基于碳纳米管渗流网络的人工快速自适应机械感受器。

Artificial fast-adapting mechanoreceptor based on carbon nanotube percolating network.

机构信息

Department of Electrical Engineering and Computer Engineering, Interdisciplinary Institute for Technological Innovation (3IT), Université de Sherbrooke, Sherbrooke, QC, Canada.

Sorbonne Université, Institut des Systèmes Intelligents et de Robotique, 75005, Paris, France.

出版信息

Sci Rep. 2022 Mar 9;12(1):2818. doi: 10.1038/s41598-021-04483-2.

DOI:10.1038/s41598-021-04483-2

PMID:35264589

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8907247/

Abstract

Most biological sensors preferentially encode changes in a stimulus rather than the steady components. However, intrinsically phasic artificial mechanoreceptors have not yet been described. We constructed a phasic mechanoreceptor by encapsulating carbon nanotube film in a viscoelastic matrix supported by a rigid substrate. When stimulated by a spherical indenter the sensor response resembled the response of fast-adapting mammalian mechanoreceptors. We modelled these sensors from the properties of percolating conductive networks combined with nonlinear contact mechanics and discussed the implications of this finding.

摘要

大多数生物传感器更倾向于编码刺激的变化，而不是稳定的成分。然而，目前还没有描述内在的相敏人工机械感受器。我们通过将碳纳米管薄膜封装在由刚性基底支撑的粘弹性基质中来构建相敏机械感受器。当受到球形压头刺激时，传感器的响应类似于快速适应的哺乳动物机械感受器的响应。我们根据渗流导电网络的特性结合非线性接触力学对这些传感器进行了建模，并讨论了这一发现的意义。