金属氧化物半导体气体传感器对农业甲烷排放的适用性评估

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY Information Processing in Agriculture Pub Date : 2023-11-01 DOI:10.1016/j.inpa.2023.11.001
Bastiaan Molleman, Enrico Alessi, Fabio Passaniti, Karen Daly
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引用次数: 0

摘要

本文研究了金属氧化物半导体(MOS)气体传感器用于甲烷环境监测的潜力。校准在实验室的受控条件下进行,在现场的半受控条件下进行,使用改进的头部空间室设置。测试的甲烷浓度高达±300 ppm。传感器电导与甲烷浓度之间的关系可以用吸附理论的原理很好地描述。可调参数为背景电导G0、灵敏度常数S和非理想系数n,其中n为0 ~ 1之间的无理数。在干燥空气中,传感器的行为与潮湿空气中有很大的不同,背景电导增加了大约10倍,灵敏度下降了20到80倍,而非理想系数从±0.4增加到±0.6。然而,在高甲烷浓度下,在干燥和潮湿空气中记录的电导值相当。预测值的标准差为1.6 μS。对于描述最少的数据集。使用相应的校准曲线,计算出潮湿环境空气的检测限为11ppm。这一数值表明,MOS传感器具有足够的灵敏度,可用于农业环境中的甲烷检测。
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Evaluation of the applicability of a metal oxide semiconductor gas sensor for methane emissions from agriculture
This work investigated the potential of metal oxide semiconductor (MOS) gas sensors for environmental monitoring of methane. Calibrations were performed under controlled conditions in the lab, and under semi-controlled conditions in the field, using a modified head space chamber set-up. Concentrations up to ±300 ppm methane were tested. The relationship between sensor conductance and methane concentrations could be very well described using principles from adsorption theory. The adjustable parameters were background conductance G0, a sensitivity constant S and a non-ideality coefficient n, where n has a non-rational value between 0 and 1. Sensor behaviour was very different in dry air than in humid air, with the background conductance increasing approximately tenfold and sensitivity decreasing between 20 fold and 80 fold, while the non-ideality coefficient increased from ±0.4 to ±0.6. Nevertheless, at high methane concentrations comparable conductance values were recorded in dry and humid air. The standard deviation of predicted values was 1.6 μS.for the least well described dataset. Using the corresponding calibration curve, a detection limit of 11 ppm is calculated for humid ambient air. This values suggests that MOS sensor are adequately sensitive to be used for methane detection in an agricultural context.
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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