首页|Northeast Forestry University Reports Findings in Artificial Intelligence [Lignosulfonate-doped polyaniline-reinforced poly(vinyl alcohol) hydrogels as hig hly sensitive, antimicrobial, and UV-resistant multifunctional sensors]
Northeast Forestry University Reports Findings in Artificial Intelligence [Lignosulfonate-doped polyaniline-reinforced poly(vinyl alcohol) hydrogels as hig hly sensitive, antimicrobial, and UV-resistant multifunctional sensors]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Heilongjiang, Peo ple's Republic of China, by NewsRx journalists, research stated, "Flexible weara ble strain sensors exist the advantages of high resolution, lightweight, wide me asurement range, which have unlimited potential in fields such as soft robotics, electronic skin, and artificial intelligence. However, current flexible sensors based on hydrogels still have some defects, including poor mechanical propertie s, self-adhesive properties and bacteriostatic properties." The news correspondents obtained a quote from the research from Northeast Forest ry University, "In this study, A conductive hydrogel Sodium Ligninsulfonate (LGS )@PANI-Ag-poly(vinyl alcohol) (PVA) hydrogels consisting of lignosulfonate-doped polyaniline (LGS@PANI), silver nitrate, and PVA interactions were designed and prepared for sensing applications. Here, the abundant reactive functional groups of lignosulfonates not only improve the electrochemical and electrical conducti vity of polyaniline, thereby increasing its potential for sensing and capacitor applications, but also provide excellent mechanical properties (0.71 MPa), self- adhesion (81.27 J/m) and ultraviolet (UV) resistance (UV inhibition close to 100 %) to the hydrogel. In addition, the hydrogel exhibited rich multi functional properties, including tensile strain resistance (up to 397 % ), antimicrobial properties (up to 100 % inhibition of Escherichia coli and Staphylococcus aureus), high sensitivity (gauge factor, GF = 4.18), and a rapid response time (100 ms )."
HeilongjiangPeople's Republic of ChinaAsiaArtificial IntelligenceEmerging TechnologiesMachine Learning
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