Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Catalysis

Catalysis

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.

Catalysis

Catalysis

Catalysis influences the rate of chemical reactions by providing an alternative reaction pathway with lower activation energy. A catalyst speeds up a reaction, but it is not consumed during the process. The fundamental principle of catalysis is the ability of a catalyst to alter the reaction mechanism, often introducing a more efficient pathway than the uncatalyzed process.In a catalyzed reaction, the catalyst participates directly in the reaction mechanism. It interacts with reactants to form...

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation

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...

Inorganic Nitrogen Assimilation

Inorganic Nitrogen Assimilation

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 nitrate reductase...

Preparation of Amines: Reduction of Oximes and Nitro Compounds

Preparation of Amines: Reduction of Oximes and Nitro Compounds

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,...

Heterogeneous Catalysis

Heterogeneous Catalysis

Heterogeneous catalysis involves a catalyst in a different phase from the reactants. It is a process where the catalyst and the reactants are in distinct phases, typically solid and gas or liquid.Most heterogeneous catalysts are metals, metal oxides, or acids. The list includes transition metals like iron (Fe), cobalt (Co), nickel (Ni), palladium (Pd), platinum (Pt), chromium (Cr), manganese (Mn), tungsten (W), silver (Ag), and copper (Cu). These metals possess partially vacant d orbitals that...

You might also read

Related Articles

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

Sort by

Same author

How Ligand Protonation and Hydrogen Bonding Govern Oxygen Reduction Reaction Selectivity by a Dinuclear Copper Catalyst: Insights from DFT Calculations.

Inorganic chemistry·2026

Same author

Unraveling N-O Cleavage and N-H Bond Formation for NO Reduction to NH<sub>3</sub> at a Bimetallic Fe-Mo Site.

Journal of the American Chemical Society·2026

Same author

Influence of the Conjugated Structure of Redox Ligands on the PCET Process in Water Oxidation Reactions.

Inorganic chemistry·2026

Same author

A Functional Model of Diiron Nitrite Reductases: Overcoming Catalyst Poisoning in the Diiron-Mediated Catalytic Reduction of Nitrite to Nitric Oxide.

Journal of the American Chemical Society·2026

Same author

Electrochemical Alkene Diazidation via Voltage-Controlled High-Valent Mn Catalysis: A DFT Mechanistic Study.

The Journal of organic chemistry·2026

Same author

Reduction of Nitrite to NO on a Molybdenum-Thiolate Platform and Protonation of the Anionic Nitrosyl Complex.

Inorganic chemistry·2026

Search research articles

Related Experiment Video

Updated: Jun 7, 2026

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

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

Published on: April 10, 2018

Machine Learning Accelerated Computational Design of Bio-Inspired Catalysts in the Nitrogen Reduction Reaction.

Leonardo Di Ciano¹, Zihan You^1,2, Haoran Chen³

¹Department of Chemistry-Ångström Laboratory, Molecular Biomimetics, Uppsala University, Uppsala, Sweden.

Advanced Materials (Deerfield Beach, Fla.)

|June 6, 2026

Summary

Developing efficient ammonia synthesis catalysts is key for sustainability. This study uses computation and machine learning to predict catalyst performance, enabling rational design for a greener Haber-Bosch process alternative.

Keywords:

computational workflow feature engineering machine learning nitrogen reduction reaction

More Related Videos

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

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

Preparation of Biomass-based Mesoporous Carbon with Higher Nitrogen-/Oxygen-chelating Adsorption for Cu(II) Through Microwave Pre-Pyrolysis

Preparation of Biomass-based Mesoporous Carbon with Higher Nitrogen-/Oxygen-chelating Adsorption for Cu(II) Through Microwave Pre-Pyrolysis

Published on: February 12, 2019

Related Experiment Videos

Last Updated: Jun 7, 2026

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

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

Published on: April 10, 2018

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

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

Preparation of Biomass-based Mesoporous Carbon with Higher Nitrogen-/Oxygen-chelating Adsorption for Cu(II) Through Microwave Pre-Pyrolysis

Preparation of Biomass-based Mesoporous Carbon with Higher Nitrogen-/Oxygen-chelating Adsorption for Cu(II) Through Microwave Pre-Pyrolysis

Published on: February 12, 2019

Area of Science:

Catalysis
Computational Chemistry
Machine Learning

Background:

Developing efficient catalysts for nitrogen conversion to ammonia is crucial for a sustainable alternative to the energy-intensive Haber-Bosch process.
Rational catalyst design is challenging due to complex structure-function relationships under realistic conditions.

Purpose of the Study:

To present an integrated computational framework combining quantum chemical calculations with machine learning models to predict experimental catalytic metrics in metal-ligand complexes.
To establish an efficient and practical framework for the discovery and inverse design of high-performance catalysts.

Main Methods:

Integrated computational framework using quantum chemical calculations.
Development and application of 27 machine learning models trained and validated on a large experimental database.
Prediction of experimental catalytic metrics including classification, turnover frequency (TOF), and turnover number (TON).

Main Results:

Machine learning models demonstrated high predictive accuracy for catalyst classification (up to 1 test accuracy) and regression (test R² values up to 0.99).
Models accurately captured time-dependent variability of TOF and TON for new complexes, with predicted values closely matching experimental results.
Strong transfer learning capabilities were observed across structurally distinct coordination architectures, and feature interpretation revealed key design principles.

Conclusions:

The study established an efficient and practical framework for the discovery and inverse design of high-performance catalysts under realistic conditions.
The developed framework has broader relevance to electrocatalysis and other catalytic applications.
Identified key design principles for optimal catalysts, including metal spin state, ligand geometry, charge distribution, and experimental conditions.