the dye anchoring group and binding modes on NiO surfaces [5].
In this contribution we discuss state-of-the-art first-principles calculations (DFT+U) on the interfaces between NiO surface and two different sensitizers: a coumarin-based dye (C343) and a recently proposed push-pull dye [6]. We computed the dye-NiO binding energies for different anchoring groups as well as the effects of changing the dye-anchoring moiety on the interfacial electronic features. From our results, we derive structure-property-function relationships that can help to develop further new p-type DSSC photocathodes with improved performances.
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We present dissipative particle dynamics simulations (DPD) of entangled polymer chains with containing solid filler particles. Due to represent entangled polymers in DPD models, the slip-springs approach which has been proposed by Langeloth et.al., J. Chem. Phys. 138, 104907 (2013), is applied to our simulation. Dynamical properties, such as mean-square displacements and diffusion constants of polymers and filler particles, are analyzed and viscoelastic properties of the composites are also obtained. Based on the results, filler-induced reinforcement in the model is discussed. In addition, this coarse-grained approach using DPD models is examined on advantages over hard-core potential models in terms of computational efficiency and applicability to study various kinds of polymer containing soft materials, such as rubber, ink and slurry.

The detection of protein biomarker with protein imprint based biorecognition is a novel approach to diagnose many diseases. However, the complication of the protein structure is a paramount challenge to design the imprint polymer particularly in the selection of functional monomers. Here, we use human papillomavirus derived E7 protein as template protein to study the binding between E7 with polymerized products. The study is conducted with atomistic molecular dynamics simulation. We capture a self-assembly phenomena between polymerized compounds and template protein in the polymerization process. Polymerized compounds compete for binding with the E7 protein. We also study the effects of experimental conditions on the bindings. The results outline approaches to screen polymer compounds for the design of imprint of a given protein biomarker.

Resolving the multiple length scales present in semi-crystalline polymers is a challenge when designing coarse-grained potentials for mechanical property simulations. While polymer chains in amorphous domains can be radically simplified into single monomer particles, crystalline domains require precise interatomic interactions to maintain their crystalline structure. Accurately modeling the interfacial morphology between amorphous and crystalline regions is also of interest. The computational efficiency gained with coarse-grained potentials allows more physically relevant polymer compositions and processing conditions to be modeled, such as higher molecular weights and their corresponding higher degrees of entanglement, more topologically diverse interfacial patterns, and the simulation of tensile deformation at slower drawing speeds and higher draw ratios. Although polymer crystallization from the melt typically generates micron-scale spherulites that are beyond the scope of molecular simulations, materials processed at lower temperatures may contain feature sizes that approach a scale accessible by molecular simulation. In this talk we develop coarse-grain potentials from all-atom models of polypropylene, which represent prototypical polymers with side groups. We use a Monte Carlo algorithm to build equilibrated semi-crystalline interfaces with varying loop/tail density, entanglement, percent crystallinity, and molecular weight. To assess the quality of our potentials, we compare the following calculated phenomena with those simulated with all-atom potentials: chain dynamics and chain morphology at the interface, crystallinity under strain, and the ability to reproduce the tensile properties of polypropylene. We also simulate the tensile properties of higher molecular weight polypropylene to address the scalability of our model.

Polymer based nanoparticle formulations attract attention as potentially highly tunable drug delivery systems. However we currently have limited understanding of the underlying polymer - nanoparticle assembly mechanisms and this limits our ability to rationally design and optimise them.
Building on our recent work developing molecular simulation techniques to study drug-polymer interactions¹ here we describe atomistic molecular dynamics (MD) simulations that model the coating of a nanoparticle with a polymer layer when an aqueous phase containing the former is mixed with an organic phase containing the latter. Specifically, monomethoxy poly(ethylene)glycol (mPEG) - polycaprolactone (PCL) diblock copolymers have been modelled in four different hydrophilic (mPEG) /hydrophobic (PCL) molecular weight ratios and their interactions with indomethacin nanoparticles evaluated, for comparison with associated experimental studies.
In accordance with the actual experimental setup and true to the concentrations used, a system has been built to replicate the coating method. An initially biphasic simulation system comprising an acetone - polymer region and an aqueous region containing an indomethacin nanoparticle are allowed to equilibrate through MD. The trajectories provide detailed information on how specific polymer-drug, polymer-polymer, and polymer-solvent interactions all affect the coating process.
Combined with parallel experimental work, we are moving towards the realisation of “structure based formulation design” to complement the process of “structure based drug design” which is already very well established in the pharmaceutical industry.
1. Mackenzie, R.; Booth, J.; Alexander, C.; Garnett, M. C.; Laughton, C. A., Multiscale Modeling of Drug-Polymer Nanoparticle Assembly Identifies Parameters Influencing Drug Encapsulation Efficiency. Journal of Chemical Theory and Computation 2015,11 (6), 2705-2713.

Pentaerythritol tetranitrate (PETN), a high explosive, initiates with traditional shock and thermal mechanisms. In this study, a novel approach to synthetically link optically active chromophores to existing molecular energetic materials is explored. The tetrazine-substituted derivative of PETN, pentaerythritol trinitrate chlorotetrazine (PetrinTzCl), is being investigated for a photochemical initiation mechanism that could allow control over the chemistry contributing to decomposition and leading to initiation. PetrinTzCl exhibits a photochemical quantum yield (QYPC) at 532 nm not evident with PETN. Using static spectroscopic methods, energy absorption on the tetrazine (Tz) ring that resulting in photodissociation yielding N2, Cl-CN, and Petrin-CN as the major photoproducts has been observed. Density functional theory (DFT) calculations imply this excitation mechanism follows sequential photon absorption allowing for tunability and control. Nonadiabatic excited state molecular dynamics (NA-ESMD) simulations have been performed to model the photochemistry of photoactive energetic materials in order to predict primary photoproducts, relative timescales, and quantum yields. Our simulations demonstrate that the relaxation mechanism leading to the observed photochemistry in PetrinTzCl is due to vibrational excitation during internal conversion.

