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Intelligent MEMS Sensor Based on an Oxidized Medium-Entropy Alloy (FeCoNi) for H2 and CO Recognition.
Yan, Wenjun; Liu, Yun; Bai, Yan; Chen, Yulong; Zhou, Houpan; Ahmad, Waqar.
Affiliation
  • Yan W; School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Liu Y; Faculty of Information, Liaoning University, Shenyang 110036, China.
  • Bai Y; Faculty of Information, Liaoning University, Shenyang 110036, China.
  • Chen Y; Industrialization Center of Micro & Nano ICs and Devices, Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China.
  • Zhou H; School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Ahmad W; Department of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China.
ACS Appl Mater Interfaces ; 16(37): 49474-49483, 2024 Sep 18.
Article in En | MEDLINE | ID: mdl-39231248
ABSTRACT
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 H2/CO, the sensor shows great selectivity for H2 and CO at 150 °C of the integrated microheater in the MEMS device, with the theoretical detection limit of 0.3 ppm for H2 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 H2/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 H2 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 H2 and CO. This work provides insight for the development of low-power, high-performance MEMS gas sensors and further intelligence of individual MEMS sensors.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: ACS Appl Mater Interfaces Journal subject: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Year: 2024 Document type: Article Affiliation country: China Country of publication: Estados Unidos

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: ACS Appl Mater Interfaces Journal subject: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Year: 2024 Document type: Article Affiliation country: China Country of publication: Estados Unidos