Integrated Lithium Battery-Powered High-Field Asymmetric Ion Mobility Spectrometer (FAIMS) for Molecular Structure Fingerprinting and Deep Learning

Abstract

A portable high-field asymmetric ion transfer spectrometer (FAIMS) was miniaturized to improve the design. The prototype consists of a main controller, a galvanometer, a high-voltage square-wave power supply, a Raspberry Pi module, and integrated circuitry. The new design eliminates a main power supply and meets the high- and low-voltage requirements. Qt, a cross-platform software package for creating graphical user interfaces, was used to map the molecular structure fingerprint. The timing, circuit structure, and mechanical assembly were optimized. The dimensions were 45 cm × 48 cm × 23 cm. 16 s were required to collect 1000 data points; the usage time exceeded 8 h, and the background noise was approximately 0.2 pA. The prototype offers excellent efficiency, stability, and multiple applications. The accuracy of FAIMS with the deep-learning model for data acquisition is good. This system is anticipated to evaluate the concentrations of volatiles in diverse types of samples.

Keywords:

• Deep learning
• fingerprint
• high-field asymmetric ion mobility spectrometry (FAIMS)

Declaration of conflicting interests

The authors report there are no competing interests to declare.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China by Grants 62163009 and 61864001; the Natural Science Foundation of GuangXi by Grant 2021JJD170019; the Foundation of Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (Guilin University of Electronic Technology) by Grant YQ21111; the Basic Ability Enhancement Program for Young and Middle-Aged Teachers of Guangxi by Grant 2022KY0206; and the College Students’ Innovation And Entrepreneurship Training Program by Grant S202210595272.