Abstract
The ability to detect gas molecule and assign a concentration offers an inventive solution in the field of plasma integrated with machine learning. The most important finding of this work is firstly, to develop an algorithm for gas-molecule identification using three different hydrocarbons (CH4, C2H2, C2H6) and secondly, organize a model for detecting gas concentration (classification). For this reason, initially eight different gases evaluated. The study confirms the present of the unique emission lines as a gas indicator, i.e., a wavelength peak related to hydrocarbons identified via increasing in C x H y concentration. By means of unique variable important in projection, hydrocarbons can be distinguished. Our proposed Chemometric analysis strategy examined on >1000 samples and results development of suitable techniques that are sufficiently rapid, accurate and innovative. This demonstrates the potential for real-time, portable, and continuous monitoring of trace gases with potential applications in medical, environmental, and industrial gas sensing.
| Original language | English |
|---|---|
| Article number | 345202 |
| Pages (from-to) | 1-11 |
| Number of pages | 11 |
| Journal | Journal of Physics D: Applied Physics |
| Volume | 57 |
| Issue number | 34 |
| Early online date | 4 Jun 2024 |
| DOIs | |
| Publication status | Published online - 4 Jun 2024 |
Bibliographical note
Publisher Copyright:© 2024 The Author(s). Published by IOP Publishing Ltd.
Data Access Statement
The data cannot be made publicly available upon publication because they are not available in a format that is sufficiently accessible or reusable by other researchers. The data that support the findings of this study are available upon reasonable request from the authors.Keywords
- methane identification
- optical emission spectroscopy (OES)
- variable importance in projection (VIP)
- unique VIP
- partial least square discriminant analysis
- hydrocarbons
- classification