Au–Pt Co-Modified PrFeO3 for Enhanced CO2 Sensing with Support Vector Regression-Assisted Calibration

Improving chemiresistive CO2 sensing in real gas samples requires advances in both material response and signal calibration. Here, Au–Pt co-modified PrFeO3 (Au–Pt/PFO) was prepared by a citric-acid-assisted sol–gel method and evaluated against PFO, Au/PFO, and Pt/PFO to examine the effects of noble-metal modification on CO2 sensing. Au–Pt/PFO exhibited a specific surface area of 16.26 m2/g and an altered surface oxygen chemical environment. At 190 °C, its response (Rg/Ra) to 1000 ppm CO2 reached 9.68, corresponding to 2.61 times that of PFO, with response and recovery times of 22.38 and 16.37 s, respectively. During a 31-day test, it retained 99.38% of its initial response on Day 31. The sensing behavior was interpreted in terms of surface adsorption and interfacial charge modulation using characterization and electrical measurements. Seven features extracted from the response–recovery curves were subsequently used to construct a support vector regression (SVR)-assisted calibration model. Spectroscopic measurements served as references for evaluating CO2 concentration predictions in respiratory gas samples from imbibed rice seeds. Across separate groups comprising two gas-sample batches per method, the mean absolute relative errors were 97.55% for conventional response-based calibration and 33.07% for SVR-assisted calibration, corresponding to a relative reduction of 66.10%. These results demonstrate enhanced CO2 response through Au–Pt co-modification and improved concentration prediction in the investigated real gas samples through multiparameter-assisted calibration.

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Publication Details

Journal
Micromachines
Published
2026-10-29
DOI
https://doi.org/10.3390/mi17111241
Primary Topic
Gas Sensing Nanomaterials and Sensors
Type
article
Field-Weighted Citation Impact
0.00

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article

Au–Pt Co-Modified PrFeO3 for Enhanced CO2 Sensing with Support Vector Regression-Assisted Calibration

Yang Liu, Zhengdao Li, Heng Zhang, Yongshuo Zhang et al.
2 links
Micromachines
Gas Sensing Nanomaterials and Sensors
article

Au–Pt Co-Modified PrFeO3 for Enhanced CO2 Sensing with Support Vector Regression-Assisted Calibration

Yang Liu, Zhengdao Li, Heng Zhang, Yongshuo Zhang, Feiyong Chen
article en

Abstract

Improving chemiresistive CO2 sensing in real gas samples requires advances in both material response and signal calibration. Here, Au–Pt co-modified PrFeO3 (Au–Pt/PFO) was prepared by a citric-acid-assisted sol–gel method and evaluated against PFO, Au/PFO, and Pt/PFO to examine the effects of noble-metal modification on CO2 sensing. Au–Pt/PFO exhibited a specific surface area of 16.26 m2/g and an altered surface oxygen chemical environment. At 190 °C, its response (Rg/Ra) to 1000 ppm CO2 reached 9.68, corresponding to 2.61 times that of PFO, with response and recovery times of 22.38 and 16.37 s, respectively. During a 31-day test, it retained 99.38% of its initial response on Day 31. The sensing behavior was interpreted in terms of surface adsorption and interfacial charge modulation using characterization and electrical measurements. Seven features extracted from the response–recovery curves were subsequently used to construct a support vector regression (SVR)-assisted calibration model. Spectroscopic measurements served as references for evaluating CO2 concentration predictions in respiratory gas samples from imbibed rice seeds. Across separate groups comprising two gas-sample batches per method, the mean absolute relative errors were 97.55% for conventional response-based calibration and 33.07% for SVR-assisted calibration, corresponding to a relative reduction of 66.10%. These results demonstrate enhanced CO2 response through Au–Pt co-modification and improved concentration prediction in the investigated real gas samples through multiparameter-assisted calibration.

MicromachinesVol. 17(11)
Shandong Jianzhu University (CN)
Shandong Jianzhu University
Openalex Percentile: Top 24%
Gas Sensing Nanomaterials and Sensors
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Au–Pt Co-Modified PrFeO3 for Enhanced CO2 Sensing with Support Vector Regression-Assisted Calibration — Yang Liu, Zhengdao Li, et al. · Micromachines (2026) | TGRS Research Map | TGRS