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

Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.3K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.3K
Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

20.3K
Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
20.3K
Multi-Step Reactions02:31

Multi-Step Reactions

7.3K
Chemical reactions often occur in a stepwise fashion involving two or more distinct reactions taking place in a sequence. A balanced equation indicates the reacting species and the product species, but it reveals no details about how the reaction occurs at the molecular level. The reaction mechanism (or reaction path) provides details regarding the precise, step-by-step process by which a reaction occurs. Each of the steps in a reaction mechanism is called an elementary reaction. These...
7.3K
Dynamic Equilibrium02:20

Dynamic Equilibrium

51.6K
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;...
51.6K
Distillation: Vapor–Liquid Equilibria01:01

Distillation: Vapor–Liquid Equilibria

2.8K
Distillation is a separation technique that takes advantage of the boiling point properties of disparate elements in a mixture. To perform distillation, we begin by heating a miscible mixture of two liquids with a significant difference in boiling points (at least 20°C). As the solution heats up and reaches the bubble point of the more volatile component, some molecules of the more volatile component transition into the gas phase and travel upward into the condenser, which is a glass tube...
2.8K
Rate-Determining Steps03:08

Rate-Determining Steps

32.4K
Relating Reaction Mechanisms
In a multistep reaction mechanism, one of the elementary steps progresses significantly slower than the others. This slowest step is called the rate-limiting step (or rate-determining step). A reaction cannot proceed faster than its slowest step, and hence, the rate-determining step limits the overall reaction rate.
The concept of rate-determining step can be understood from the analogy of a 4-lane freeway with a short-stretch of traffic-bottleneck caused due to...
32.4K

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

Flagellar Motor Dynamics of <i>E. coli</i> Persisters under High-Load Reveal Impaired Performance and a Divergent Nonequilibrium State.

The journal of physical chemistry. B·2026
Same author

Global Quantitative Dynamics and Early Warning Signals of Hepatocellular Carcinoma: Integrating Theoretical Modeling with Experimental Validation.

The journal of physical chemistry. B·2026
Same author

Driven-Dissipative Quantum Battery with Non-equilibrium Reservoirs.

The journal of physical chemistry letters·2025
Same author

Quantifying the Single-Cell Morphological Landscape of Cellular Transdifferentiation through Force Field Reconstruction.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

Spatial landscape and flux for exploring protein pattern formation in rod-shaped bacteria.

The Journal of chemical physics·2025
Same author

Engineering Aggregation-Induced Emission Photosensitizers through a Counterion-Modulation Strategy for Enhanced Photodynamic Immunotherapy.

ACS nano·2025

相关实验视频

Updated: Jun 29, 2025

Plasmid-derived DNA Strand Displacement Gates for Implementing Chemical Reaction Networks
07:50

Plasmid-derived DNA Strand Displacement Gates for Implementing Chemical Reaction Networks

Published on: November 25, 2015

14.4K

从随机反应网络中提取动态知识.

Chuanbo Liu1, Jin Wang2,3

  • 1State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, People's Republic of China.

Proceedings of the National Academy of Sciences of the United States of America
|March 26, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种知识蒸方法,使用强化学习来将随机反应网络动态压缩到神经网络中. 该模型准确地预测系统行为,使得直接的概率估计没有复杂的模拟.

关键词:
知识的蒸知识的蒸.机器学习是机器学习.随机反应网络是随机反应网络.

更多相关视频

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K
The Use of Chemostats in Microbial Systems Biology
13:19

The Use of Chemostats in Microbial Systems Biology

Published on: October 14, 2013

30.9K

相关实验视频

Last Updated: Jun 29, 2025

Plasmid-derived DNA Strand Displacement Gates for Implementing Chemical Reaction Networks
07:50

Plasmid-derived DNA Strand Displacement Gates for Implementing Chemical Reaction Networks

Published on: November 25, 2015

14.4K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K
The Use of Chemostats in Microbial Systems Biology
13:19

The Use of Chemostats in Microbial Systems Biology

Published on: October 14, 2013

30.9K

科学领域:

  • 计算化学计算化学
  • 系统生物学 系统生物学
  • 机器学习 机器学习

背景情况:

  • 随机反应网络在生物学,化学,物理学和生态学的复杂系统模型.
  • 由于指数级状态空间增长,理解它们的动态是具有挑战性的.

研究的目的:

  • 开发一种知识蒸方法,使用强化学习来压缩来自随机反应网络的动态信息.
  • 为这些系统创建一个预测神经网络模型.

主要方法:

  • 运用强化学习的原则来提炼知识.
  • 训练了一个单一的神经网络来捕捉随机反应网络的动态.
  • 该网络预测状态条件联合概率分布.

主要成果:

  • 经过训练的神经网络准确预测系统动态和概率.
  • 能够直接估计状态和轨迹概率,而无需在状态空间上进行整合.
  • 在多式联运和高维系统中证明了高精度.

结论:

  • 知识蒸方法有效地压缩了来自随机反应网络的动态知识.
  • 神经网络作为参数推断和轨迹生成的基本模型.
  • 突出了各种随机动态系统的大规模预训练模型的潜力.