Synergistic Enhancement of Modified-PVDF Humidity Sensitivity via Chemical Adsorption-Ionic Conductivity and its Application in Intelligent Powered Air-Purifying Respirator

Abstract

This study proposes an intelligent powered air-purifying respirator with a superhydrophobic polyvinylidene fluoride/SiO₂ humidity-sensitive sensor to address heat and moisture accumulation and high breathing resistance in self-priming filter-type respirators. The humidity sensor, featuring a contact angle of 151°, exhibits a 30% resistance change between 75% and 95% RH. This change is driven by the synergistic effects of chemical adsorption of water molecules onto polar groups (F, H, O) and physical adsorption with capillary condensation. First-principles calculations reveal that the band gap decreases from 0.2782 eV to 0.1616 eV as humidity increases, enhancing electrical conductivity. The respirator dynamically adjusts fan speed, ranging from 5000 to 35 000 rpm, using a Gated Recurrent Unit neural network based on breath prediction, thereby achieving efficient gas exchange. Comparative experiments with self-aspirating filtering respirators demonstrate that the respirator effectively reduces internal mask temperature, lowers humidity to ambient levels, and maintains positive pressure inside the mask, significantly decreasing breathing resistance. Computational fluid dynamics simulations confirm the dynamic balance of airflow inside the mask, ensuring high ventilation efficiency. This design significantly enhances heat and moisture management and reduces breathing resistance, offering an advanced solution for addressing the challenges in self-priming filter-type respirators.

Keywords

humidity sensor / machine learning / powered air-purifying respirator

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2026 The Author(s). Energy & Environmental Materials published by John Wiley & Sons Australia, Ltd on behalf of Zhengzhou University.