Toward Healthcare Diagnoses by Machine-Learning-Enabled Volatile Organic Compound Identification

Abstract

As a natural monitor of health conditions for human beings, volatile organic compounds (VOCs) act as significant biomarkers for healthcare monitoring and early stage diagnosis of diseases. Most existing VOC sensors use semiconductors, optics, and electrochemistry, which are only capable of measuring the total concentration of VOCs with slow response, resulting in the lack of selectivity and low efficiency for VOC detection. Infrared (IR) spectroscopy technology provides an effective solution to detect chemical structures of VOC molecules by absorption fingerprints induced by the signature vibration of chemical stretches. However, traditional IR spectroscopy for VOC detection is limited by the weak light-matter interaction, resulting in large optical paths. Leveraging the ultrahigh electric field induced by plasma, the vibration of the molecules is enhanced to improve the light-matter interaction. Herein, we report a plasma-enhanced IR absorption spectroscopy with advantages of fast response, accurate quantization, and good selectivity. An order of ∼kV voltage was achieved from the multiswitched manipulation of the triboelectric nanogenerator by repeated sliding. The VOC species and their concentrations were well-quantified from the wavelength and intensity of spectra signals with the enhancement from plasma. Furthermore, machine learning has visualized the relationship of different VOCs in the mixture, which demonstrated the feasibility of the VOC identification to mimic patients.