Robotic Manipulation under Harsh Conditions Using Self‐Healing Silk‐Based Iontronics-Reference-Cited by-同舟云学术

Robotic Manipulation under Harsh Conditions Using Self‐Healing Silk‐Based Iontronics

Published:2021-11-05 Issue:2 Volume:9 Page:2102596
ISSN:2198-3844
Container-title:Advanced Science
language:en
Short-container-title:Advanced Science

Author:

Liu Mengwei¹²,Zhang Yujia¹²,Zhang Yanghong¹²,Zhou Zhitao¹²,Qin Nan¹²,Tao Tiger H.¹²³⁴⁵^ORCID

Affiliation:

1. State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China

2. School of Graduate Study University of Chinese Academy of Sciences Beijing 100049 China

3. Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing 100049 China

4. 2020 X‐Lab Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China

5. School of Physical Science and Technology ShanghaiTech University Shanghai 200031 China

Funder

National Outstanding Youth Science Fund Project of National Natural Science Foundation of China

National Natural Science Foundation of China

Youth Innovation Promotion Association of the Chinese Academy of Sciences

Publisher

Wiley

Subject

General Physics and Astronomy,General Engineering,Biochemistry, Genetics and Molecular Biology (miscellaneous),General Materials Science,General Chemical Engineering,Medicine (miscellaneous)

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/advs.202102596

Reference38 articles.

1. Epidermal mechano-acoustic sensing electronics for cardiovascular diagnostics and human-machine interfaces

2. Skin-integrated wireless haptic interfaces for virtual and augmented reality

3. Sign-to-speech translation using machine-learning-assisted stretchable sensor arrays

4. Bioinspired Self‐Healing Human–Machine Interactive Touch Pad with Pressure‐Sensitive Adhesiveness on Targeted Substrates

5. Fusing Stretchable Sensing Technology with Machine Learning for Human–Machine Interfaces

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