We use molecular dynamics to calculate the structural, elastic, and polar properties of crystalline ferroelectric β-poly(vinylidene fluoride), PVDF (-CH₂-CF₂-)_n with randomized trifluoroethylene (TrFE) as a function of TrFE content (0-50%) in the temperature range of 0-400 K. There is a very good agreement between the experimentally obtained and the computed values of the lattice parameters, thermal expansion coefficients, elastic constants, polarization, and pyroelectric coefficients. A continuous decrease in Young's modulus with increasing TrFE content was observed and attributed to the increased intramolecular and intermolecular repulsive interactions between fluorine atoms. The computed polarization displayed a similar trend, with the room temperature spontaneous polarization decreasing by 44% from 13.8 mu;C/cm² (pure PVDF) to 7.7 mu;C/cm² [50/50 poly(VDF-co-TrFE)]. Our results show that molecular dynamics can be used as a practical tool to predict the mechanical and polarization-related behavior of ferroelectric poly(VDF-co-TrFE). Such an atomistic model can thus serve as a guide for practical applications of this important multifunctional polymer.

Charge injection at metal/polymer interfaces in electrical systems such as capacitors and cables is believed to lead to progressive degradation, and ultimately, to the failure of the embedded polymer dielectric layers. This process is governed by the electron and hole injection barriers at the interface, which are, in principle, determined by the appropriate electronic properties of the metal (i.e., its work function), the polymer (i.e., its band gap and electron affinity), and the interfacial region (i.e., its dipole moment). Because of the chemical and physical complexity of typical polymers, e.g., polyethylene, unraveling the specific roles of these factors in determining charge injection barriers is thus far from trivial. Nevertheless, such a knowledge is critical to the design of high electric field tolerant materials and devices.
In this work, we compute the charge injection barriers of metal/polyethylene interfaces using density functional theory computations, with the metal being Al, Ag, Au, Pd, or Pt (these are conventional choices for practical and model electrodes). Different geometries of the interface between polyethylene and the metal were considered in an attempt to span a few extreme cases of the interface configurations. Several relevant electronic properties (i.e., metal work function, band gap and electron affinity of PE, and the interfacial dipole moment), and the charge injection barriers are computed for these cases. The calculations reveal some important trends and correlations, and the favored mechanism of charge transport (as mediated by the charge injection barriers). While satisfactory correspondences of the computations with available measurements are achieved, quantitative discrepancies still remain between the computed and measured injection barriers. These issues may be resolved when more realistic models of the interface, inclusive of its morphological complexities, are utilized.

Nanofabrication by the use of self-assembly of block-copolymer has been an attractive choice for the semiconductor manufacturing industry, since it has the potential to break through the resolution limit of the current 193 nm immersion lithography[1]. The most commonly used block copolymer is polystyrene-block-poly(methyl methacrylate) (PS-b-PMMA), whose phase-separated domains have a periodicity on the order of 20-50 nm. To achieve sub-20 nm periodic structures, however, the segregation strength of the PS-b-PMMA becomes insufficient[2][3].
Polyhedral oligomeric silsequoxane (POSS) oligomer is a fascinating organic-inorganic hybrid material that is capable of self-assembling into sub-10 nm periodic structures[4]. This molecule consists of cage-like octa-functional silica and organic substituents. It has been demonstrated experimentally that the self-assembled morphology of POSS oligomer could be lamellae with the periodicity of ~5nm, and that the periodicity could be controlled by the length and number of the alkyl chains tethered to the POSS[4]. However, the mechanism of the self-assembly of POSS oligomers is still not well understood; finding an optimal molecular design for sub-10 nm patterning application remains challenging.
In this study, we performed molecular dynamics simulations of the POSS-oligomer bilayers to investigate their thermal and mechanical properties. The force field employed here was a combination of COMPASS and TraPPE that was developed for the study of physical properties of polymer-POSS nanocomposites[5]. First, we characterized the thermal properties (e.g., melting temperature T_m) of octa-methyl POSS in the bulk and checked if the hybrid force field could provide a reasonable estimate of the T_m. It should be noted that in some previous studies, the T_m of POSS crystals were largely overestimated due to a very high heating/cooling rate[6].Here we used the so-called interfacial method [7] and obtained the T_m closer to the experimental value.
In subsequent simulations, the T_m of POSS-oligomer bilayer is being investigated. The phase transitions of POSS and alkyl chain layer are expected to give rise to the two-stage melting as evidenced by differential scanning calorimetry. In addition to the thermal properties, the mechanical properties (e.g., local moduli) of the POSS and alkyl layers are being investigated as functions of the length and number of the alkyl chains tethered to the POSS. Results of these investigation will be discussed.
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Rosette nanotubes (RNTs) are supramolecular nanomaterials formed through a network of non-covalent interactions, such as hydrogen bonds, π-π interactions, and hydrophobic effects. They form from Watson-Crick inspired guanine-cytosine (GΛC) motifs, which undergo hierarchical self-assembly to form supermacrocycles, also called rosettes. The latter self-organize into ring stacks (RSs) or helical coils (HCs), which form the RNTs. Several studies have analyzed the effects of changing the temperature, pH, and solvent system during self-assembly on RNTs. These studies were verified using NMR spectroscopy, mass spectrometry, scanning electron microscopy (SEM), atomic force microscopy (AFM), transmission electron microscopy (TEM), scanning tunneling microscopy (STM), UV-vis melting studies, dynamic light scattering (DLS), and circular dichroism (CD). In support of these experimental techniques and to aid the design, synthesis, and characterization of new RNTs, multi-scale molecular modeling techniques can be used to predict their structure and the energetics of the system. The RNTs were shown to be biocompatible and have been used in a wide variety of biological applications. For example, they enhance osteoblasts and endothelial cells adhesion and growth, which makes them good candidates as coatings for orthopedic implants and vascular stents. Due to the less hydrophilic nature of the RNTs&’ cavity, hydrophobic drugs such as dexamethasone and tamoxifen can be encapsulated inside the RNT channel as shown in published preliminary studies.
With new applications, ease of synthesis and production in mind, novel derivatives of GΛC modules are constantly being developed. One such motif is the polyethylene glycol-grafted twin base linked GΛC module (PEG₅₀₀-TBL). In this study, molecular mechanics (MM), molecular dynamics (MD), Monte Carlo methods (MC), and the statistical mechanical theory of solvation, also known as the 3 dimensional reference interaction site model (3D-RISM) theory were applied to predict the structure of RNTs from PEG₅₀₀-TBL motifs in water and the solvation assembly, and to provide insight into the self-assembly process. The most probable conformations of individual motifs were first determined by Monte Carlo Multiple Minimum method (MCMM) by varying the dihedral angles of the PEG₅₀₀ functional group. Then RNTs were built from the motifs and minimized. MD simulations were then run to determine the stability and the most probable structure of the RNTs. 3D-RISM integral equations were solved for the system to determine the solvation structure, energetics, and to propose a self-assembly pathway for the RNTs. Using the most probable structure of the PEG₅₀₀-TBL-RNT, the possibility of encapsulating the anti cancer drug paclitaxel (PTX) in the RNTs was also investigated. The RNT-PTX complex&’s stability was tested using MD simulations in water.

