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Flow-assisted Dielectrophoresis: A Low Cost Method for the Fabrication of High Performance Solution-processable Nanowire Devices
Published on: December 7, 2017
Electronic properties of semiconductor nanowires
L C Lew Yan Voon1, Yong Zhang, B Lassen
1Department of Physics, Wright State University 3640 Colonel Glenn Hwy, Dayton, Ohio 45435, USA.
Journal of Nanoscience and Nanotechnology
|May 13, 2008
Summary
This review compares electronic-structure calculation methods for semiconductor nanowires. It highlights how these calculations predict crucial properties like band gaps and polarization anisotropy.
Area of Science:
- Materials Science
- Condensed Matter Physics
- Computational Chemistry
Background:
- Semiconductor nanowires are crucial for advanced electronic and optoelectronic devices.
- Accurate theoretical modeling is essential for understanding and designing nanowire properties.
- Various computational methods exist, each with strengths and limitations.
Purpose of the Study:
- To review and compare state-of-the-art electronic-structure calculation methods for semiconductor nanowires.
- To analyze results from empirical k.p, empirical tight-binding, semi-empirical pseudopotential, and ab initio approaches.
- To connect theoretical predictions with experimental data, focusing on band gaps and polarization anisotropy.
Main Methods:
- Comparative analysis of established computational techniques.
- Focus on free-standing plain and modulated nanowire models.
- Integration of synthesis methods for a complete overview.
Main Results:
- Electronic properties are highly dependent on the chosen calculation method.
- Theoretical predictions for band gaps and polarization anisotropy show good agreement with experimental findings.
- The review provides a consolidated view of computational approaches.
Conclusions:
- The choice of electronic-structure calculation method significantly impacts predicted nanowire properties.
- Computational methods are vital for guiding experimental efforts in nanowire research.
- Further refinement of theoretical models can lead to enhanced device performance.
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