Strain engineering, efficient excitonic photoluminescence, and exciton funnelling in unmodified MoS2 nanosheets

Nanoscale ◽  
2017 ◽  
Vol 9 (43) ◽  
pp. 16602-16606 ◽  
Author(s):  
Vijay Saradhi Mangu ◽  
Marziyeh Zamiri ◽  
S. R. J. Brueck ◽  
Francesca Cavallo
Keyword(s):  
Band Structure ◽  
Electronic Band Structure ◽  
Strain Engineering ◽  
Mos2 Nanosheets ◽  
Electronic Band

The electronic band structure of unmodified multilayer MoS2 is manipulated via dry release in place of nanosheets on textured substrates.

scholarly journals Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Evgenii Tsymbalov ◽  
Zhe Shi ◽  
Ming Dao ◽  
Subra Suresh ◽  
Ju Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Band Structure ◽  
Physical Properties ◽  
Elastic Strain ◽  
Electronic Band Structure ◽  
Strain Engineering ◽  
Semiconductor Materials ◽  
Electronic Band ◽  
Effective Electron

AbstractThe controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials. With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond, employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit, through the design of an optimal straining pathway. Such simulations, however, call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties. Motivated by this challenge, we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k-space. These calculations enable us to identify ways in which the physical properties can be altered through “deep” elastic strain engineering up to a large fraction of the ideal strain. Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure. By training a surrogate model with ab initio computational data, our method can identify the most efficient strain energy pathway to realize physical property changes. The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass. We illustrate the applications of the method with specific results for diamonds, although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials.

The electronic band structure of silicon

Physica ◽  
1954 ◽  
Vol 3 (7-12) ◽  
pp. 967-970
Author(s):  
D JENKINS
Keyword(s):  
Band Structure ◽  
Electronic Band Structure ◽  
Electronic Band

THE ELECTRONIC BAND STRUCTURE OF V3Ga AND V3Si

1972 ◽  
Vol 33 (C3) ◽  
pp. C3-223-C3-233 ◽  
Author(s):  
I. B. GOLDBERG ◽  
M. WEGER
Keyword(s):  
Band Structure ◽  
Electronic Band Structure ◽  
Electronic Band

Ab initio calculation on the Optical Properties of AA- Stacked two dimensional Graphene, Silicene, Germanene, and Stanene

2018 ◽  
Vol 1 (1) ◽  
pp. 46-50
Author(s):  
Rita John ◽  
Benita Merlin
Keyword(s):  
Optical Properties ◽  
Band Structure ◽  
Density Functional ◽  
Electronic Band Structure ◽  
Complex Refractive Index ◽  
Single Layer ◽  
Generalized Gradient ◽  
Electronic Band ◽  
Wide Range ◽  
Optical Region

In this study, we have analyzed the electronic band structure and optical properties of AA-stacked bilayer graphene and its 2D analogues and compared the results with single layers. The calculations have been done using Density Functional Theory with Generalized Gradient Approximation as exchange correlation potential as in CASTEP. The study on electronic band structure shows the splitting of valence and conduction bands. A band gap of 0.342eV in graphene and an infinitesimally small gap in other 2D materials are generated. Similar to a single layer, AA-stacked bilayer materials also exhibit excellent optical properties throughout the optical region from infrared to ultraviolet. Optical properties are studied along both parallel (||) and perpendicular ( ) polarization directions. The complex dielectric function (ε) and the complex refractive index (N) are calculated. The calculated values of ε and N enable us to analyze optical absorption, reflectivity, conductivity, and the electron loss function. Inferences from the study of optical properties are presented. In general the optical properties are found to be enhanced compared to its corresponding single layer. The further study brings out greater inferences towards their direct application in the optical industry through a wide range of the optical spectrum.

scholarly journals Electronic Band Structure and Transport Properties of the Cluster Compound Ag3Tl2Mo15Se19

2019 ◽  
Vol 58 (9) ◽  
pp. 5533-5542 ◽  
Author(s):  
Patrick Gougeon ◽  
Philippe Gall ◽  
Rabih Al Rahal Al Orabi ◽  
Benoit Boucher ◽  
Bruno Fontaine ◽  
...  
Keyword(s):  
Band Structure ◽  
Transport Properties ◽  
Electronic Band Structure ◽  
Cluster Compound ◽  
Electronic Band
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