In the present work a systematic theoretical study on 1-(1-hydroxy-4-methyl-2-phenylazo)-2-naphthol-4-sulfonic acid was performed. All possible protonation and deprotonation degrees were analyzed. The use of solvents in calculations affect in a direct way the stability of azo and hydrazone tautomers. This is the first systematic theoretical study, from electronic structure point of view, on this widely used azodye. Theoretical absorption spectra at distinct pH could be simulated varying the protonation a deprotonation degree, then, generalize gradient approximation, hybrid methods and Coulomb attenuating method in addition to one semi-empirical method were utilized to compare theoretical and experimental absorption spectra at different pH. The main peaks and shoulders observed in experimental spectra were assigned to its correct conformer. It was found that generalized gradient approximation performs very well the first transition peak at neutral pH. For higher pH all methodologies got a bathochromic shift in agreement with experiment , from these theoretical results, it was obtained that this azodye is hardly protonated in experiments since results presented here, predict a variation of absorption spectra for all proposed methodologies when the molecule is protonated, which is diferent to experimental results. Additionally, local reactivity utilizing the Fukui function was obtained. Interestingly, reactivity of the molecule undergoes no significant changes despite azo-hydrazone tautomerism, since the Fukui functions are not modified in a significant way in both tautomers. It is worth mentioning that results presented here are important in studies about degradation of this kind of azodyes, in studies when calmagite forms complex compounds with metallic and lanthanide atoms or in photoabsorption studies since spectrum depends on protonation and deprotonation degree and on the most stable conformers in the sample.

Applications that exploit the bio-titania interface include biomedical implants, biosensors and photovoltaics. However, the critical biomolecular recognition events and the interfacial interactions that enable these applications to be realized remain far from being understood. Predicting and controlling bio-interfacial properties is extremely challenging without a molecular-level comprehension of how biomolecules interact with material surfaces, and the factors that influence these interactions. Here, we utilize cutting-edge techniques such as Replica Exchange with Solute Tempering (REST)¹ and Metadynamics to study, at the negatively-charged aqueous rutile TiO₂ (110) interface, the adsorption of amino acids and titania-binding peptides (namely Ti1, QPYLFATDSLIK, and Ti2, GHTHYHAVRTQT)² and calculate their binding free energies. Our results revealed a stronger affinity between charged amino acids and the titania surface compared to uncharged amino acids.³ In addition, peptides Ti1 and Ti2 are found to have a similar free energy of adsorption but different binding mechanisms. While Ti2 reveals an enthalpic binding, Ti1 shows an entropically-driven binding mechanism.⁴ Moreover, the effect of using Ca²⁺ counterions, in contrast to Na⁺, on the adsorption of both peptides at aqueous titania is also studied. Finally, the impact of His protonation state on the adsorption of Ti2 is evaluated. Our results provide valuable insights into the complex interplay between peptide sequence, structure, and binding at the aqueous TiO₂ interface.
Terakawa, T., Kameda, T. and Takada, S. J. Comput. Chem. (2010) 32, 1228-1234.
Puddu, V., Slocik, J.M., Naik, R.R. and Perry, C. Langmuir (2013) 29, 9464-9472.
Sultan, A.M., Hughes, Z.E. and Walsh, T.R. Langmuir (2014) 30, 13321-13329.
Tang, Z., Palafox-Hernandez, J.P., Law, W.C., Hughes, Z.E., Swihart, M.T., Prasad, P.N. and Walsh, T.R. ACS Nano (2013) 7, 9632-9646.

An Electroactive polymer (EAP) is capable of converting electrical energy into mechanical energy. In this paper the thermoelastic effect in field-based EAP, dielectric elastomer (DE), is studied, . A thermodynamic model is developed to analyze the nonlinearity of dielectric constant induced by the stretch and temperature change. The ferroelectricity when the temperature falls below the Curie point is studied during the polarization. Experimental characterization is conducted to evaluate how the ambient temperature and pre-stretch affect the actuation performance, which exhibits an exceptionally large influence of temperature on the deformation of DEs. For DEs with pre-stretch of 2 by 2, the increase of temperature from -10#8451; to 80#8451; enhances the actuation strain up to more than 1700%. Furthermore, low pre-stretched DE is more susceptible to temperature variation. High pre-stretched DE is relatively insensitive to temperature, for its energy conversion is dominated by mechanical stretching rather than thermal conduction.

