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Application of Machine Learning-Based Regression Model to rGO-SnO2 Nanohybrid-Based Gas Sensor Analysis

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Abstract:

Designing a model that utilizes previously reported experimental data on graphene and metal oxide nanoparticle-based hybrids and nanocomposites to predict the gas sensor response can be a promising approach for developing innovative and effective gas sensors. In this work, experimental data were extracted from published reviews and research articles to build a dataset for training various machine learning (ML) models. The compiled dataset focuses on the rGO-SnO2 nanohybrid-based chemiresistive sensor and includes features such as gas concentration (ppm), operating temperature (°C), sensor response (%), response time (s), and recovery time (s). The sensor response and gas concentration were considered as target variables, one at a time. Several machine learning models, such as random forest regression (RFR), support vector regression (SVR), gradient boosting regression (GBR), and extreme gradient boosting regression (XGBR), were employed to predict target variables. Prediction accuracy was evaluated using the coefficient of determination (R² score), root mean squared error (RMSE), and mean absolute error (MAE). Among all the models, the XGBR ML model achieved the best performance, with a maximum R2 score (0.93) and minimum RMSE (0.52) and MAE (0.23) values when predicting gas concentration and a highest R2 score of 0.99 with RMSE and MAE values of 7.97 and 5.92 when predicting sensor response as the target variable. This study demonstrates the application of machine learning for the rational design of rGO-SnO2 nanohybrid-based NO2 gas sensors, supporting their potential use in various applications such as indoor and outdoor monitoring and industrial gas leakage detection.

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Nano Hybrids and Composites Vol. 49 View Preview

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Nano Hybrids and Composites (Volume 49)

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1-14

DOI:

https://doi.org/10.4028/p-8DZHcC

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Online since:

December 2025

Authors:

Monika Gupta*, Pradeep Kumar, Vipin Kumar

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Gas Sensor, Machine Learning, Prediction Efficiency, Regression Models, rGO-SnO2

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© 2025 Trans Tech Publications Ltd. All Rights Reserved

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