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
Flexible nanofiber membranes are compelling materials for the development of functional multi-mode sensors; however, their essential features such as high cross-sensitivity, reliable stability and signal discrimination capability have rarely been realized simultaneously in one sensor. Here, a novel multi-mode sensor with a nanofiber membrane structure based on multiple interpenetrating networks of bidisperse magnetic particles, sodium alginate (SA), chitosan (CHI) in conjunction with polyethylene oxide hydrogels was prepared in a controllable electrospinning technology. Specifically, the morphology distributions of nanofibers could be regulated by the crosslinking degree of the interpenetrating networks and the spinning process parameters. The incorporation of SA and CHI endowed the sensor with desirable flexibility, ideal biocompatibility and skin-friendly property. Besides, the assembled sensors not only displayed preferable magnetic sensitivity of 0.34 T and reliable stability, but also exhibited favorable cross-sensitivity, quick response time, and long-term durability for over 5000 cycles under various mechanical stimuli. Importantly, the multi-mode stimuli could be discriminated via producing opposite electrical signals. Furthermore, based on the signal distinguishability of the sensor, a wearable Morse code translation system assisted by the machine learning algorithm was demonstrated, enabling a high recognizing accuracy (>99.1 %) for input letters and numbers information. Due to the excellent multifunctional sensing characteristics, we believe that the sensor will have a high potential in wearable soft electronics and human-machine interactions.
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Source |
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http://dx.doi.org/10.1016/j.ijbiomac.2024.130482 | DOI Listing |
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