Both biomineralisation and bioattachment involve an interface between minerals (hard) and organic molecules and arrays (soft). Such an interface can exercise control in two directions. On the one hand, the binding of large molecules on surfaces can induce conformational folding and consequent effects on molecular function. On the other hand, soft matter in the form of molecules, or arrays can control the nucleation and growth of crystals. In many cases, the process involves an amorphous phase. The resulting materials have complex structures, often with distinctive features at different lengthscales. We present a number of examples from our recent work showing how a combination of theory and experiment can shed light on the fundamental mechanisms involved. These examples include the effect of binding on the conformation of biomolecules, the effect of biomolecules such as ovocleidin and struthiocalcin on crystal nucleation and the control of crystal growth by molecules such as polysaccharides and peptoids.

Due to their promising properties of light-weight materials in, e.g. the aerospace industry, carbon fiber reinforced polymer (CFRP) composites are the subject of intense scrutiny [1,2]. Of particular interest is the structure/property relationship arising from the polymer interphase, which strongly influences the mechanical properties of the material. Investigating the polymer structure at the interface/interphase between polymer and carbon fibre (CF) using experimental techniques alone may be challenging. Here, molecular dynamics (MD) simulations can complement experiments, to bridge the gap in better capturing the interfacial phenomena [3,4] . Results obtained via MD simulations can be compared to experimental results, to enable advances in our search for composite materials with superior properties.
Here, we investigate a well-studied epoxy polymer, EPON-862/DETDA, to create a CFRP composite model. To do this, a reliable in-situ cross-linking procedure is developed, which enables us to form cross-links in the presence of CF surface. We consider a surface functionalization of the CF surface as proposed by our experimental partners [5] which can react with the EPON. To systematically evaluate the effects of surface functionalization, we study four different systems: (1) the pure epoxy polymer, (2) epoxy CFRP with a bare graphene surface, (3) CFRP with a functionalized surface that is not reactive with EPON, and (4) CFRP with a functionalized surface, that can form covalent bonds with EPON. Our investigation has a twofold purpose. First, the effect of the presence of the CF surface on the mechanical properties of the CFRP was investigated. Second, the functional group was studied to understand its effect on the mechanical performance of the composite. Our findings provide a platform for the systematic and knowledge-based design and testing of CF surface treatments for generating new CFRPs with superior properties.
References
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[2] S. Shiu and J. Tsai, Compos. Part. B-Eng., 56 (2014), 691
[3] C. Li et al., Compos. Part. A-Appl. S., 43 (2012), 1293
[4] R. Rahman and A. Haque, Compos. Part. B-Eng., 54 (2013), 353
[5] L. Servinis et al., J. Mater. Chem. A, 3 (2015), 3360

Fibrinogen, a blood glycoprotein of vertebrates, plays an essential role in blood clotting by polymerizing into fibrin upon activation. It also contributes, upon adsorption on material surfaces, to determine their biocompatibility and has been implicated as a cause of thrombosis
and inflammation at medical implants. Here we present the first fully atomistic simulations of the initial stages of the adsorption process of fibrinogen on mica and graphite surfaces. The simulations reveal a weak adsorption on mica that allows frequent desorption and reorientation
events. This adsorption is driven by electrostatic interactions between the protein and the silicate surface as well as the counter ion layer.
Preferred adsorption orientations for the globular regions of the protein are identified. The adsorption on graphite is found to be stronger with fewer reorientation and desorption events, and showing the onset of denaturation of the protein.

Microscopic understanding and control of protein-surface interactions is gaining an increasing interest due to the new development of bio-interfaces for medical and bio-technological applications.
In this contribution we aim to provide a characterization of different peptides / gold interactions at a molecular level in order to explain and interpret recent surface experimental results.
We have devised a novel scheme to include the metal polarization (image charge effect) induced by the adsorbed molecules into atomistic simulations. Our scheme can easily complement currently used 12-6 Lennard-Jones potentials, as included in simulation packages as GROMACS and LAMMPS. Extensive tests have been performed for the force field validation and comparisons with quantum mechanics (QM) density functional theory (DFT) calculations are also discussed.
Results for aminoacids nano assembly on different gold surfaces are presented.

Density Functional Theory (DFT), Agent-based modeling and Scanning Tunneling Microscopy are used to investigate the self-assembly of (RS)-N⁹-(2,3-Dihydroxypropyl)adenine (DHPA), a nucleoside analog of Adenosine, on the Au(111) surface. It is found that DHPA self-organizes along the herringbone reconstruction of Au(111) into double-chain structures that remain well organized up to the micron scale. DFT calculations reveal that the polymer is held together by a variety of hydrogen bonds involving the two moieties of DHPA. An Agent-based modeling algorithm is designed to predict self-assembled patterns, and it is found that with this technique self-assembly at the mesoscale can be predicted in an efficient and precise manner.

Applications requiring pristine graphene derived from graphite demand a solution stabilization method that utilizes an easily removable media. We use large scale atomistic molecular dynamics simulations to demonstrate solubilization/stability of pristine graphene sheets in mixed solvents. Our simulations have shown that the graphene surface templates self-assembly of an equimolar mixture of benzene and hexafluorobenzene into periodic layers extending from both sides of the graphene sheet. The solvent structuring is driven by quadrupolar interactions and consists of alternating C6H6/F6H6 molecules extending from the surface of graphene. This solvent self-assembly stabilizes exfoliated graphene sheets, opening a path to stable graphene suspensions. We have also performed simulations of exfoliated graphene sheets in two immiscible solvents such as water and oil (heptane and styrene). These simulations have shown that exfoliated graphene sheets are captured by the water/oil interface, demonstrating properties expected for surfactants. Graphene layers localized at the interface are capable of restacking, forming an interface covering graphitic skin. Graphene stacks are located at the water/oil interface with slight preference towards the oil phase. We used potential of the mean force calculations to evaluate the strength of attraction between graphene and the water/oil interface. The surface activity of graphene was confirmed experimentally by formation of stable water-in-oil emulsions stabilized by graphitic skin formed at the water/oil interface. Polymerization of the continuous styrene phase produced graphene infused polymer foams with remarkable conducting and mechanical properties.

