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相关概念视频

Mechanisms of Heat Transfer II01:20

Mechanisms of Heat Transfer II

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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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Mechanism of heat transfer01:19

Mechanism of heat transfer

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Understanding heat transfer mechanisms is essential for understanding how our bodies maintain balance in different environmental conditions. When the environment is thermoneutral, the body is in a state of balance, neither using nor releasing energy to maintain its core temperature. However, when the environment is not thermoneutral, the body employs four heat transfer mechanisms to maintain homeostasis: conduction, convection, evaporation, and radiation. These mechanisms facilitate heat...
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Mechanisms of Heat Transfer I01:14

Mechanisms of Heat Transfer I

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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 Transfer01:14

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Heat transfer between the human body and its environment occurs through four main mechanisms: conduction, convection, radiation, and evaporation.
Conduction, accounting for approximately 3% of body heat loss at rest, is the process of exchanging heat between molecules of two materials in direct contact. This can result in both heat loss and gain. For instance, when the body is submerged in water, which conducts heat 20 times more effectively than air, it can either lose or gain significant...
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Thermal Sigmatropic Reactions: Overview01:16

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Sigmatropic rearrangements are a class of pericyclic reactions in which a σ bond migrates from one part of a π system to another. These are intramolecular rearrangements where the total number of σ and π bonds remain unchanged.
Sigmatropic shifts are classified based on an order term [i, j ], where i and j indicate the number of atoms across which each end of the σ bond migrates. Below are examples of a [3,3] sigmatropic shift in...
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Peripheral thermosensation is the perception of external temperature. A change in temperature (on the surface of the skin and other tissues) is detected by a family of temperature-sensitive ion channels called Transient Receptor Potential, or TRP, receptors. These receptors are located on free nerve endings. Those detecting cold temperatures are closer to the surface of the skin than the nerve endings detecting warmth. These thermoTRP channels, while temperature selective, have relatively...
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释放离子热电材料的新可能性:机器学习的视角

Yidan Wu1, Dongxing Song2, Meng An3

  • 1Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.

National science review
|January 7, 2025
PubMed
概括

研究人员开发了一种机器学习模型来预测离子热电 (i-TE) 材料的Seebeck系数,加速了用于废热回收和热传感应用的新材料的发现.

关键词:
可以解释的分析分析.离子热电材料是离子热电材料.机器学习是机器学习.热电转换是一种热电转换.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 热电学是一种热电学.
  • 机器学习 机器学习

背景情况:

  • 离子热电 (i-TE) 材料为废热回收和热传感器提供高热功率.
  • 目前的材料发现依赖于低效的试错方法,缺乏理论指导.

研究的目的:

  • 开发一种机器学习模型,用于预测i-TE材料的Seebeck系数.
  • 为了克服不一致的i-TE材料类型的挑战,使用简化分子输入系统.
  • 为了加速发现高性能i-TE材料.

主要方法:

  • 引入了一个简化的分子输入线路输入系统.
  • 开发并验证了一种机器学习模型来评估Seebeck系数 (R2 = 0.98).
  • 进行了一种新的离子凝材料的实验识别,并使用分子动力学模拟进行分析.

主要成果:

  • 使用机器学习模型实现了Seebeck系数的高预测精度 (R2 = 0.98).
  • 在实验中确定了一种由水传播的聚氨/化离子体离子凝,其Seebeck系数为41.39mV/K.
  • 确定了对Seebeck系数产生负面影响的关键分子描述因子 (可旋转的键,八醇-水分区系数).

结论:

  • 机器学习辅助框架显著加速了i-TE材料的发现.
  • 开发的模型为材料设计提供了理论基础,减少了对试错的依赖.
  • 这种开创性的方法对推动离子热电领域的发展具有重大前景.