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.


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.


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


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.