DNA has been widely used with various surfaces for applications such as sensors. Recently, graphene and graphene oxide (GO) have attracted intense research interest due to their interesting physical and chemical properties. However, the underlying processes of DNA binding to these surfaces are still unclear as the published studies are controversial. Therefore, it is imperative to understand which properties of a surface are major factors for the stabilization or destabilization of DNA. In an attempt to address this issue and elucidate the mechanisms of the physical adsorption of DNA on graphene-based surfaces, we performed all-atom molecular dynamics simulations of pre-folded ssDNA on graphene and GO surfaces in explicit water. Simulation results indicated that, for graphene, ssDNA was completely adsorbed via pi-pi stacking interactions between DNA bases and the graphene. In contrast, ssDNA formed hydrogen bonds with hydroxyl and epoxy groups of GO maintained its folded conformation. Judging from pi-pi stacking interactions and water bridging analysis, we conclude that pi-pi interactions between DNA bases and the surface play a key role in strong adsorption and DNA unfolding on graphene. However, GO has less propensity to form pi-pi interactions due to steric hindrance by hydroxyl and epoxy groups sticking out from the surface and numerous bridging water molecules in between ssDNA and GO that strongly hold ssDNA and prevent bases from forming pi-pi stacking interactions with the surface. We also found that upon the adsorption of ssDNA to largely non-polar surfaces, the persistence length of the ssDNA was higher than in solution. However, as the polarity of the surface increased, the persistence length approached that exhibited in solution indicating the retention of a globular fold of ssDNA. In summary, our results demonstrate mechanisms of physical adsorption between ssDNA and graphene-based surfaces. It is expected that this study can improve the fundamental understanding of these mechanisms and provide insights for many applications using DNA.

Non-covalent functionalization of Au nanoparticles (AuNPs) with biomolecules offers a viable route to realizing potential applications in fields such as catalysis, smart materials and nano-medicine.[1] One of the most widely used methods for preparing monodisperse AuNPs under aqueous conditions is the Turkevitch-Frens reduction process,[2] where citrate acts as a capping and dispersion agent for AuNPs. Despite efforts to experimentally characterize the adsorbed citrate overlayers[3] the molecular level details regarding the structure and features of the adsorbed citrate overlayer at the aqueous Au interface are still unclear. The molecular-level impact of these overlayers on the adsorption of biomolecules is also not well understood. Experimental binding characterization, for example using surface plasmon resonance spectroscopy, is typically measured on flat Au surfaces in the absence of citrate. However, a clear determination of how the presence of citrate molecules affects the adsorption of biomolecules onto AuNPs in solution, via experimental techniques alone, is highly challenging.
As an alternative, we use molecular dynamics (MD) simulations, with a polarizable force-field,[4] to predict the structure of citrate overlayers at the aqueous Au(111) interface and identify two classes of overlayer morphology.[5,6] Furthermore, using a Hamiltonian replica exchange method[7] we investigate, compare and quantify the structural ensemble of a well-characterized gold binding peptide, considered both in-solution and adsorbed at the aqueous Au interface, in both the presence and absence citrate molecules.
[1] Saha, K., et al.; Gold Nanoparticles in Chemical and Biological Sensing, Chem. Rev., 2012, 112, 2739-2779.
[2] Frens, G.; Controlled Nucleation for Regulation of Particle-Size in Monodisperse Gold Suspensions, Nature Phys. Sci., 1973, 241, 20-22.
[3] Lin, Y., et al.; Study of Citrate Adsorbed on the Au(111) Surface by Scanning Probe Microscopy, Langmuir, 2003, 19, 10000-10003.
[4] Wright L.B., et al.; GolP-CHARMM: First Principles Based Force Fields for the interacton of Proteins with Au(111) and Au(100), J. Chem. Theory Comput., 2013, 9, 1616-1630.
[5] Wright, L.B., et al.; Aqueous Citrate: a First-Principles and Force-field Molecular Dynamics Study, RSC Adv., 2013, 3, 16399-16409.
[6] Wright, L.B., et al.; Structure and Properties of Citrate Overlayers Adsorbed at the Aqueous Au(111) Interface, Langmuir, 2014, 30, 15171-15180.[7] Terakawa, T., et al.; On easy implementation of a variant of the replica exchange with solute tempering in GROMACS, J. Comput. Chem., 2010, 32, 1228-1234.
[7] Terakawa, T., et al.; On easy implementation of a variant of the replica exchange with solute tempering in GROMACS, J. Comput. Chem., 2010, 32, 1228-1234.

