• [Oral Presentation]Electroactive Poly(lactic acid) Nanofibrous Protective Membrane for Deep Learning-Assisted Respiratory Disease Diagnosis
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    [Oral Presentation]Electroactive Poly(lactic acid) Nanofibrous Protective Membrane for Deep Learning-Assisted Respiratory Disease Diagnosis

    Electroactive Poly(lactic acid) Nanofibrous Protective Membrane for Deep Learning-Assisted Respiratory Disease Diagnosis
    ID:197 Submission ID:344 View Protection:ATTENDEE Updated Time:2024-05-19 20:07:57 Hits:186 Oral Presentation

    Start Time:2024-05-30 19:20 (Asia/Shanghai)

    Duration:10min

    Session:[S2] Safety Engineering and Occupational Health ? [S2-4B] Evening of May 30th-4B

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    Abstract
    The low electroactivity and poor electret properties of PLA fibrous membranes have largely impeded a broader utilization of biodegradable and ecofriendly PLA materials in the field of long-term respiratory protection. Here, an electroactive PLA nanofibrous protective membrane was disclosed by anchoring the interfacial enhancer ZnO@PDA to the PLA fiber surface via electrospinning-electrospray strategy. Benefiting from the enhanced surface area and improved interfacial polarization, PLA/ZnO@PDA nanofibrous membranes exhibited superior electret property (surface potential of up to 9.5 kV) and high filtration performance (99.45% for PM0.3) while providing a desirable air resistance (94.1 Pa at 32 L/min). Notably, the improvement in triboelectric properties (output voltage of 11.5 V at 10 N, 0.5 Hz) extends the application of PLA/ZnO@PDA nanofibrous membranes in self-powered respiratory monitoring. With the assistance of deep learning, respiratory pattern recognition and disease diagnosis were realized with an accuracy up to 100%. The combination of artificial intelligence and self-powered health monitoring based on electroactive nanofibrous membranes opens new avenue for early disease diagnosis.
     
    Keywords
    respiratory protection,biodegradable nanofibers,respiratory monitoring
    Speaker
    Jiaqi Li
    China University of Mining and Technology

    Submission Author
    佳琪 李 中國礦業大學
    歡 徐 中國礦業大學
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