The band gap and mechanical control ability of two-dimensional materials largely determine the application value of two-dimensional devices in optical and electronic properties, so the bandgap controllability of two-dimensional materials broadens the application range of multi-functional devices. In the layered van der Waals (vdW) material AgInPS, the band gap can be adjusted by the number of layers and flexible strain, and the few layers AgInPShave discrete band gap values, which are also relevant for optoelectronic applications. In the strain range of up to 2.7% applied, the band gap can be adjusted, and the film is relatively stable under strain. We further analyzed the physical mechanism of flexible strain band gap regulation and found that strain-regulation reduced the band gap and increased the chemical bond length. These studies open up new opportunities for the future development of vdW material photoelectric resonators represented by AgInPS, and have important reference value.
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http://dx.doi.org/10.1088/1361-6528/acf6c5 | DOI Listing |
J Phys Chem Lett
January 2025
Key Laboratory of Physics and Technology for Advanced Batteries, Ministry of Education, College of Physics, Jilin University, Qianjin Street No. 2699, Changchun 130012, China.
Developing heavy-metal-free materials with wide tunable emission is important to light-emitters. The alloying method is utilized in ZnSe magic size clusters (MSCs) with Te to form ZnSeTe and manipulate the band gap structure in ZnSe. The growth of ZnTe on alloyed ZnSeTe quantum dots (QDs) forms ZnSeTe/ZnTe core/shell nanostructures, showing the tunable photoluminescence emission peak from 450 to 760 nm with the different thicknesses of ZnTe shell.
View Article and Find Full Text PDFNano Lett
January 2025
Department of Physics, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
Two-dimensional (2D) transition metal dichalcogenides (TMDs) have received significant interest for use in tunnel field-effect transistors (TFETs) due to their ultrathin layers and tunable band gap features. In this study, we used density functional theory (DFT) to investigate the electronic properties of six TMD heterostructures, namely, MoSe/HfS, MoTe/ZrS, MoTe/HfS, WSe/HfS, WTe/ZrS, and WTe/HfS, focusing on variations in band alignments. We demonstrate that WTe/ZrS and WTe/HfS have the smallest band gaps (close to 0 or broken) from the considered set.
View Article and Find Full Text PDFLangmuir
January 2025
ESYCOM, CNRS-UMR 9007, Université Gustave Eiffel, F-77454 Marne-la-Vallée, France.
This study investigates the synthesis, characterization, and functional properties of well-aligned zinc oxide (ZnO) nanowires (NWs) obtained by a two-step hydrothermal method. ZnO NWs were grown on silicon substrates precoated with a ZnO seed layer. The growth process was conducted at 90 °C for different durations (2, 3, and 4 h) to examine the time-dependent evolution of the nanowire properties.
View Article and Find Full Text PDFNanoscale
January 2025
Laboratory of Quantum Functional Materials Design and Application, School of Physics and Electronic Engineering, Jiangsu Normal University, Xuzhou 221116, China.
Two-dimensional materials with a combination of a moderate bandgap, highly anisotropic carrier mobility, and a planar structure are highly desirable for nanoelectronic devices. This study predicts a planar BeP monolayer with hexagonal symmetry that meets the aforementioned desirable criteria using the CALYPSO method and first-principles calculations. Calculations of electronic properties demonstrate that the hexagonal BeP monolayer is an intrinsic semiconductor with a direct band gap of approximately 0.
View Article and Find Full Text PDFNPJ Comput Mater
January 2025
School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut, Storrs, CT USA.
Machine learning has advanced the rapid prediction of inorganic materials properties, yet data scarcity for specific properties and capturing thermodynamic stability remains challenging. We propose a framework utilizing a Graph Neural Network with composition-based and crystal structure-based architectures, combined with a transfer learning scheme. This approach accurately predicts energy-related properties (e.
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