Single-Atom Tailoring of Two-Dimensional Atomic Crystals Enables Highly Efficient Detection and Pattern Recognition of Chemical Vapors.
ACS Sens
; 7(5): 1533-1543, 2022 May 27.
Article
em En
| MEDLINE
| ID: mdl-35546283
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, MoS2, and WSe2) 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-WSe2 SASs), and fast response/recovery time (11/58 s for Pt-Graphene and 22/48 s for Pt-MoS2 to 50 ppm ethanol, 9/57 s for Pt-Graphene and 15/75 s for Pt-MoS2 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.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
ACS Sens
Ano de publicação:
2022
Tipo de documento:
Article