MRS Meetings and Events


SB05.04.02 2023 MRS Fall Meeting

Biomimetic Designs of Organic Electrochemical Transistors for Human-Interfaced Biosensing and Neuromorphic Computing

When and Where

Nov 28, 2023
8:15am - 8:30am

Hynes, Level 1, Room 102



Sihong Wang1

The University of Chicago1


Sihong Wang1

The University of Chicago1
The use of electronic devices for acquiring biological information and delivering therapeutic interventions relies on two aspects: the effective acquisition of physiological information through direct contact with soft bio-tissues, and the high-throughput processing of the acquired data using machine learning. To ensure high-quality signal transductions, the interfaces between bioelectronic devices and bio-tissues must combine signal amplification with stable and conformable contact. Organic electrochemical transistors (OECTs) have been developed as one of the most advanced technologies for high-performance bio-sensing and neuromorphic computing. Moreover, the fastest and most energy-efficient data processing through machine learning is directly on the human body. However, the rigid mechanical properties and the lack of tissue/skin adhesion from transistors largely prevent the formation of such intimate and long-term stable bio-interfaces. In the first part of my talk, I will introduce our material and device designs for introducing three highly important biomimetic properties onto OECT-based biosensors—stretchability, tissue-like softness, and bioadhesive properties. Our rationale designs from the material to the device level allow the realization of these properties with state-of-the-art electrical performance. I will also introduce the strategies and advantages of using these new biomimetic properties in bioelectrical and biochemical sensing. In the second part of my talk, I will introduce our research in imparting intrinsic stretchability onto neuromorphic devices that can provide state-of-the-art computing performance. I will also show the practical applicability of this device for implementing machine-learning computing and algorithms for health data analysis, when the computing hardware is under human-body-induced deformation.



Symposium Organizers

Herdeline Ann Ardoña, University of California, Irvine
Guglielmo Lanzani, Italian Inst of Technology
Eleni Stavrinidou, Linköping University
Flavia Vitale, University of Pennsylvania

Symposium Support

iScience | Cell Press

Publishing Alliance

MRS publishes with Springer Nature