A facile method for precise layer number identification of two-dimensional materials through optical images

Lei, Jiayu; Qu, Jiafan; Wang, Peng; Hu, Jiang; Shi, Hongyan; Sun, Xiudong; Gao, Bo

doi:10.1016/j.optcom.2019.02.003

Cited by 7 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the contrast changes in the optical image one can conclude that the WSe 2 nanosheets are made of a few atomic layers. [40] Fig. 2b displays the Raman spectrum of few-layer WSe 2 from the black dot indicated in Fig.…”

Section: Resultsmentioning

confidence: 99%

Mixed-dimensional Heterojunction by 3D CdS Nanowire Arrays Bridged with 2D WSe2 for Ultrafast Photoelectric Gas Sensor

Zhang,

Pinna,

Liu

et al. 2024

Preprint

View full text Add to dashboard Cite

Heterojunctions are of essential importance for electronic sensors due to their unique properties at the junctions. However, a planar junction made of two-dimensional (2D) materials commonly suffers from slow response and irreversible recovery because of slow physisorption and desorption rates. Herein, we present a unique design of a mixed-dimensional heterojunction built from patterned growth of 3D n-type CdS nanowire arrays and p-type 2D WSe2 nanosheets for photoelectric gas sensor. This heterojunction sensor showed highly selective and reversible response to NO2 and NH3 with detection limits of 60 and 54 ppb, respectively, under UV illumination at room temperature. Notably, the sensor exhibited ultrafast response time of less than 1s to 1 ppm NO2 and NH3, which outperforms most previous reports on NO2 and NH3 detection at room temperature. The outstanding sensing performance are attributed to the tuning of the Schottky barrier at the CdS/WSe2 heterojunction through the gas adsorption/desorption under UV excitation. The hybrid junction structure proposed herein will pave the way to engineer new electronic devices from a broad selection of materials to achieve improved sensing performances at room temperature.

show abstract

Section: Resultsmentioning

confidence: 99%

Mixed-dimensional Heterojunction by 3D CdS Nanowire Arrays Bridged with 2D WSe2 for Ultrafast Photoelectric Gas Sensor

Zhang,

Pinna,

Liu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The study provides a method to obtain various charming shapes of the focusing intensity which may determine the trapping effects. At the same time, it may be widely used in optical microscopy [16][17][18][19], imaging [20][21][22], material processing [23,24] and other fields [25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Tight focusing of high-order polarized anomalous vortex beam

Xu¹,

Qin²,

Ji³

et al. 2022

Laser Phys.

View full text Add to dashboard Cite

Based on the vector Debye theory, the tight focusing properties of a high-order polarized anomalous vortex (HPAV) beam are studied. The corresponding mathematical expressions of the HPAV beam are derived theoretically. We accomplish the inner and outer gear shapes of the focusing intensity where the number of the gear tooth can be modulated by polarization order. The results show that the focusing gear intensity can be flexibly modulated by initial polarization azimuth which may determine the trapping effects. Various charming focusing field patterns can be used to capture two kinds of different refractive indices particles simultaneously. The compactness of the intensity distribution can be freely adjusted by the HPAV beam topological charges and polarization order. The focal spot size, which is far beyond the Rayleigh diffraction limitation can be achieved. It may be expected to have potential applications in optical microscopy, imaging, optical telecommunication and other fields.

show abstract

“…Therefore, they were able to bypass the problem of condition variation and identify the layer number of 2D material. This work was applied to MoS 2 , WS 2 , and WSe 2 layers 29 .…”

mentioning

confidence: 99%

Toward automated classification of monolayer versus few-layer nanomaterials using texture analysis and neural networks

Aleithan

Mahmoud‐Ghoneim

2020

Sci Rep

View full text Add to dashboard Cite

The need for a fast and robust method to characterize nanostructure thickness is growing due to the tremendous number of experiments and their associated applications. By automatically analyzing the microscopic image texture of MoS2 and WS2, it was possible to distinguish monolayer from few-layer nanostructures with high accuracy for both materials. Three methods of texture analysis (TA) were used: grey level histogram (GLH), grey levels co-occurrence matrix (GLCOM), and run-length matrix (RLM), which correspond to first, second, and higher-order statistical methods, respectively. The best discriminating features were automatically selected using the Fisher coefficient, for each method, and used as a base for classification. Two classifiers were used: artificial neural networks (ANN), and linear discriminant analysis (LDA). RLM with ANN was found to give high classification accuracy, which was 89% and 95% for MoS2 and WS2, respectively. The result of this work suggests that RLM, as a higher-order TA method, associated with an ANN classifier has a better ability to quantify and characterize the microscopic structure of nanolayers, and, therefore, categorize thickness to the proper class.

show abstract

A facile method for precise layer number identification of two-dimensional materials through optical images

Cited by 7 publications

References 31 publications

Mixed-dimensional Heterojunction by 3D CdS Nanowire Arrays Bridged with 2D WSe2 for Ultrafast Photoelectric Gas Sensor

Mixed-dimensional Heterojunction by 3D CdS Nanowire Arrays Bridged with 2D WSe2 for Ultrafast Photoelectric Gas Sensor

Tight focusing of high-order polarized anomalous vortex beam

Toward automated classification of monolayer versus few-layer nanomaterials using texture analysis and neural networks

Contact Info

Product

Resources

About