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Condensed Matter > Materials Science

Title: Multiscale statistical quantum transport in porous media and random alloys with vacancies

Abstract: We have developed a multi-scale self-consistent method to study the charge conductivity of a porous system or a metallic matrix alloyed by randomly distributed nonmetallic grains and vacancies by incorporating Schr\"{o}dinger's equation and Poisson's equation. To account for the random distribution of the nonmetallic grains and clusters within the alloy system, we have used an uncorrelated white-noise Monte-Carlo sampling to generate numerous random alloys and statistically evaluate the charge conductance. We have performed a parametric study and investigated various electrical aspects of random porous and alloy systems as a function of the inherent parameters and density of the random grains. Our results find that the charge conductance within the low-voltage regime shows a highly nonlinear behavior against voltage variations in stark contrast to the high-voltage regime where the charge conductance is constant. The former finding is a direct consequence of the quantum scattering processes. The results reveal the threshold to the experimentally observable quantities, e.g., voltage difference, so that the charge current is activated for values larger than the threshold. The numerical study determines the threshold of one quantity as a function of the remaining quantities. Our method and results can serve to guide future experiments in designing circuital elements, involving this type of random alloy system.
Comments: 10 pages, 8 figures
Subjects: Materials Science (cond-mat.mtrl-sci); Disordered Systems and Neural Networks (cond-mat.dis-nn); Mesoscale and Nanoscale Physics (cond-mat.mes-hall); Statistical Mechanics (cond-mat.stat-mech); Quantum Physics (quant-ph)
Journal reference: J. Appl. Phys. 133, 035102 (2023)
DOI: 10.1063/5.0130917
Cite as: arXiv:2301.07569 [cond-mat.mtrl-sci]
  (or arXiv:2301.07569v2 [cond-mat.mtrl-sci] for this version)

Submission history

From: Mohammad Alidoust [view email]
[v1] Wed, 18 Jan 2023 14:40:52 GMT (2966kb,D)
[v2] Wed, 15 Feb 2023 13:31:25 GMT (2966kb,D)

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