Intelligent MEMS Sensor Based on an Oxidized Medium-Entropy Alloy (FeCoNi) for H2 and CO Recognition

被引:2
作者
Yan, Wenjun [1 ]
Liu, Yun [2 ]
Bai, Yan [2 ]
Chen, Yulong [5 ]
Zhou, Houpan [1 ]
Ahmad, Waqar [3 ,4 ]
机构
[1] Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310018, Peoples R China
[2] Liaoning Univ, Fac Informat, Shenyang 110036, Peoples R China
[3] Zhejiang Univ, Dept Chem & Biol Engn, Hangzhou 310027, Peoples R China
[4] Inst Zhejiang Univ Quzhou, Quzhou 324000, Peoples R China
[5] Shenzhen Technol Univ, Sino German Coll Intelligent Mfg, Industrializat Ctr Micro & Nano ICs & Devices, Shenzhen 518118, Peoples R China
基金
中国国家自然科学基金;
关键词
intelligent sensor; MEMS; medium-entropy alloy; low temperature; pulse heating mode; E-NOSE SYSTEM; GAS SENSORS; METAL; IDENTIFICATION; MIXTURES;
D O I
10.1021/acsami.4c07782
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
In this paper, we present the design and evaluation of an intelligent MEMS sensor employing the oxidized medium-entropy alloy (O-MEA) of FeCoNi as the gas-sensing material. Due to the specific catalytic exothermic reaction of the O-MEA on H-2/CO, the sensor shows great selectivity for H-2 and CO at 150 degrees C of the integrated microheater in the MEMS device, with the theoretical detection limit of 0.3 ppm for H-2 and 0.29 ppm for CO. The MEMS heater, capable of instantaneous temperature changes in pulse operation mode, offers a novel approach for thermal modulation of the sensor, which is crucial for the adsorption and reaction of H-2/CO molecules on the sensing layer surface. Consequently, we investigate the gas-sensing capabilities of the sensor under pulse heating modes (PHMs) of the MEMS heater and then propose the gas-sensing mechanism. The results indicate that PHMs significantly not only reduce the operating temperature and power consumption but also enhance the sensor's functionality by providing multivariable response signals, paving the way for intelligent gas detection. Based on the high selectivity to H-2 and CO, transforming the transient sensing curves into two-dimensional images via Gramian Angular Field (GAF) model and subsequent modeling using a convolutional neural network (CNN) algorithm, we have realized efficient and intelligent recognition of H-2 and CO. This work provides insight for the development of low-power, high-performance MEMS gas sensors and further intelligence of individual MEMS sensors.
引用
收藏
页码:49474 / 49483
页数:10
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