Suppr超能文献

二维原子晶体的单原子剪裁实现化学蒸汽的高效检测与模式识别

Single-Atom Tailoring of Two-Dimensional Atomic Crystals Enables Highly Efficient Detection and Pattern Recognition of Chemical Vapors.

作者信息

Liu Bingqian, Zhu Qin, Pan Yanghang, Huang Futao, Tang Lingyu, Liu Cheng, Cheng Zheng, Wang Peng, Ma Jing, Ding Mengning

机构信息

Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China.

Institute of Theoretical and Computational Chemistry, Nanjing University, Nanjing 210023, People's Republic of China.

出版信息

ACS Sens. 2022 May 27;7(5):1533-1543. doi: 10.1021/acssensors.2c00356. Epub 2022 May 11.

Abstract

Low-dimensional semiconductor materials, such as single-walled carbon nanotubes, two-dimensional (2D) atomic crystals, and organic frameworks, have been widely adapted as ideal platforms to construct various chemo/biosensors with satisfying sensitivity. However, the general drawbacks in chemiresistive devices, including high operation temperatures, low response to low-polarity molecules, and poor selectivity, have limited their real-world applications. In this study, 2D materials (graphene, MoS, and WSe) were systematically functionalized with series of monodispersed single atomic sites (Pt, Co, and Ru) through a facile approach to construct single-atom sensors (SASs) for the detection of VOCs at room temperature. The structural and catalytic characteristics of SAs successfully translated into enhanced gas-sensing performance, with a 1-2 orders of magnitude increase in relative response to ethanol (@5 ppm) and acetone (@20 ppm) vapors (in all M-2D SASs as compared to pristine substrates), high selectivity to VOCs against relative humidity (M-WSe SASs), and fast response/recovery time (11/58 s for Pt-Graphene and 22/48 s for Pt-MoS to 50 ppm ethanol, 9/57 s for Pt-Graphene and 15/75 s for Pt-MoS to 200 ppm acetone) that are several times faster than the pristine 2D materials. Density functional theory (DFT) calculations revealed the signaling mechanism in SASs, and the data were further trained to build machine learning (ML) models for predicting the adsorption energies and sensing performance using the features of adsorption heights, metal charge, and charge transfer between the adsorbed VOCs and SASs sites. Finally, the rich combination of the metal single atoms and 2D atomic crystal supports were converted to cross-sensitive SA sensor array that allows for detection and identification of different VOCs.

摘要

低维半导体材料,如单壁碳纳米管、二维(2D)原子晶体和有机框架,已被广泛用作理想平台,以构建具有令人满意灵敏度的各种化学/生物传感器。然而,化学电阻式器件的一般缺点,包括高工作温度、对低极性分子的低响应以及差的选择性,限制了它们的实际应用。在本研究中,通过一种简便的方法,用一系列单分散的单原子位点(Pt、Co和Ru)对二维材料(石墨烯、MoS和WSe)进行系统功能化,以构建用于在室温下检测挥发性有机化合物(VOCs)的单原子传感器(SASs)。单原子的结构和催化特性成功转化为增强的气敏性能,对乙醇(@5 ppm)和丙酮(@20 ppm)蒸汽的相对响应增加了1-2个数量级(与原始基板相比,在所有M-2D SASs中),对VOCs相对于相对湿度具有高选择性(M-WSe SASs),以及快速的响应/恢复时间(Pt-石墨烯对50 ppm乙醇为11/58 s,Pt-MoS对50 ppm乙醇为22/48 s,Pt-石墨烯对200 ppm丙酮为9/57 s,Pt-MoS对200 ppm丙酮为15/75 s),比原始二维材料快几倍。密度泛函理论(DFT)计算揭示了SASs中的信号传导机制,并对数据进行进一步训练以建立机器学习(ML)模型,用于使用吸附高度、金属电荷以及吸附的VOCs与SASs位点之间的电荷转移等特征来预测吸附能和气敏性能。最后,金属单原子和二维原子晶体载体的丰富组合被转化为交叉敏感的SA传感器阵列,可用于检测和识别不同的VOCs。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验