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

Hydrogen Bonds00:26

Hydrogen Bonds

129.9K
Hydrogen bonds are weak attractions between atoms that have formed other chemical bonds. One of these atoms is electronegative, like oxygen, and has a partial negative charge. The other is a hydrogen atom that has bonded with another electronegative atom and has a partial positive charge.
Hydrogen Bonds Control the World!
Because hydrogen has very weak electronegativity when it binds with a strongly electronegative atom, such as oxygen or nitrogen, electrons in the bond are unequally shared....
129.9K
Hydrogen Bonds01:04

Hydrogen Bonds

13.1K
A hydrogen bond is formed when a weakly positive hydrogen atom already bonded to one electronegative atom (for example, the oxygen in the water molecule) is attracted to another electronegative atom from another polar molecule, such as water (H2O), hydrogen fluoride (HF), or ammonia (NH3). The huge electronegativity difference between the H atom (2.1) and the atom to which it is bonded (4.0 for an F atom, 3.5 for an O atom, or 3.0 for an N atom), combined with the very small size of an H atom...
13.1K
Reduction of Alkenes: Asymmetric Catalytic Hydrogenation02:17

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation

3.8K
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...
3.8K
Reduction of Alkenes: Catalytic Hydrogenation02:13

Reduction of Alkenes: Catalytic Hydrogenation

13.9K
Alkenes undergo reduction by the addition of molecular hydrogen to give alkanes. Because the process generally occurs in the presence of a transition-metal catalyst, the reaction is called catalytic hydrogenation.
Metals like palladium, platinum, and nickel are commonly used in their solid forms — fine powder on an inert surface. As these catalysts remain insoluble in the reaction mixture, they are referred to as heterogeneous catalysts.
The hydrogenation process takes place on the...
13.9K

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

Long-term renal function after high-grade renal trauma: a nuclear imaging based cohort study.

European journal of trauma and emergency surgery : official publication of the European Trauma Society·2026
Same author

Joint-Level Analysis of the Barbell Back Squat During Chain and Elastic Variable Resistance Use.

Journal of strength and conditioning research·2026
Same author

Evaluating stress distribution and clinical success in varied dental implant designs.

Bioinformation·2026
Same author

New range-separated screened and full-range hybrid functionals.

The Journal of chemical physics·2026
Same author

Exploring quantum active learning for materials design and discovery.

Physical chemistry chemical physics : PCCP·2026
Same author

Comparative Evaluation of Efficacy of Buccal Infiltration and Intraligamentary Injection with 4% Articaine in Primary Molars: <i>In Vivo</i> Study.

International journal of clinical pediatric dentistry·2026

相关实验视频

Updated: Jan 14, 2026

Author Spotlight: Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks
06:53

Author Spotlight: Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks

Published on: June 9, 2023

2.6K

寻找用于储存的金属有机框架,使用经典和量子主动学习.

Maicon Pierre Lourenço1, Rishabh Shukla2, Mosayeb Naseri2,3

  • 1Departamento de Química e Física - Centro de Ciências Exatas, Naturais e da Saúde - CCENS - Universidade Federal do Espírito Santo, 29500-000 Alegre, Espírito Santo, Brazil. maiconpl01@gmail.com.

Physical chemistry chemical physics : PCCP
|October 21, 2025
PubMed
概括

人工智能,包括量子主动学习 (QAL),可以有效地发现金属有机框架 (MOF) 以最小的数据来进行最佳的储存. 这些方法确定了增强气体吸附的有希望的MOF和实验条件.

更多相关视频

Synthesis and Characterization of Functionalized Metal-organic Frameworks
11:27

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Published on: September 5, 2014

49.1K
Preparation of Hydrophobic Metal-Organic Frameworks via Plasma Enhanced Chemical Vapor Deposition of Perfluoroalkanes for the Removal of Ammonia
12:05

Preparation of Hydrophobic Metal-Organic Frameworks via Plasma Enhanced Chemical Vapor Deposition of Perfluoroalkanes for the Removal of Ammonia

Published on: October 10, 2013

16.0K

相关实验视频

Last Updated: Jan 14, 2026

Author Spotlight: Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks
06:53

Author Spotlight: Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks

Published on: June 9, 2023

2.6K
Synthesis and Characterization of Functionalized Metal-organic Frameworks
11:27

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Published on: September 5, 2014

49.1K
Preparation of Hydrophobic Metal-Organic Frameworks via Plasma Enhanced Chemical Vapor Deposition of Perfluoroalkanes for the Removal of Ammonia
12:05

Preparation of Hydrophobic Metal-Organic Frameworks via Plasma Enhanced Chemical Vapor Deposition of Perfluoroalkanes for the Removal of Ammonia

Published on: October 10, 2013

16.0K

科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学的计算化学
  • 人工智能的人工智能

背景情况:

  • 金属有机框架 (MOF) 是先进的多孔材料,具有大量气体储存和分离的潜力.
  • 高效的储存对于清洁能源应用至关重要,MOFs提供了一个有前途的途径.
  • 发现最佳的MOF和用于储存的实验条件是具有挑战性的,因为设计空间广.

研究的目的:

  • 开发和评估主动学习 (AL) 和量子主动学习 (QAL) 方法,用于设计具有增强存储能力的MOF.
  • 探索AL和QAL在识别最佳MOF和实验参数 (温度,压力) 的性能,使用文献衍生数据集.
  • 评估这些人工智能技术在有限的实验数据下有效地导航MOF设计空间的能力.

主要方法:

  • 实施AL和QAL方法用于MOF发现.
  • 利用回归模型,包括人工神经网络,支持向量回归和高斯过程 (GP和QGP).
  • 采用了各种不确定性量化和获取功能来选择下一个实验目标; QAL特别使用了带有预测量子内核的QGP.

主要成果:

  • 通过AL和QAL方法,使用有限的数据集,成功识别了具有增强存储特性的MOF.
  • 人工智能方法证明了能够从类似的候选人中区分最佳的MOF和条件.
  • 开发了一种网络图形方法来分析AL和QAL在MOF搜索中的表现.

结论:

  • 主动学习和量子主动学习是加速发现具有优越储能能力的MOF的有效工具.
  • 这些人工智能驱动的方法可以显著减少识别新材料和最佳条件所需的实验力度.
  • 该研究强调了古典和量子计算方法在推进能源应用中的材料设计方面的潜力.