Many high-performance natural composite materials found in nature such as bones or shells, contain nanocrystalline inorganic components that are formed or stabilized by the presence of peptides and proteins. Many of these biomineral-associated peptides/proteins exhibit the ability to carry out function in the absence of a well-defined secondary, a property of intrinsically disordered peptides/proteins (IDPs). [1] Furthermore a subset of these IDPs also display the ability to reversibly switch conformations from random-coil to a well-defined secondary structure in the presence of an external stimulus. [2]
Hydroxyapatite (HAP)-binding sequences such as osteocalcin and the de novo designed JAK1 peptide display disordered native states in aqueous solution, however the presence of calcium is thought to induce helical folding which in turn facilitates binding to hydroxyapatite. The interaction between Ca²⁺ and key post-translationally-modified γ-carboxylate glutamic acid (Gla) residues is thought to be responsible for triggering helical folding and binding functionality. Mutations of these sequences where Gla → Glu display a loss of helical folding and are thought to abrogate the ability to bind to HAP. [3,4]
To elucidate the molecular-level causes for these properties, bioinformatics approaches have previously sought to correlate sequence motifs and individual residues of the IDPs to their structural preferences, while experimental work has sought to explore the materials-binding ability of these sequences. Molecular simulations can offer new complementary, in-depth molecular-level insights into these often challenging to study reversible switching mechanisms.
Utilizing large-scale Replica Exchange with Solute Tempering (REST) molecular dynamics simulations we explore the Ca²⁺ induced α-helical folding of JAK1 and osteocalcin in CaCl₂ solutions and compare these with their behavior in NaCl solution.[5] We investigate the frequency and length of secondary structure motifs using Ramachandran plots, cluster analysis to explore the dominant conformations and residue-residue contact maps to determine key intra-peptide interactions. We also utilize metadynamics[6] simulations to explore the strength of the Ca²⁺ interactions with the Glu and Gla side-chains and suggest new force field parameters to improve the representation of these interactions. Our work provides molecular level insight into how environmental influences can mediate the conformational switching in some IDP sequences via side chain interactions, and highlights some of the challenges faced in the study of complex IDP systems using molecular simulation approaches.
[1] Uversky, V. N. Int. J. Biochem. 2011, 43, 1090
[2] Hauschka, P. V. et al. Biochemistry 1982, 21, 10, 2538
[3] Cristiani A. et al. Front Biosci, 2014, 19, 1105
[4] Capriotti, L. A. et al. J. Am. Chem. Soc. 2007, 129, 5281
[5] Terakawa, T. et al. J. Comp. Chem. 2010 32, 1228
[6] Barducci, A. et al. Phys. Rev. Lett., 2008, 100, 020603

Emulsification is a powerful age-old technique for mixing and dispersing immiscible components within a continuous liquid phase. Consequently, emulsions are central components of medicine, food, and performance materials. Complex emulsions, including multiple emulsions and Janus droplets, are of increasing importance in pharmaceuticals and medical diagnostics, in the fabrication of microparticles and capsules for food, in chemical separations, for cosmetics, and for dynamic optics. As complex emulsion properties and functions are related to the droplet geometry and composition, the development of rapid and facile fabrication approaches allowing precise control over the droplets&’ physical and chemical characteristics is critical. Significant advances in the fabrication of complex emulsions have been accomplished by a number of procedures, ranging from large-scale less precise techniques that give compositional heterogeneity using high-shear mixers and membranes to small-volume microfluidic methods. However, such approaches have yet to create droplet morphologies that can be controllably altered after emulsification. Reconfigurable complex liquids potentially have greatly expanded utility as dynamically tunable materials.
Using theories of interfacial energetics, we have modeled the interplay between interfacial tensions during the one-step fabrication of three- and four-phase complex emulsions displaying highly controllable and reconfigurable morphologies. The fabrication makes use of the temperature-sensitive miscibility of hydrocarbon, silicone, and fluorocarbon liquids and is applied to both microfluidic and scalable batch production of complex droplets. We demonstrate that droplet geometries can be alternated between encapsulated and Janus configurations via variations in interfacial tensions as controlled with hydrocarbon and fluorinated surfactants including stimuli-responsive and cleavable surfactants. Therefore, we have discovered a generalizable strategy for the fabrication of multiphase emulsions with controllably reconfigurable morphologies to create a diversity of responsive materials.

Nanoscopic objects stabilized with charged organics exhibit properties fundamentally different from either molecular or macromoleculer ions, and can combine ionic-like properties with electronic and ionic conductivity and/or photoexcitability. By careful control of electrostatic interactions, ”nanoions” of various shapes and material compositions can be assembled into functional nanomaterials including 3D supracrystals, ”layered” crystals, or extended films. Depending on the properties of the charged organics, these nanomaterials can act as chemical amplifiers, photoconductors, diodes, transistors, or even full-fledged electronic circuits containing no semiconductors. This last set of constructs can integrate on the nanoparticles electronic function with chemical sensing.

Crystalline solids exhibit a periodic arrangement of molecular units that allow mechanical, electrical, optical and other behavioral properties to be directly correlated to the solids' molecular and microscopic topology. However, amorphous solids like glasses and granular matter have disordered arrangements of their molecular or microscopic units that hinder efforts to correlate their structure to their behavior. Several scientists have resorted to using a volume-based ensemble to develop statistical mechanics for disordered solids. This volume-based ensemble along with simulations by Makse and collaborators have produced a thermodynamic phase diagram for packings of mono-disperse particles. The phase diagram is constructed from measurements of the average coordination number and packing density of jammed mono-disperse spheres. The work herein experimentally tests this phase diagram by preparing packings of fluorescently labeled colloidal particles at different packing densities and using the particles' fluorescent signal to measure coordination number.

For controlling segregation, it is important to understand the behavior of impurities on the solidification process. We constructed the simulation model of formation of monolayer colloidal crystals from uniform dilute suspensions by an advection accumulation method. Here, we considered the dispersion of N(=30000 and 60000) colloids with repulsive interactions, which consists of 98% identical normal colloids with radius a and 2% impurities with radius b. Fixed boundary conditions were applied on one pair of parallel sides of a rectangular simulation cell. Periodic boundary conditions were applied on the other pair. We approximated an advective flow by the constant flow perpendicular to the fixed boundary. By performing a large-scale Brownian dynamics simulation of the solidification process of monolayer colloids with the addition of differently sized impurities, we discussed the effect of size difference between normal colloids and impurities.
We found that big impurities induce the defects in crystal region and they are exhausted from more dense regions to less dense regions near the interfaces. Thus the fraction of big impurities in crystal region is smaller than that in the initial suspensions. These simulation results are in quantitative good agreement with the experimental results done by Nozawa et al (JPC B, 117, 5289-5295 (2013)). We also discuss the small impuries effect and the velocity dependence of advective flow on the solidiffication process in the meeting.

