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相关概念视频

Real Gases: Effects of Intermolecular Forces and Molecular Volume Deriving Van der Waals Equation04:01

Real Gases: Effects of Intermolecular Forces and Molecular Volume Deriving Van der Waals Equation

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Thus far, the ideal gas law, PV = nRT, has been applied to a variety of different types of problems, ranging from reaction stoichiometry and empirical and molecular formula problems to determining the density and molar mass of a gas. However, the behavior of a gas is often non-ideal, meaning that the observed relationships between its pressure, volume, and temperature are not accurately described by the gas laws. 
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Kinetic Molecular Theory and Gas Laws Explain Properties of Gas Molecules02:34

Kinetic Molecular Theory and Gas Laws Explain Properties of Gas Molecules

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The test of the kinetic molecular theory (KMT) and its postulates is its ability to explain and describe the behavior of a gas. The various gas laws (Boyle’s, Charles’s, Gay-Lussac’s, Avogadro’s, and Dalton’s laws) can be derived from the assumptions of the KMT, which have led chemists to believe that the assumptions of the theory accurately represent the properties of gas molecules.
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Van der Waals Equation01:10

Van der Waals Equation

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The ideal gas law is an approximation that works well at high temperatures and low pressures. The van der Waals equation of state (named after the Dutch physicist Johannes van der Waals, 1837−1923) improves it by considering two factors.
First, the attractive forces between molecules, which are stronger at higher densities and reduce the pressure, are considered by adding to the pressure a term equal to the square of the molar density multiplied by a positive coefficient a. Second, the volume...
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Physical Principles Governing Gas Exchange01:16

Physical Principles Governing Gas Exchange

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Gas behavior plays a vital role in understanding bodily processes such as external and internal respiration. External respiration involves the diffusion of oxygen into the blood and carbon dioxide out of it in the lungs. In contrast, internal respiration happens in body tissues, where these gases move in opposite directions.
Gas Laws Governing Respiration
The behavior of gases is guided by Dalton's Law of partial pressures and Henry's Law.
Dalton's Law asserts that the total...
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Analyte Adsorption and Distribution01:09

Analyte Adsorption and Distribution

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In certain chromatographic separations, solutes transfer between the mobile phase and the stationary phase via sorption, which typically refers to the process of adsorption. For many chromatographic systems, the sorption process often depends on the polarity of the compounds—an expression of the overall dipole moment within the molecule. During the separation process, there is competition between the solute and solvent for adsorption to the stationary phase. Highly polar compounds and...
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Basic Postulates of Kinetic Molecular Theory: Particle Size, Energy, and Collision02:43

Basic Postulates of Kinetic Molecular Theory: Particle Size, Energy, and Collision

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The ideal-gas equation, which is empirical, describes the behavior of gases by establishing relationships between their macroscopic properties. For example, Charles’ law states that volume and temperature are directly related. Gases, therefore, expand when heated at constant pressure. Although gas laws explain how the macroscopic properties change relative to one another, it does not explain the rationale behind it.
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Author Spotlight: Characterizing Porous Materials for Aiding the Development of Robust Metal-Organic Frameworks with Adsorption Behavior
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预测气体吸附而不了解孔结构:基于经典密度函数理论的机器学习方法.

Xiangkun Wu1, Yu Liu1

  • 1School of Chemical Engineering and Technology, Sun Yat-sen University, Zhuhai 519082, China.

The journal of physical chemistry letters
|November 3, 2023
PubMed
概括

本研究介绍了一种机器学习 (ML) 方法,使用卷积神经网络来从原始N2吸附数据中预测气体吸附. ML模型准确地预测了Ar吸附,即使在复杂的孔状结构中也是如此.

科学领域:

  • 材料科学 材料科学 材料科学
  • 物理化学 物理化学
  • 计算化学计算化学

背景情况:

  • 从孔隙结构中预测气体吸附是常见的,但由于复杂的现实数据而具有挑战性.
  • 传统方法通常依赖于从表征数据中获得的近似结构信息.

研究的目的:

  • 开发一种机器学习 (ML) 方法,直接从原始N2吸附特性数据中预测气体吸附.
  • 为了克服处理复杂毛孔结构的传统方法的局限性.

主要方法:

  • 一个卷积神经网络 (CNN) 模型被开发用于气体吸附预测.
  • 该ML模型是使用从经典密度函数理论 (DFT) 计算中生成的大量数据进行训练的.
  • 该模型在孔数据上进行了训练,但对更广泛的适用性进行了测试.

主要成果:

  • 该ML模型实现了对 (Ar) 吸附的高度准确的预测.
  • 该模型成功应用于三维结构孔和现实世界材料,超出了其训练数据.
  • 预测和实际吸附等热量之间观察到强烈一致.

结论:

  • 机器学习可以有效地从原始N2吸附数据中预测气体吸附,绕过复杂的结构分析.

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  • 不同吸附剂的吸附同热度之间存在一种普遍关系,这种关系可以被ML模型捕获.
  • 这种方法为材料表征和气体吸附研究提供了强大的工具.