Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

3.9K
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...
3.9K
Turnover Number and Catalytic Efficiency01:19

Turnover Number and Catalytic Efficiency

9.9K
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....
9.9K
Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

7.9K
For many years, scientists thought that enzyme-substrate binding took place in a simple "lock-and-key" fashion. This model stated that the enzyme and substrate fit together perfectly in one instantaneous step. However, current research supports a more refined view scientists call induced fit. The induced-fit model expands upon the lock-and-key model by describing a more dynamic interaction between enzyme and substrate. As the enzyme and substrate come together, their interaction causes...
7.9K
Catalysis02:50

Catalysis

26.5K
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.
26.5K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.1K
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,...
8.1K
Factors Influencing the Rate of Chemical Reactions01:22

Factors Influencing the Rate of Chemical Reactions

2.9K
A variety of factors influence the rate of chemical reactions. For a chemical reaction to happen, atoms must collide with enough energy to overcome the repulsion between their electrons. This energy is called activation energy. Factors influencing the rate of reaction either lower the activation energy or increase the likelihood of a successful collision.
Concentration and Pressure:
The more particles present within a given space, the more likely those particles are to bump into one another....
2.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Anion-Assisted Regulation of Solvation Structure for High-Performance Manganese-Organic Batteries.

ACS applied materials & interfaces·2026
Same author

Formally Triplet Aluminyl Anions within the [Al<sub>2</sub>Pd<sub>2</sub>]<sup>2-</sup> Cluster Stabilized by All-Metal Double Aromaticity.

Journal of the American Chemical Society·2026
Same author

Ultrasound-Activated Prodrugs for Tumor-Specific Immunotherapy.

Journal of the American Chemical Society·2026
Same author

Auxiliary Linker-Enabled Local Protonation Boosts the Ammonia Electrosynthesis in Heterometallic Metal-Organic Frameworks.

Journal of the American Chemical Society·2026
Same author

Mapping Antibiotic Photocatalytic Transformation and Resistance Risks with a DFT-Informed Machine Learning Workflow.

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

Active bridging hydride species in ZnO nanorods originated from hydroxyl and oxygen vacancy.

Nature communications·2025

Related Experiment Video

Updated: May 25, 2025

Catalytic Reactions at Amine-Stabilized and Ligand-Free Platinum Nanoparticles Supported on Titania During Hydrogenation of Alkenes and Aldehydes
12:08

Catalytic Reactions at Amine-Stabilized and Ligand-Free Platinum Nanoparticles Supported on Titania During Hydrogenation of Alkenes and Aldehydes

Published on: June 24, 2022

3.4K

Digital Descriptors in Predicting Catalysis Reaction Efficiency and Selectivity.

Qin Zhu1, Yuming Gu1, Jing Ma1

  • 1State Key Laboratory of Coordination Chemistry, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China.

The Journal of Physical Chemistry Letters
|February 26, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) uses descriptors to optimize catalysts by understanding active sites like vacancies and metals. This accelerates the discovery of efficient catalysts for energy and environmental applications.

More Related Videos

A Complete Method for Evaluating the Performance of Photocatalysts for the Degradation of Antibiotics in Environmental Remediation
08:30

A Complete Method for Evaluating the Performance of Photocatalysts for the Degradation of Antibiotics in Environmental Remediation

Published on: October 6, 2022

2.1K
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

12.7K

Related Experiment Videos

Last Updated: May 25, 2025

Catalytic Reactions at Amine-Stabilized and Ligand-Free Platinum Nanoparticles Supported on Titania During Hydrogenation of Alkenes and Aldehydes
12:08

Catalytic Reactions at Amine-Stabilized and Ligand-Free Platinum Nanoparticles Supported on Titania During Hydrogenation of Alkenes and Aldehydes

Published on: June 24, 2022

3.4K
A Complete Method for Evaluating the Performance of Photocatalysts for the Degradation of Antibiotics in Environmental Remediation
08:30

A Complete Method for Evaluating the Performance of Photocatalysts for the Degradation of Antibiotics in Environmental Remediation

Published on: October 6, 2022

2.1K
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

12.7K

Area of Science:

  • Catalysis
  • Materials Science
  • Computational Chemistry

Background:

  • Controlling interactions between multiple active sites (metals, vacancies, heteroatoms) is crucial for catalyst design.
  • Machine learning (ML) aids in optimizing catalyst performance and predicting new materials.

Purpose of the Study:

  • To explore the role of descriptors in machine learning for catalyst design.
  • To understand how active site interactions influence catalytic activity.

Main Methods:

  • Utilizing active center, interfacial, and reaction pathway descriptors.
  • Investigating the synergy between vacancies and metals in catalytic reactions.

Main Results:

  • Descriptors are key for optimizing electrochemical performance and elucidating catalytic activity.
  • Vacancies synergize with metals to enhance reduction reactions of small molecules.
  • Interpretable descriptors can be constructed by combining physical descriptors.

Conclusions:

  • Developing descriptors for complex multicatalysis systems, especially vacancies, is essential for rational catalyst design.
  • Generative AI and multimodal ML can accelerate descriptor extraction and mechanism exploration.
  • Transferable descriptors offer innovative solutions for energy conversion and environmental protection.