Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Dynamic Equilibrium02:20

Dynamic Equilibrium

62.4K
A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
62.4K
Solution Equilibrium and Saturation01:59

Solution Equilibrium and Saturation

21.9K
Imagine adding a small amount of sugar to a glass of water, stirring until all the sugar has dissolved, and then adding a bit more. You can repeat this process until the sugar concentration of the solution reaches its natural limit, a limit determined primarily by the relative strengths of the solute-solute, solute-solvent, and solvent-solvent attractive forces. You can be certain that you have reached this limit because, no matter how long you stir the solution, undissolved sugar remains. The...
21.9K
Free Energy and Equilibrium02:56

Free Energy and Equilibrium

27.2K
The free energy change for a process may be viewed as a measure of its driving force. A negative value for ΔG represents a driving force for the process in the forward direction, while a positive value represents a driving force for the process in the reverse direction. When ΔGrxn is zero, the forward and reverse driving forces are equal, and the process occurs in both directions at the same rate (the system is at equilibrium).
Recall that Q is the numerical value of the mass action...
27.2K
Calculating the Equilibrium Constant02:46

Calculating the Equilibrium Constant

37.9K
The equilibrium constant for a reaction is calculated from the equilibrium concentrations (or pressures) of its reactants and products. If these concentrations are known, the calculation simply involves their substitution into the Kc expression.
For example, gaseous nitrogen dioxide forms dinitrogen tetroxide according to this equation:
37.9K
Calculating Equilibrium Concentrations02:05

Calculating Equilibrium Concentrations

53.1K
Being able to calculate equilibrium concentrations is essential to many areas of science and technology—for example, in the formulation and dosing of pharmaceutical products. After a drug is ingested or injected, it is typically involved in several chemical equilibria that affect its ultimate concentration in the body system of interest. Knowledge of the quantitative aspects of these equilibria is required to compute a dosage amount that will solicit the desired therapeutic effect.
A more...
53.1K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

15.0K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
15.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Distinguishing Ideal and Non-Ideal Chemical Systems Based on Kinetic Behavior.

Entropy (Basel, Switzerland)·2025
Same author

Understanding Catalyst 'Volcano' Dependence Through Fermi-Level Controlled Kinetics Using Electronic Theory.

Entropy (Basel, Switzerland)·2025
Same author

Three-Factor Kinetic Equation of Catalyst Deactivation.

Entropy (Basel, Switzerland)·2021
Same author

Spherical core-shell alumina support particles for model platinum catalysts.

Nanoscale·2021
Same author

Numerical Modelling Assisted Design of a Compact Ultrafiltration (UF) Flat Sheet Membrane Module.

Membranes·2021
Same author

Perturbed and Unperturbed: Analyzing the Conservatively Perturbed Equilibrium (Linear Case).

Entropy (Basel, Switzerland)·2020

相关实验视频

Updated: Jan 29, 2026

Accurate Determination of the Equilibrium Surface Tension Values with Area Perturbation Tests
07:57

Accurate Determination of the Equilibrium Surface Tension Values with Area Perturbation Tests

Published on: August 30, 2019

7.8K

一种基于物理学的神经网络 (PINN) 方法,用于在保守扰乱的线性平衡系统中的过度平衡动力学.

Abhishek Dutta1, Bitan Mukherjee2, Sk Aftab Hosen2

  • 1Department of Chemical Engineering, Izmir Institute of Technology, Izmir 35430, Turkey.

Entropy (Basel, Switzerland)
|January 28, 2026
PubMed
概括

基于物理学的神经网络 (PINNs) 准确地模拟化学反应动态,在没有广泛的数据的情况下捕获暂时的超平衡度极端. 这种方法可以确保有效地满足物理保护法则.

关键词:
保守性地扰乱了平衡的平衡.循环和非循环的机制.有限时间热力学有限时间热力学超平衡的动态 超平衡的动态基于物理学的神经网络.

更多相关视频

Author Spotlight: Tackling Challenges in Synthetic Cell Engineering
10:56

Author Spotlight: Tackling Challenges in Synthetic Cell Engineering

Published on: April 12, 2024

1.7K
Non-equilibrium Microwave Plasma for Efficient High Temperature Chemistry
07:17

Non-equilibrium Microwave Plasma for Efficient High Temperature Chemistry

Published on: August 1, 2017

13.1K

相关实验视频

Last Updated: Jan 29, 2026

Accurate Determination of the Equilibrium Surface Tension Values with Area Perturbation Tests
07:57

Accurate Determination of the Equilibrium Surface Tension Values with Area Perturbation Tests

Published on: August 30, 2019

7.8K
Author Spotlight: Tackling Challenges in Synthetic Cell Engineering
10:56

Author Spotlight: Tackling Challenges in Synthetic Cell Engineering

Published on: April 12, 2024

1.7K
Non-equilibrium Microwave Plasma for Efficient High Temperature Chemistry
07:17

Non-equilibrium Microwave Plasma for Efficient High Temperature Chemistry

Published on: August 1, 2017

13.1K

科学领域:

  • 化学动力学 化学动力学
  • 计算化学是一种计算化学.
  • 科学中的人工智能.

背景情况:

  • 保守性扰乱平衡 (CPE) 实验显示过渡性度极端超过稳定状态值.
  • 在化学反应网络中模拟这些超平衡动态在计算上具有挑战性,通常需要广泛的时间序列数据.

研究的目的:

  • 引入物理信息神经网络 (PINN) 框架,用于模拟线性化学反应网络中的超平衡动态.
  • 为了证明PINN能够在没有大量时间序列数据的情况下准确地捕获短暂的度极端.

主要方法:

  • 开发了一种PINN框架,将反应动力学,稳定计不变量和平衡约束纳入损失函数.
  • 确保PINN解决方案严格遵守物理保护法.
  • 将PINN应用于三种和四种可逆反应机制 (非循环和循环).

主要成果:

  • 替代PINN准确地复制了传统ODE集成的结果.
  • 该模型成功地捕获了早期特征的极端度 (最大/最小) 和随后的平衡放松.
  • 在预测极端的时间和大小方面取得了很高的准确性,同时保持了总质量.

结论:

  • 基于物理学的方法能够准确地模拟超平衡动力学,使用最小的数据.
  • PINNs提供了一个参数高效和物理约束的方法来建模复杂的化学系统.
  • 这个框架展示了人工智能在推进计算化学和反应动态方面的潜力.