In Situ Synthesis of Ordered Macroporous Metal Oxides Monolayer on MEMS Chips: Toward Gas Sensor Arrays for Artificial Olfactory.

IF 12.1 2区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Small Pub Date : 2025-07-25 DOI:10.1002/smll.202503267
Liyuan Zhu,Keyu Chen,Chao Zeng,Qi Cao,Yonghui Deng,Kaiping Yuan
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引用次数: 0

Abstract

Metal oxide semiconductor gas sensors have attracted particular attention due to their merits of high sensitivity and easy integration. However, their insufficient selectivity severely limited their applications, especially in identifying low-concentration gases in a complex environment. Herein, inspired by the human olfaction, an in situ colloidal assembly strategy is developed to directly synthesize ordered macroporous metal oxide monolayers on micro-electromechanical system (MEMS) chips. It enables wafer-scale fabrication of gas sensors with excellent device-to-device consistency, which is beneficial for the construction of a highly reliable artificial olfactory system. In order to exploit efficient sensor arrays, five different monolayer macroporous gas sensitive materials with high specific surface areas, diverse nanostructures, rich catalytically active sites, and diverse compositions are synthesized in situ on MEMS chips, which displayed tailored selectivity and sensing behaviors. The crucial cross-selectivity contributes to the complex gas identification. Based on principal component analysis and back propagation neural network algorithm, an advanced artificial olfactory system is constructed, which can distinguish four different common hazardous gases with accurate concentrations, including hydrogen sulfide, carbon monoxide, acetone, and toluene. The proposed MEMS-based artificial olfactory system holds great promise to develop an electronic nose for detection of toxic gases in a complex environment.
MEMS芯片上有序大孔金属氧化物单层的原位合成:面向人工嗅觉的气体传感器阵列。
金属氧化物半导体气体传感器因其灵敏度高、易于集成等优点而备受关注。然而,它们的选择性不足严重限制了它们的应用,特别是在复杂环境中识别低浓度气体。本文受人类嗅觉的启发,提出了一种在微机电系统(MEMS)芯片上直接合成有序大孔金属氧化物单层的原位胶体组装策略。它使晶圆级制造具有优异设备间一致性的气体传感器成为可能,这有利于构建高度可靠的人工嗅觉系统。为了开发高效的传感器阵列,在MEMS芯片上原位合成了五种不同的单层大孔气敏材料,它们具有高比表面积、不同的纳米结构、丰富的催化活性位点和不同的成分,表现出量身定制的选择性和传感行为。关键的交叉选择性有助于复杂气体的识别。基于主成分分析和反向传播神经网络算法,构建了一种先进的人工嗅觉系统,能够准确识别硫化氢、一氧化碳、丙酮和甲苯四种不同的常见有害气体浓度。提出的基于mems的人工嗅觉系统为开发一种用于复杂环境中有毒气体检测的电子鼻提供了很大的希望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Small
Small 工程技术-材料科学:综合
CiteScore
17.70
自引率
3.80%
发文量
1830
审稿时长
2.1 months
期刊介绍: Small serves as an exceptional platform for both experimental and theoretical studies in fundamental and applied interdisciplinary research at the nano- and microscale. The journal offers a compelling mix of peer-reviewed Research Articles, Reviews, Perspectives, and Comments. With a remarkable 2022 Journal Impact Factor of 13.3 (Journal Citation Reports from Clarivate Analytics, 2023), Small remains among the top multidisciplinary journals, covering a wide range of topics at the interface of materials science, chemistry, physics, engineering, medicine, and biology. Small's readership includes biochemists, biologists, biomedical scientists, chemists, engineers, information technologists, materials scientists, physicists, and theoreticians alike.
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