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一种由硫化铜量子点/硫化铋纳米片构建的新型类人工神经元气体传感器。

A Novel Artificial Neuron-Like Gas Sensor Constructed from CuS Quantum Dots/BiS Nanosheets.

作者信息

Chen Xinwei, Wang Tao, Shi Jia, Lv Wen, Han Yutong, Zeng Min, Yang Jianhua, Hu Nantao, Su Yanjie, Wei Hao, Zhou Zhihua, Yang Zhi, Zhang Yafei

机构信息

Key Laboratory of Thin Film and Microfabrication (Ministry of Education), Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.

出版信息

Nanomicro Lett. 2021 Dec 2;14(1):8. doi: 10.1007/s40820-021-00740-1.

Abstract

Real-time rapid detection of toxic gases at room temperature is particularly important for public health and environmental monitoring. Gas sensors based on conventional bulk materials often suffer from their poor surface-sensitive sites, leading to a very low gas adsorption ability. Moreover, the charge transportation efficiency is usually inhibited by the low defect density of surface-sensitive area than that in the interior. In this work, a gas sensing structure model based on CuS quantum dots/BiS nanosheets (CuS QDs/BiS NSs) inspired by artificial neuron network is constructed. Simulation analysis by density functional calculation revealed that CuS QDs and BiS NSs can be used as the main adsorption sites and charge transport pathways, respectively. Thus, the high-sensitivity sensing of NO can be realized by designing the artificial neuron-like sensor. The experimental results showed that the CuS QDs with a size of about 8 nm are highly adsorbable, which can enhance the NO sensitivity due to the rich sensitive sites and quantum size effect. The BiS NSs can be used as a charge transfer network channel to achieve efficient charge collection and transmission. The neuron-like sensor that simulates biological smell shows a significantly enhanced response value (3.4), excellent responsiveness (18 s) and recovery rate (338 s), low theoretical detection limit of 78 ppb, and excellent selectivity for NO. Furthermore, the developed wearable device can also realize the visual detection of NO through real-time signal changes.

摘要

室温下对有毒气体进行实时快速检测对于公共卫生和环境监测尤为重要。基于传统块状材料的气体传感器往往因其表面敏感位点较少,导致气体吸附能力非常低。此外,与内部相比,表面敏感区域的低缺陷密度通常会抑制电荷传输效率。在这项工作中,构建了一种受人工神经网络启发的基于硫化铜量子点/硫化铋纳米片(CuS QDs/BiS NSs)的气敏结构模型。通过密度泛函计算的模拟分析表明,CuS QDs和BiS NSs可分别用作主要吸附位点和电荷传输途径。因此,通过设计类人工神经元传感器可以实现对NO的高灵敏度传感。实验结果表明,尺寸约为8nm的CuS QDs具有高度吸附性,由于丰富的敏感位点和量子尺寸效应,可提高对NO的灵敏度。BiS NSs可作为电荷转移网络通道,实现高效的电荷收集和传输。模拟生物嗅觉的类神经元传感器表现出显著增强的响应值(3.4)、优异的响应性(18s)和恢复率(338s)、78ppb的低理论检测限以及对NO的优异选择性。此外,所开发的可穿戴设备还可以通过实时信号变化实现对NO的可视化检测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/19ae/8639894/2eb1b769d76b/40820_2021_740_Fig1_HTML.jpg

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