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Oxidation of Alkenes: Syn Dihydroxylation with Osmium Tetraoxide

Oxidation of Alkenes: Syn Dihydroxylation with Osmium Tetraoxide

Alkenes are converted to 1,2-diols or glycols through a process called dihydroxylation. It involves the addition of two hydroxyl groups across the double bond with two different stereochemical approaches, namely anti and syn. Dihydroxylation using osmium tetroxide progresses with syn stereochemistry.

Oxidation of Alkenes: Syn Dihydroxylation with Potassium Permanganate

Oxidation of Alkenes: Syn Dihydroxylation with Potassium Permanganate

Alkenes can be dihydroxylated using potassium permanganate. The method encompasses the reaction of an alkene with a cold, dilute solution of potassium permanganate under basic conditions to form a cis-diol along with a brown precipitate of manganese dioxide.

Properties of Organometallic Compounds

Properties of Organometallic Compounds

Organometallic compounds are compounds that contain a carbon–metal bond. Carbon belongs to an organyl group like alkyl, aryl, allyl, or benzyl groups. The metal can be from Group I or Group II of the periodic table, a transition metal, or a semimetal.

Oxidative Cleavage of Alkenes: Ozonolysis

Oxidative Cleavage of Alkenes: Ozonolysis

In ozonolysis, ozone is used to cleave a carbon–carbon double bond to form aldehydes and ketones, or carboxylic acids, depending on the work-up.
Ozone is a symmetrical bent molecule stabilized by a resonance structure.

Oxidation-Reduction Reactions

Oxidation-Reduction Reactions

Oxidation–Reduction Reactions

Oxidation of Alkenes: Anti Dihydroxylation with Peroxy Acids

Oxidation of Alkenes: Anti Dihydroxylation with Peroxy Acids

Diols are compounds with two hydroxyl groups. In addition to syn dihydroxylation, diols can also be synthesized through the process of anti dihydroxylation. The process involves treating an alkene with a peroxycarboxylic acid to form an epoxide. Epoxides are highly strained three-membered rings with oxygen and two carbons occupying the corners of an equilateral triangle. This step is followed by ring-opening of the epoxide in the presence of an aqueous acid to give a trans diol.

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Related Experiment Video

Updated: Feb 23, 2026

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides

Published on: May 29, 2018

Machine-learned and codified synthesis parameters of oxide materials.

Edward Kim¹, Kevin Huang¹, Alex Tomala¹

¹Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

Scientific Data

|September 13, 2017

Summary

Researchers developed an automated system using natural language processing (NLP) and machine learning (ML) to extract synthesis parameters from over 640,000 scientific articles. This creates a valuable, community-accessible resource for planning novel materials synthesis across diverse systems.

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Last Updated: Feb 23, 2026

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides

Published on: May 29, 2018

Tuning Oxide Properties by Oxygen Vacancy Control During Growth and Annealing

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Published on: June 9, 2023

Molten-Salt Synthesis of Complex Metal Oxide Nanoparticles

Molten-Salt Synthesis of Complex Metal Oxide Nanoparticles

Published on: October 27, 2018

Area of Science:

Materials Science
Computational Materials Science
Chemical Synthesis

Background:

Predictive materials design is advancing rapidly due to large-scale databases from ab initio computations.
Current methods lack comprehensive, autonomously compiled frameworks for materials synthesis planning.
High-throughput and machine learning techniques have shown promise for generating synthesis strategies.

Purpose of the Study:

To develop a community-accessible, autonomously compiled synthesis planning resource.
To aggregate synthesis parameters across a wide range of materials systems.
To facilitate the planning of novel materials syntheses.

Main Methods:

Utilized state-of-the-art natural language processing (NLP) and machine learning (ML) techniques.
Processed text from over 640,000 journal articles.
Compiled synthesis parameters autonomously across 30 different oxide systems.

Main Results:

Created a dataset of aggregated synthesis parameters.
The dataset is optimized for planning novel materials syntheses.
Demonstrated autonomous compilation across multiple oxide systems.

Conclusions:

The developed resource provides a novel approach to materials synthesis planning.
Autonomous compilation of synthesis data accelerates materials discovery.
This work bridges the gap between computational materials design and experimental synthesis.