Despite recent reports of the synthesis and assembly of faceted nanoplates with a wide range of shapes and composition, studies of how nanoplate shape accompanied by perturbative attractive interactions controls their assembly - knowledge necessary for their inverse design from target structures - has been performed for only a handful of systems. In this comprehensive work, in which we describe many known nanoplates in terms of shape and enthalpic anisotropies, we performed Monte Carlo simulations to extract underlying design rules to guide future synthesis and assembly. In our first study we focus on attractive polygons whose shape is altered systematically under the following four transformations: faceting, pinching, elongation and truncation. Depending on the shape transformation, we report the formation of regular porous structures, stabilization of complex structures such as dodecagonal quasicrystals, asymmetric phase behavior and the transformation of a shape that assembles complex structures into one that behaves like a disk. We provide important insight into how the shape and attractive interactions of a nanoplate can be exploited or designed to target specific classes of structures, including space-filling, porous, and complex tilings. In our second study, we present experimentally accessible, rational design rules for the self-assembly of the Archimedean tilings from polygonal nanoplates. The Archimedean tilings represent a model set of target patterns that contain patterns with potentially interesting materials properties. We show for which tilings entropic patches alone is sufficient for assembly, and when and how patchy short-range enthalpic interactions are required. This study provides a minimal set of guidelines for the design of anisotropic patchy particles that can self-assemble all 11 Archimedean tilings.

Recently we have reported the ability to simultaneously control the nano-scale and micro-scale assembly of droplets made of star polymers which are used as scaffolds for tissue regeneration.¹ A variety of hollow, non-hollow, and porous structures were obtained via experiment, and were corroborated by simulation. Importantly, the droplet mesostructure was tunable via a single control parameter - the relative density of unreacted star polymer end groups (hydroxyls) within the block. Using massive dissipative particle dynamics simulations (DPD) of 1 to 10 million particles we describe how the droplet mesostructure depends critically on this single control parameter; by varying the star polymer architecture (arm length and number) we stabilize different droplet morphologies that can be predicted a priori from the hydroxyl density. Additionally, we describe the role of solvent selectivity between the hydroxyl groups and the polymer itself in determining the resultant mesostructure.
1. Zhang, Z.; Marson, R. L.; Ge, Z.; Glotzer, S. C.; Ma, P. X. “Simultane- ous Nano- and Microscale Control of Nanofibrous Microspheres Self-Assembled from Star-Shaped Polymers.” Advanced Materials (2015). doi: 10.1002/adma.201501329.

We have developed an advanced multiscale simulation system to predict the properties of polymer-clay nanocomposites based on their molecular structures and composition. These methods could have applications in modelling a wide range of materials and it is our aim to create a “virtual lab” to compute the properties of new soft materials based simply on knowledge of their chemical composition, molecular structure and processing conditions [1]. Here we will present our findings from modelling chemically specific combinations of clay and polymers, where we use our multiscale methods and tools to take us from a parameter free quantum description to atomistic and coarse-grained molecular dynamics simulations, ultimately leading to predictions of the materials properties of these nanocomposites. Our simulations approach realistic sizes of clay platelets (diameter 100 Å) at low clay volume fractions (5%). These systems exhibit property enhancements compared to the pristine polymer (elastic properties, gas permeation), but homogenous dispersion of the clay sheets is required.
Our multiscale approach provides us with predictions of the melt intercalation behaviour and final morphologies of montorillonite clay - polyvinyl-alcohol and montorillonite clay - polyethylene-glycol systems, and associated organic-treated clays. Many hitherto unobserved phenomena come into view as a result of this study, including the dynamical process of polymer intercalation into pristine and organo-treated clay tactoids and the ensuing aggregation of polymer-entangled tactoids into larger structures. We observe the role of surfactants and are able to elucidate how they facilitate polymer intercalation; ultimately we observe the mechanisms of clay sheet exfoliation, which is driven by attraction to the clay surfaces of the hydrophillic polymers.
From our multiscale simulations, we can compute various characteristics of these nanocomposites, including clay-layer spacings, out-of-plane clay sheet bending energies, X-ray diffractograms and materials properties, which we relate to the system's final morphology.
[1] J.L. Suter, D Groen, P.V. Coveney, Adv. Mater. 2015, 27, 966-984

Here we present the application of molecular simulations to the design of novel bioinspired nanosensors [1] for HIV-1 protease. A homodimer with 99 residues per subunit, HIV-1 protease is responsible for the formation of the mature virus particle and exhibits broad substrate recognition. Although it has been exploited as a drug target, only a few studies have examined its potential as a biomarker for HIV infection due to the complexity of detection and propensity for false positives [2-4]. Through the use of peptide-mediated Förster resonance energy transfer (FRET) [5], the presence of HIV-1 protease may be detected in solution when two peptide nanosensors are simultaneously complexed with the same protease molecule, resulting in an observed decrease in the fluorescence signal of the peptide-conjugated dye. However, the exact interactions between the peptides and the protease are not clear. We have undertaken a systematic molecular modeling approach to study these interactions using molecular docking, molecular dynamics, and advanced sampling techniques. Replica exchange with solute tempering simulations reveal the most favored binding orientations of the peptide nanosensors, with both peptides bound on opposite sides of the protease active site in similar orientations, including hydrophobic interactions with the hydrophobic pocket adjacent to the active site and with the protease flaps. These results provide a rationale for the experimentally observed FRET signal and may yield new possibilities in the detection and inhibition of HIV-1 protease.
[1] P.D. Howes, R. Chandrawati, M.M. Stevens “Colloidal nanoparticles as advanced biological sensors” Science 346:1247390 (2014)
[2] K.A. Mahmoud, J.H. Luong “A Sensitive Electrochemical Assay for Early Detection of HIV-1 Protease Using Ferrocene-Peptide Conjugate/Au Nanoparticle/Single Walled Carbon Nanotube Modified Electrode” Anal. Lett. 43:1680 (2010)
[3] Y. Choi, et al. “Fluorogenic Assay and Live Cell Imaging of HIV-1 Protease Activity Using Acid-Stable Quantum Dot-Peptide Complex” Chem. Commun. 46:9146 (2010)
[4] C. Esseghaier, A. Ng, M. Zourob “A Novel and Rapid Assay for HIV-1 Protease Detection Using Magnetic Bead Mediation” Biosens. Bioelectron. 41:335 (2013)
[5] I.L. Medintz, et al. “Proteolytic Activity Monitored by Fluorescence Resonance Energy Transfer Through Quantum-Dot-Peptide Conjugates” Nat. Mat. 5:581 (2006)

