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Thermodynamic Systems01:06

Thermodynamic Systems

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A thermodynamic system is a set of objects whose thermodynamic properties are of interest. The system is considered to be embedded in its surroundings or the environment. The system and its environment can exchange heat and do work on each other through a boundary that separates them. However, the immediate surroundings of the system interact with it directly and therefore have a much stronger influence on its behavior and properties.
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Mechanisms of Heat Transfer I01:14

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Just as interesting as the effects of heat transfer on a system are the methods by which the heat transfer occur. Whenever there is a temperature difference, heat transfer occurs. It may occur rapidly, such as through a cooking pan, or slowly, such as through the walls of a picnic ice box. So many processes involve heat transfer that it is hard to imagine a situation where no heat transfer occurs. Yet, every heat transfer takes place by only three methods: conduction, convection, and radiation.
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Mechanisms of Heat Transfer II01:20

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In convection, thermal energy is carried by the large-scale flow of matter. Ocean currents and large-scale atmospheric circulation, which result from the buoyancy of warm air and water, transfer hot air from the tropics toward the poles and cold air from the poles toward the tropics. The Earth’s rotation interacts with those flows, causing the observed eastward flow of air in the temperate zones. Convection dominates heat transfer by air, and the amount of available space for the airflow...
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Heat transfer between the human body and its environment occurs through four main mechanisms: conduction, convection, radiation, and evaporation.
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Path Between Thermodynamics States01:21

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Thermodynamic Potentials01:26

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Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...
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Topological thermoelectrics: analytical framework, material aspects and machine learning.

Deep Mondal1, Supriya Ghosal2,3, Sujoy Datta4

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Summary

Topological quantum materials enhance thermoelectric devices by optimizing band geometry and carrier transport. This review unifies analytical, materials, and machine learning approaches for designing next-generation topological thermoelectrics.

Keywords:
machine learningthermoelectricitytopological insulatortopological semimetalstopology

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Area of Science:

  • Condensed Matter Physics
  • Materials Science
  • Quantum Mechanics

Background:

  • Topological quantum materials offer novel mechanisms for thermoelectric (TE) energy conversion.
  • Reshaping band geometry, carrier scattering, and heat transport are key opportunities in TE design.
  • A unified perspective is needed to leverage these properties for advanced TE materials.

Purpose of the Study:

  • To provide a comprehensive review of topological quantum materials for thermoelectric applications.
  • To unify analytical, materials-based, and machine learning strategies for TE design.
  • To outline design principles and computational pathways for next-generation topological thermoelectrics.

Main Methods:

  • Development of an analytical framework using the Bernevig-Hughes-Zhang model to analyze anomalous Nernst response.
  • Survey of material platforms including topological insulators, semimetals, and altermagnets.
  • Review of machine learning strategies for topological thermoelectrics, including descriptor engineering and model development.

Main Results:

  • Helical edge channels can function as energy filters, requiring particle-hole asymmetry for significant thermopower.
  • Various material platforms hosting topological phenomena are identified for TE applications.
  • Machine learning approaches show promise for accelerating the discovery and optimization of topological thermoelectrics.

Conclusions:

  • Topological properties can be controllably exploited to enhance thermoelectric performance.
  • A combination of analytical, materials, and data-driven approaches is crucial for advancing topological thermoelectrics.
  • This review provides a roadmap for designing and discovering novel topological thermoelectric materials and devices.