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Reduction of Alkynes to cis-Alkenes: Catalytic Hydrogenation

Reduction of Alkynes to cis-Alkenes: Catalytic Hydrogenation

Introduction
Like alkenes, alkynes can be reduced to alkanes in the presence of transition metal catalysts such as Pt, Pd, or Ni. The reaction involves two sequential syn additions of hydrogen via a cis-alkene intermediate.

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...

Reduction of Alkenes: Catalytic Hydrogenation

Reduction of Alkenes: Catalytic Hydrogenation

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...

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...

Carboxylic Acids to Methylesters: Alkylation using Diazomethane

Carboxylic Acids to Methylesters: Alkylation using Diazomethane

Carboxylic acids react with diazomethane in an ether solvent via alkylation at the carboxylate oxygen atom to give methyl esters of the corresponding acid with excellent yields.

Predicting Reaction Outcomes

Predicting Reaction Outcomes

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,...

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Confinement-Driven Anomalous Behaviors for Diffusion in Zeolites: Mechanisms and Beyond.

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Related Experiment Video

Updated: Apr 20, 2026

Adsorption Device Based on a Langatate Crystal Microbalance for High Temperature High Pressure Gas Adsorption in Zeolite H-ZSM-5

Adsorption Device Based on a Langatate Crystal Microbalance for High Temperature High Pressure Gas Adsorption in Zeolite H-ZSM-5

Published on: August 25, 2016

Harnessing confinement effect and interpretable machine learning to predict alkane diffusion in zeolite catalysts.

Xiaobao Wang¹, Ji Qi², Mingyu Wan¹

¹Interdisciplinary Institute of NMR and Molecular Sciences, Hubei Province Key Laboratory for Coal Conversion and New Carbon Materials, School of Chemistry and Chemical Engineering, Wuhan University of Science and Technology, Wuhan, PR China.

Nature Communications

|April 18, 2026

Summary

Machine learning models predict zeolite diffusion using topology-informed descriptors. Tortuosity and channel variations are key barriers, enabling tailored zeolite design for catalysis and separations.

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Last Updated: Apr 20, 2026

Adsorption Device Based on a Langatate Crystal Microbalance for High Temperature High Pressure Gas Adsorption in Zeolite H-ZSM-5

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Published on: August 25, 2016

Imine Metathesis by Silica-Supported Catalysts Using the Methodology of Surface Organometallic Chemistry

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Synthesis of Zeolites Using the ADOR Assembly-Disassembly-Organization-Reassembly Route

Synthesis of Zeolites Using the ADOR Assembly-Disassembly-Organization-Reassembly Route

Published on: April 3, 2016

Area of Science:

Materials Science
Chemical Engineering
Computational Chemistry

Background:

Diffusion in zeolites is crucial for catalysis and separations but lacks universal predictive models.
Current understanding of zeolite diffusion mechanisms is often system-specific, hindering rational material design.

Purpose of the Study:

To develop a universal framework connecting zeolite topology to diffusion properties using machine learning.
To identify key structural factors governing mass transport in zeolites.

Main Methods:

High-throughput molecular simulations of approximately 100,000 zeolitic frameworks.
Application of interpretable machine learning with topology-informed descriptors (tortuosity, cross-sectional variance).
Utilizing transfer learning to extend model applicability to various small organic molecules.

Main Results:

A highly accurate and transferable machine learning model predicting zeolite diffusion was developed.
Pore-limiting diameter facilitates diffusion, while channel tortuosity and cross-sectional heterogeneity significantly impede it.
The model successfully predicted diffusion for methane and was transferable to ethane, ethene, and methanol.

Conclusions:

A machine learning framework elucidates confinement-governed mass transport in zeolites.
This approach accelerates the rational design of tailored zeolite materials for enhanced performance.
The study provides a large, curated dataset for zeolite diffusion research.