Interdisciplinary Fundamental Concepts in STEM: Solid State Physics and COVID-19 Pandemic Evolution

Michael Shur
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Abstract

The rapid development of computer-aided design tools, such as MATLAB or Octave, or Mathematica enabled students to solve many complicated problems focusing less on underlying STEM-related concepts that are interdisciplinary. Therefore, it is important to demonstrate how it could be done using specific examples that could be linked to different subjects or even to their everyday life experience. This paper reports on using the COVID-19 pandemic evolution model (Shur, 2022) in my class on the physics of advanced semiconductors devices. I use this model to show how the concepts, such as the Born-Oppenheimer approximation and Fermi-Dirac distribution function could be used in a completely different STEM field. In solid-state physics, the Born-Oppenheimer approximation is used to separate rapid electronic motion, relevant to the electronic states and much slower nuclei motion (since nuclei are thousands of times heavier than electrons). Likewise, the COVID-19 model uses a relatively fast pandemic evolution growth or decay constant, a slow function of time itself. In solid-state physics, the Fermi-Dirac distribution function describes the transition from the occupied electronic states to empty electron states with the temperature determining the transition interval. The COVID-19 model uses the generalized Fermi-distribution function to describe the mitigation measures that determine the transition from a high to a lower infection rate. A more accurate COVID-19 evolution model requires a generalized Fermi-Dirac function that accounts for a slow variation of the effect of the mitigation measures with time. In turn, this generalization could be used in solid-state physics to describe the electron temperature increase in the electric field.
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STEM跨学科基本概念:固态物理和COVID-19大流行演变
计算机辅助设计工具的快速发展,如MATLAB或Octave或Mathematica,使学生能够解决许多复杂的问题,而不是专注于潜在的跨学科stem相关概念。因此,重要的是要用具体的例子来证明如何做到这一点,这些例子可以与不同的主题甚至与他们的日常生活经验联系起来。本文报告了在我的先进半导体器件物理课上使用COVID-19大流行进化模型(Shur, 2022)。我使用这个模型来展示如何将Born-Oppenheimer近似和Fermi-Dirac分布函数等概念应用于完全不同的STEM领域。在固体物理学中,玻恩-奥本海默近似用于分离与电子态相关的快速电子运动和更慢的原子核运动(因为原子核比电子重数千倍)。同样,COVID-19模型使用相对较快的大流行演变增长或衰减常数,这是时间本身的缓慢函数。在固体物理中,费米-狄拉克分布函数描述了从占据电子态到空电子态的转变,温度决定了转变间隔。COVID-19模型使用广义费米分布函数来描述确定从高感染率到低感染率过渡的缓解措施。一个更准确的COVID-19进化模型需要一个广义的费米-狄拉克函数,该函数可以解释缓解措施的效果随时间的缓慢变化。反过来,这种推广可以用于固态物理来描述电场中的电子温度升高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal on Advanced Science, Engineering and Information Technology
International Journal on Advanced Science, Engineering and Information Technology Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
CiteScore
1.40
自引率
0.00%
发文量
272
期刊介绍: International Journal on Advanced Science, Engineering and Information Technology (IJASEIT) is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of science, engineering and information technology. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the IJASEIT follows the open access policy that allows the published articles freely available online without any subscription. The journal scopes include (but not limited to) the followings: -Science: Bioscience & Biotechnology. Chemistry & Food Technology, Environmental, Health Science, Mathematics & Statistics, Applied Physics -Engineering: Architecture, Chemical & Process, Civil & structural, Electrical, Electronic & Systems, Geological & Mining Engineering, Mechanical & Materials -Information Science & Technology: Artificial Intelligence, Computer Science, E-Learning & Multimedia, Information System, Internet & Mobile Computing
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