Morphological Characterisation of Printed Networks of Nanomaterials using FIB-SEM Nanotomography

Networks of nanomaterials find applications in a wide variety of fields such as printed electronics, sensors, and energy storage[1]. While it is known that the morphology of the network plays a dominant role in determining their physical properties, quantitative measurements of morphology have proven difficult[2]. It has been demonstrated that changing nanomaterial size changes the network’s electronic properties, however, its effect on network morphology is not well understood[3]. Utilizing FIB-SEM nanotomography we characterize the morphology of networks of nanomaterials. 3D-images with a voxel size of 5 x 5 x 15 nm were obtained from printed networks of graphene with different nanosheet length. Using machine learning, 3D images were segmented into nanosheet and pore components and a wide range of morphological properties of the networks, including porosity, pore shape, nanosheet alignment, nanosheet aggregation and pore and nanosheet tortuosity were calculated. These properties were used to distinguish between the effect of network morphology and nanosheet size on the electrical properties of the networks, allowing calculation of the resistance of the junctions between the nanosheets. The same methodology was applied to printed networks composed of silver nanoplatelets, silver nanowires and tungsten disulphide with varying nanomaterial length. The technique was then extended to investigate the interfaces within vertical printed hetrostacks, allowing measurement of the interfacial roughness and material penetration into subsequent layers, demonstrating the ability of this technique to characterize printed hetrostacks devices[4].<br/>[1] F. Bonaccorso, A. Bartolotta, J.N. Coleman, C. Backes, Adv Mat., 28, 6136-3166, (2016)<br/>[2] A.G. Kelly, D. O’Suilleabhain, C. Gabbett, and J.N. Coleman, Nature Reviews Materials, 7.3, 217-234 (2022).<br/>[3] S. P. Ogilvie <i>et al</i>, Nanoscale, 2022, 14, 320<br/>[4] C. Gabbett, L. Doolan et. al, preprint, https://doi.org/10.21203/rs.3.rs-2723977/v1.