MRS Meetings and Events

 

SB10.08.05 2024 MRS Spring Meeting

Nature and Human-Inspired Sensing Devices

When and Where

Apr 24, 2024
11:30am - 12:00pm

Room 429, Level 4, Summit

Presenter

Co-Author(s)

Benjamin Tee1

National University of Singapore1

Abstract

Benjamin Tee1

National University of Singapore1
B10- Bioinspired Organic Materials and Devices for Sensing and Computing<br/><br/>Sensing systems provide timely<sup>1</sup> and requisite data for machines to operate in physical environments. Often, natural biological systems provide optimized concepts that could be applied to design and engineer artificial sensor systems. Novel organic-based materials can even provide self-healing capabilities to such sensor devices<sup>2</sup>. In this talk, I will discuss our recent advances in nature- and human-inspired materials for designing high performance sensor devices<sup>3,4</sup>. The exciting cross-disciplinary interplay of physical interfaces, materials chemistry and electronics become essential for optimizing sensitivity, linearity and speed for the sensor system.<br/><br/><br/><b>References </b><br/><br/><br/>1. Lee, W. W., Tan, Y. J., Yao, H., Li, S., See, H. H., Hon, M., Ng, K. A., Xiong, B., Ho, J. S., & Tee, B. C. K. (2019). A neuro-inspired artificial peripheral nervous system for scalable electronic skins. <i>Science Robotics</i>, <i>4</i>(32), eaax2198. https://doi.org/10.1126/scirobotics.aax2198<br/>2. Hashina Parveen Anwar Ali, Zichen Zhao, Yu Jun Tan, Wei Yao, Qianxiao Li, and Benjamin C. K. Tee, <i>ACS Applied Materials & Interfaces</i> <b>2022</b> <i>14</i> (46), 52486-52498, DOI: 10.1021/acsami.2c14543<br/>3. Cheng, W., Wang, X., Xiong, Z. et al. Frictionless multiphasic interface for near-ideal aero-elastic pressure sensing. Nat. Mater. (2023). https://doi.org/10.1038/s41563-023-01628-8<br/>4. Yao, H., Yang, W., Cheng, W., Tan, Y. J., See, H. H., Li, S., Ali, H. P. A., Lim, B. Z. H., Liu, Z., & Tee, B. C. K. (2020). Near–hysteresis-free soft tactile electronic skins for wearables and reliable machine learning. <i>Proceedings of the National Academy of Sciences</i>, 202010989. https://doi.org/10.1073/pnas.2010989117

Symposium Organizers

Simone Fabiano, Linkoping University
Sahika Inal, King Abdullah University of Science and Technology
Naoji Matsuhisa, University of Tokyo
Sihong Wang, University of Chicago

Symposium Support

Bronze
IOP Publishing

Publishing Alliance

MRS publishes with Springer Nature