Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
In the context of the rapid development of artificial intelligence and robotics, their application scenarios are continuously expanding to a variety of complex environments, with increasing attention being paid to the use of flexible sensors in low-temperature environments. In this study, an ionic hydrogel was synthesized using acrylamide (AM), hydroxyethyl cellulose (HEC), and lithium chloride (LiCl) as composites. This hydrogel exhibits high adhesion, excellent sensitivity (gauge factor (GF) = 2.84), rapid response time (100 ms), exceptional stretch ability (> 1776%), high toughness (2.5 MJ/m3), and the ability to maintain detectability at low temperatures (−60 °C). HEC imparts reliable mechanical properties to the sensor through hydrogen bonding interactions of its hydroxyl groups. LiCl ensures that the sensor has outstanding anti-freezing properties, maintains good conductivity and mechanical performance. Used for robotic attitude detection, the sensor demonstrated accurate recognition of various joint movements at both 20 and −20 °C. This technology was extended to industrial operations and maintenance, where a mechanical claw was used to grasp parts at both room temperature and low temperature. A convolutional neural network deep learning algorithm was employed to identify and classify eight types of parts, achieving an impressive recognition accuracy of 98.8%. The polyacrylamide (PAM)/HEC/LiCl hydrogel sensor demonstrates the capability for wide-temperature range detection in flexible robotics, holding significant potential for future applications in human-machine interaction, tactile perception, and related fields.

This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).
Comments on this article