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

Catalysis02:50

Catalysis

27.6K
The presence of a catalyst affects the rate of a chemical reaction. A catalyst is a substance that can increase the reaction rate without being consumed during the process. A basic comprehension of a catalysts’ role during chemical reactions can be understood from the concept of reaction mechanisms and energy diagrams.
27.6K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

87
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
87
Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

4.1K
The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
Most enzymes...
4.1K
Reduction of Alkenes: Asymmetric Catalytic Hydrogenation02:17

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation

3.4K
Catalytic hydrogenation of alkenes is a transition-metal catalyzed reduction of the double bond using molecular hydrogen to give alkanes. The mode of hydrogen addition follows syn stereochemistry.
The metal catalyst used can be either heterogeneous or homogeneous. When hydrogenation of an alkene generates a chiral center, a pair of enantiomeric products is expected to form. However, an enantiomeric excess of one of the products can be facilitated using an enantioselective reaction or an...
3.4K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.6K
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,...
8.6K
Turnover Number and Catalytic Efficiency01:19

Turnover Number and Catalytic Efficiency

10.7K
The turnover number of an enzyme is the maximum number of substrate molecules it can transform per unit time. Turnover numbers for most enzymes range from 1 to 1000 molecules per second. Catalase has the known highest turnover number, capable of converting up to 2.8×106 molecules of hydrogen peroxide into water and oxygen per second. Lysozyme has the lowest known turnover number of half a molecule per second.
Chymotrypsin is a pancreatic enzyme that breaks down proteins during digestion....
10.7K

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

Mapping the crystallization landscape of rare earth MOFs: a high-throughput investigation of structure, kinetics, and selectivity.

Chemical science·2026
Same author

Quantitative prediction of siRNA complexation by ionizable drugs enables their codelivery in nanoparticles.

Science advances·2026
Same author

Lessons From Drug Discovery for Cryoprotective Agent Design: An AI-Oriented Perspective.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Photochemical post-functionalization of polystyrene enables accelerated chemical recycling.

Chemical science·2026
Same author

Adsorption Hysteresis Under Control: Tuning Host-Guest Interactions via a Genetic Algorithm.

ACS nano·2026
Same author

Pulsed-Laser-Deposited LiMn<sub>2</sub>O<sub>4</sub> Thin-Film Solid-State Microbatteries with Extended Voltage Window Cycling.

ACS applied energy materials·2026
Same journal

A trigger that feeds itself.

Nature reviews. Chemistry·2026
Same journal

Advances in electrochemical peptide synthesis and modification.

Nature reviews. Chemistry·2026
Same journal

Making chemistry sing with AI.

Nature reviews. Chemistry·2026
Same journal

Publisher Correction: Reprogramming CO<sub>2</sub> reduction through interfacial water.

Nature reviews. Chemistry·2026
Same journal

Hydrogen generation promoted by single-atom-based thermochemical catalysts.

Nature reviews. Chemistry·2026
Same journal

The phonon map of molecular qubits.

Nature reviews. Chemistry·2026
查看所有相关文章

相关实验视频

Updated: Sep 14, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.0K

用实验和计算数据开发用于异质催化物的机器学习.

Carlota Bozal-Ginesta1,2,3, Sergio Pablo-García4,5,6,7, Changhyeok Choi4,5

  • 1Nanoionics and Fuel Cells group, Catalonia Institute for Energy Research, Barcelona, Spain. carlota.bozalginesta@empa.ch.

Nature reviews. Chemistry
|July 18, 2025
PubMed
概括
此摘要是机器生成的。

机器学习 (ML) 模型可以使用计算数据预测催化剂性能. 本综述分析了将ML与高通量方法集成为固体异质催化物的趋势,使用实验数据和计算数据.

更多相关视频

Preparation and 3D Tracking of Catalytic Swimming Devices
06:50

Preparation and 3D Tracking of Catalytic Swimming Devices

Published on: July 1, 2016

7.7K
Development of Heterogeneous Enantioselective Catalysts using Chiral Metal-Organic Frameworks MOFs
08:25

Development of Heterogeneous Enantioselective Catalysts using Chiral Metal-Organic Frameworks MOFs

Published on: January 17, 2020

7.4K

相关实验视频

Last Updated: Sep 14, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.0K
Preparation and 3D Tracking of Catalytic Swimming Devices
06:50

Preparation and 3D Tracking of Catalytic Swimming Devices

Published on: July 1, 2016

7.7K
Development of Heterogeneous Enantioselective Catalysts using Chiral Metal-Organic Frameworks MOFs
08:25

Development of Heterogeneous Enantioselective Catalysts using Chiral Metal-Organic Frameworks MOFs

Published on: January 17, 2020

7.4K

科学领域:

  • 催化剂是一种催化剂.
  • 材料科学 材料科学 材料科学
  • 计算化学的计算化学

背景情况:

  • 机器学习 (ML) 在大型数据集中的模式识别方面表现出色,包括催化剂性能预测.
  • 目前在催化中的ML模型主要使用高通量量子化学,实验验证有限.
  • 简化的计算模型和稀缺的实验数据阻碍了ML在催化中的成功.

研究的目的:

  • 审查整合高通量方法和ML用于固体异质催化物的研究.
  • 分析ML模型描述符,材料,反应和数据集大小的趋势.
  • 通过使用不同趋势的R平方值来评估ML模型的性能.

主要方法:

  • 对异质催化中的ML现有文献的系统分析.
  • 基于输入/输出描述符,材料,反应和数据集大小的研究分类.
  • 基于确定趋势的ML模型性能 (R平方值) 的比较.

主要成果:

  • 在异质催化 ML 应用中确定了关键趋势.
  • 强调了整合实验和计算数据的重要性.
  • 根据各种因素对模型性能进行了比较分析.

结论:

  • 集成高吞吐量计算和机器学习对推进异质催化有很大的前景.
  • 解决计算模型和实验数据的局限性对于更广泛的ML采用至关重要.
  • 本综述提供了关于 ML 在催化剂设计中的当前趋势和未来方向的见解.