Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Soft strain gauges provide a flexible and versatile alternative to traditional rigid and inextensible gauges, overcoming issues such as impedance mismatch, the limited sensing range, and fatigue/fracture. Although several materials and structural designs are used to fabricate soft strain gauges, achieving multi-functionality for applications remains a significant challenge. Herein, a mechanically interlocked gel-elastomer hybrid material is exploited for soft strain gauge. Such a material design provides exceptional fracture energy of 59.6 kJ m and a fatigue threshold of 3300 J m , along with impressive strength and stretchability. The hybrid material electrode possesses excellent sensing performances under both static and dynamic loading conditions. It boasts a tiny detection limit of 0.05% strain, ultrafast time resolution of 0.495 ms, and high linearity. This hybrid material electrode can accurately detect full-range human-related frequency vibrations ranging from 0.5 to 1000 Hz, enabling the measurement of physiological parameters. Additionally, the patterned soft strain gauge, created through lithography, demonstrates superior signal-noise rate and electromechanical robustness against deformation. By integrating a multiple-channel device, an intelligent motion detection system is developed, which can classify six typical human body movements with the assistance of machine learning. This innovation is expected to drive advancements in wearable device technology.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10375198 | PMC |
http://dx.doi.org/10.1002/advs.202301116 | DOI Listing |
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