Chemical Space Mapping for Multicomponent Gas Mixtures

22 February 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

In our manuscript, we present our protocol for data processing to mitigate the effects of interfering analytes on the identification of the chemical species detected by sensors. Considering NO2 and CO2, we designed electrochemical sensors whose response yielded the cyclic voltammetry data that we analyzed to classify single-species components and their mixtures using a data-driven approach to generate a chemical space where their mixtures can be deconvoluted.

Keywords

cyclic voltammetry
carbon dioxide (CO2)
nitrogen dioxide (NO2)
gas analytical system
machine learning
feature importance

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