ArticleMicromachines2019
A Gas Mixture Prediction Model Based on the Dynamic Response of a Metal-Oxide Sensor.
Article in Micromachines, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Enhance Ethanol Sensing Performance of Fe-Doped Tetragonal SnOSensors (Basel, Switzerland) · 2024Article
- A Gas Sensors Detection System for Real-Time Monitoring of Changes in Volatile Organic Compounds during Oolong Tea Processing.Foods (Basel, Switzerland) · 2024Article
- Multi-Channel MEMS-FAIMS Gas Sensor for VOCs Detection.Micromachines · 2023Article
- A novel ethanol gas sensor based on α-BiRSC advances · 2020Article
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Authors and funding
3 authors.
Funding
Abstract
Metal-oxide (MOX) gas sensors are widely used for gas concentration estimation and gas identification due to their low cost, high sensitivity, and stability. However, MOX sensors have low selectivity to different gases, which leads to the problem of classification for mixtures and pure gases. In this study, a square wave was applied as the heater waveform to generate a dynamic response on the sensor. The information of the dynamic response, which includes different characteristics for different gases due to temperature changes, enhanced the selectivity of the MOX sensor. Moreover, a polynomial interaction term mixture model with a dynamic response is proposed to predict the concentration of the binary mixtures and pure gases. The proposed method improved the classification accuracy to 100%. Moreover, the relative error of quantification decreased to 1.4% for pure gases and 13.0% for mixtures.
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Registered trials
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