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

30.0K
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.
30.0K
Preparation of Amines: Reduction of Oximes and Nitro Compounds01:29

Preparation of Amines: Reduction of Oximes and Nitro Compounds

4.5K
Oximes can be reduced to primary amines using catalytic hydrogenation, hydride reduction, or sodium metal reduction. The reduction of aliphatic and aromatic nitro compounds to primary amines takes place by either catalytic hydrogenation or by using active metals like Fe, Zn, and Sn in the presence of an acid.
Though catalytic hydrogenation can reduce nitrobenzenes, the reduction is nonselective in the presence of other functional groups. For instance, if nitrobenzene contains an aldehyde group,...
4.5K
Inorganic Nitrogen Assimilation01:22

Inorganic Nitrogen Assimilation

421
Nitrogen is an essential element in biological systems, forming a crucial component of proteins, nucleic acids, and other cellular constituents. Many bacteria and archaea acquire nitrogen in the form of nitrate (NO₃⁻) or ammonia (NH₃), which are then assimilated into biomolecules through specific enzymatic pathways.Assimilatory Nitrate ReductionWhen nitrate enters the cell, it undergoes a two-step reduction process known as assimilatory nitrate reduction. Initially, the enzyme...
421
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.6K
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
1.6K

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

MXene (Ti<sub>3</sub>C<sub>2</sub>T <sub><i>x</i></sub> ) mediated multiscale electrolyte regulation enables robust interphase for dendrite-free solid-state sodium metal batteries.

Chemical science·2026
Same author

Bulk-to-interface fluorination for stable and low-pressure all-solid-state lithium metal batteries.

Nature communications·2026
Same author

A plant-receptor-inspired cuprous complex for wearable trace-level ethylene gas sensing.

Nature communications·2026
Same author

Sulfur-Bridge Engineering Enables Reverse Hydrogen Spillover to Atomic Cu for Nitrate-to-Ammonia Electrocatalysis.

Angewandte Chemie (International ed. in English)·2026
Same author

Constant-Potential MD with Neural Network Potentials Reveals Cation Effects on CO<sub>2</sub> Reduction at Au-Water Interfaces.

JACS Au·2026
Same author

Density-Potential Functional Theory with Explicit Solvation and Desolvation for Electrical Double Layers.

Journal of chemical theory and computation·2026

相关实验视频

Updated: Jan 6, 2026

Synthesis and Performance Characterizations of Transition Metal Single Atom Catalyst for Electrochemical CO2 Reduction
10:57

Synthesis and Performance Characterizations of Transition Metal Single Atom Catalyst for Electrochemical CO2 Reduction

Published on: April 10, 2018

18.9K

通过多代理合作,自动化电化学降低催化剂设计的结构-活性分析.

Xu Hu1, Suya Chen1, Letian Chen1

  • 1School of Materials Science and Engineering, Institute of New Energy Material Chemistry, Renewable Energy Conversion and Storage Center (RECAST), Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), Nankai University, Tianjin 300350, China.

National science review
|November 7, 2025
PubMed
概括

eNRRCrew是一个新的AI框架,通过分析科学论文来加速可持续氨生产研究,以预测电催化剂性能,并指导电化学降解反应 (eNRR) 的催化剂设计.

关键词:
大型语言模型.多个代理合作的协作.固化的方法 固化的方法合理的催化剂设计设计结构活动关系结构活动关系

更多相关视频

Synthesis of Metal Nanoparticles Supported on Carbon Nanotube with Doped Co and N Atoms and its Catalytic Applications in Hydrogen Production
08:40

Synthesis of Metal Nanoparticles Supported on Carbon Nanotube with Doped Co and N Atoms and its Catalytic Applications in Hydrogen Production

Published on: December 6, 2021

4.1K
Synthesis of Platinum-nickel Nanowires and Optimization for Oxygen Reduction Performance
09:02

Synthesis of Platinum-nickel Nanowires and Optimization for Oxygen Reduction Performance

Published on: April 27, 2018

8.2K

相关实验视频

Last Updated: Jan 6, 2026

Synthesis and Performance Characterizations of Transition Metal Single Atom Catalyst for Electrochemical CO2 Reduction
10:57

Synthesis and Performance Characterizations of Transition Metal Single Atom Catalyst for Electrochemical CO2 Reduction

Published on: April 10, 2018

18.9K
Synthesis of Metal Nanoparticles Supported on Carbon Nanotube with Doped Co and N Atoms and its Catalytic Applications in Hydrogen Production
08:40

Synthesis of Metal Nanoparticles Supported on Carbon Nanotube with Doped Co and N Atoms and its Catalytic Applications in Hydrogen Production

Published on: December 6, 2021

4.1K
Synthesis of Platinum-nickel Nanowires and Optimization for Oxygen Reduction Performance
09:02

Synthesis of Platinum-nickel Nanowires and Optimization for Oxygen Reduction Performance

Published on: April 27, 2018

8.2K

科学领域:

  • 电化学 电化学 电化学
  • 材料科学 材料科学 材料科学
  • 人工智能的人工智能

背景情况:

  • 电化学降解反应 (eNRR) 是可持续氨合成的一个有前途的途径.
  • 在ENRR电催化剂中阐明结构-活性关系 (SAR) 仍然是一个重大挑战.

研究的目的:

  • 引入 eNRRCrew,这是一个利用LLMs,机器学习和自动化工具推进 eNRR研究的多代理框架.
  • 开发一个可扩展的平台来提取SAR和指导合理的电催化剂设计.

主要方法:

  • 对2321篇科学论文进行分析,以建立一个全面的eNRR数据库.
  • 使用随机森林分类器进行NNRR产量预测和模型解释性分析.
  • 采用聚类分析来识别法拉第克效率模式.
  • 整合五个LLM代理用于自然语言交互,催化剂建议和性能预测.

主要成果:

  • 构建一个详细的电催化剂特性,条件和性能数据库.
  • 确定关键的SAR因子,包括空间组号和元素电子阴性差异.
  • 通过聚类发现明显的法拉第效率模式.
  • 证明LLM代理人在催化剂推,预测,数据分析和文献见解方面的能力.

结论:

  • eNRRCrew为LLM驱动的电催化科学发现提供了一个新的范式.
  • 该框架在提取SAR和加速催化剂设计方面超越了传统方法.
  • eNRRCrew提供了一个可扩展的平台,适用于各种电催化领域.