|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM34310685X |
| 003 |
DE-627 |
| 005 |
20231226015647.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2022 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1002/adma.202202911
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1143.xml
|
| 035 |
|
|
|a (DE-627)NLM34310685X
|
| 035 |
|
|
|a (NLM)35790036
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Lu, Ang-Yu
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Unraveling the Correlation between Raman and Photoluminescence in Monolayer MoS2 through Machine-Learning Models
|
| 264 |
|
1 |
|c 2022
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 25.08.2022
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a © 2022 The Authors. Advanced Materials published by Wiley-VCH GmbH.
|
| 520 |
|
|
|a 2D transition metal dichalcogenides (TMDCs) with intense and tunable photoluminescence (PL) have opened up new opportunities for optoelectronic and photonic applications such as light-emitting diodes, photodetectors, and single-photon emitters. Among the standard characterization tools for 2D materials, Raman spectroscopy stands out as a fast and non-destructive technique capable of probing material's crystallinity and perturbations such as doping and strain. However, a comprehensive understanding of the correlation between photoluminescence and Raman spectra in monolayer MoS2 remains elusive due to its highly nonlinear nature. Here, the connections between PL signatures and Raman modes are systematically explored, providing comprehensive insights into the physical mechanisms correlating PL and Raman features. This study's analysis further disentangles the strain and doping contributions from the Raman spectra through machine-learning models. First, a dense convolutional network (DenseNet) to predict PL maps by spatial Raman maps is deployed. Moreover, a gradient boosted trees model (XGBoost) with Shapley additive explanation (SHAP) to bridge the impact of individual Raman features in PL features is applied. Last, a support vector machine (SVM) to project PL features on Raman frequencies is adopted. This work may serve as a methodology for applying machine learning to characterizations of 2D materials
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a 2D materials
|
| 650 |
|
4 |
|a Raman spectroscopy
|
| 650 |
|
4 |
|a machine learning
|
| 650 |
|
4 |
|a molybdenum disulfide
|
| 650 |
|
4 |
|a photoluminescence
|
| 700 |
1 |
|
|a Martins, Luiz Gustavo Pimenta
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Shen, Pin-Chun
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Chen, Zhantao
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Park, Ji-Hoon
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Xue, Mantian
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Han, Jinchi
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Mao, Nannan
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Chiu, Ming-Hui
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Palacios, Tomás
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Tung, Vincent
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Kong, Jing
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 34(2022), 34 vom: 21. Aug., Seite e2202911
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
|
| 773 |
1 |
8 |
|g volume:34
|g year:2022
|g number:34
|g day:21
|g month:08
|g pages:e2202911
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1002/adma.202202911
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 34
|j 2022
|e 34
|b 21
|c 08
|h e2202911
|