Proteins are among the most important biological molecules, and are responsible for the majority of physiological functions on living organisms. All proteins have well defined functions that range from DNA and RNA synthesis to catalysts of reactions or immune responses. Despite this diversity, all proteins are constructed of smaller fundamental units called amino acids [1] and the sequence that forms a particular protein is determined by information contained in DNA/RNA.
Each amino acid has different properties, of which the most important is the hydrophobicity. [2] Under normal physiological conditions, a protein will fold into a specific spatial tridimensional configuration known as the native conformation to be able to perform its function. The protein folding problem lies in determining a protein native conformation based solely on its sequence of amino acids. We examined the problem focusing on prediction of the final structure of arbitrary proteins rather than the process by which it folds by using genetic algorithms and a HP cubic lattice model [3]. In this reduced model we consider each amino acid to occupy one spot in a cubic lattice, and we assume the driving force of the folding process is the interaction between hydrophobic (H) and hydrophilic/polar (P) amino acids with the solution, neglecting all other interactions. This approach is in agreement with experimental evidence that globular proteins tend to arrange themselves with a hydrophobic core surrounded by a polar outer shell.
Genetic algorithms [4] have been successfully applied to simplified models of protein folding for some time [5]. We propose a modified genetic algorithm [8] to optimize protein structures on the 3D HP lattice model achieving equal or better results compared to current literature. To the canonical genetic algorithm we added a new operator called maternal effect. It is inspired on the biological occurring maternal effect where the phenotype of an individual is influenced by the genotype of the mother as well as its own [6]. It has been proposed and shown to cause a delay in convergence of the algorithm and affect the “error catastrophe"[7], minimizing the loss of good information due to rapid converging of the algorithm to local optima or high mutation rates.
[1] Hao, B. et al., SCIENCE 296 (2002) 1462.
[2] Dill, K. A., BIOCHEMISTRY 24 (1985) 1501.
[3] Guo, Y.-z. and Feng, E.-m., JOURNAL OF CHEMICAL PHYSICS 125 (2006).
[4] D. E. Goldberg, Genetic Algorithms in Search and Optimization (Addison-Wesley, 1989)
[5] Unger, R. and Moult, J., Genetic algorithm for 3d protein folding simulations, in Proceedings of the 5th International Conference on Genetic Algorithms, pages 581-588
[6] A. J. F. Griths, An Introduction to genetic analysis(New York: W. H. Freeman, 1999)
[7] C. O. Wilke, Phys. Rev. Lett. 88, 078101 (2002)
[8] Moura, F. A. and Galvao, D. S. - to be published.

Natural systems frequently exploit intricate multiscale and multiphasic structures to achieve remarkable properties. This talk will summarize our recent efforts aimed at mimicking such multitier organization by designing nacre-inspired systems that feature both high strength and toughness. Our vision is that understanding the nanoconfinement of solids in low-dimensional phases is crucial to mimic biological systems. First, I will outline an atomistically informed coarse-grained molecular dynamics (CGMD) approach to describing the thermomechanical behavior of confined polymers and 2D nanomaterials at extended length and time scales. Following this, I will present simulations that explain why polymers with very similar physical properties (PS/PMMA) exhibit comparable elastic properties but divergent glass-transition behavior in ultra-thin films. Following this, I will describe how nanoconfinement could be best utilized to achieve high strength and toughness in layered assemblies of polymers and functionalized graphene. These findings will culminate in analytical models that are aimed at predicting failure mechanisms and localization phenomena in layer-by-layer assemblies. Applications of the models to study size and interfacial effects in emergent materials such as nanocellulose will be briefly discussed. I will conclude by contrasting atomistic vs. continuum perspectives, as well as additive manufacturing vs. self-assembly approaches towards bioinspired materials-by-design.

Confining DNA in a nanochannel whose cross-sectional dimensions are approximately 50 nm has emerged as a promising way to obtain genetic information at the gene level through the technique of genome mapping. The technique relies on stretching and linearizing DNA to optically measure the genomic distance between sequence-specific probes on the DNA molecule. However, theoretical description of DNA confined in such channels whose cross sectional size is close to the persistence length is challenging due to the interplay between entropy and excluded volume. In this talk, we describe the statistics of semiflexible polymers confined in channels whose size is close to the persistence length. Through large-scale simulations of a coarse-grained model using the Pruned-Enriched Rosenbluth Method (PERM), we show that the extension of confined semiflexible polymers follow the scaling theory developed by Odijk about 7 years ago (Phys. Rev. E, 2008) for confinement in channels with square cross section. We then present results showing how these statistics change for channels with rectangular cross-section. Finally, we discuss implications of our work to genome mapping technology. Our work also reveals interesting analogies between the behavior of such confined semiflexible polymers and liquid-crystalline semiflexible polymer solutions.