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

Radical Autoxidation

Radical Autoxidation

The oxidation of an organic compound in the presence of air or oxygen is called autoxidation. For example, cumene reacts with oxygen to form hydroperoxide. Autoxidation involves initiation, propagation, and termination steps. Many organic compounds are susceptible to autoxidation—especially ethers in the presence of oxygen, which form hydroperoxides. Even though this reaction is slow, old ether bottles contain small amounts of peroxide, which leads to laboratory explosions during ether...

Oxidation of Alcohols

Oxidation of Alcohols

In this lesson, the oxidation of alcohols is discussed in depth. The various reagents used for oxidation of primary and secondary alcohols are detailed, and their mechanism of action is provided.
The process of oxidation in a chemical reaction is observed in any of the three forms:

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 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.

You might also read

Related Articles

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

Sort by

Same author

Development of a comprehensive food phenolic acids database and its application: associations between dietary phenolic acids and risk of mortality.

Food research international (Ottawa, Ont.)·2026

Same author

Multi-residue analysis of systemic insecticides and their transformation products in environmental matrixes: Method development and application.

Journal of chromatography. A·2026

Same author

Indirect comparison of endoscopic balloon dilatation, stent placement, and stricturotomy for lower gastrointestinal strictures in Crohn's disease: a systematic review and meta-analysis.

European journal of gastroenterology & hepatology·2026

Same author

Development and Preclinical Evaluation of <sup>68</sup>Ga-Labeled B7-H3-Specific Bicyclic Peptides as Immuno-PET Tracers for Oncology.

Journal of medicinal chemistry·2026

Same author

Protective effect and possible mechanisms of resveratrol in animal models of spinal cord injury: a preclinical systematic review and meta-analysis.

Frontiers in immunology·2026

Same author

Per- and polyfluoroalkyl substances (PFASs) in humus-mineral layers of forest soils: Distribution, key drivers, and leaching rates.

Environmental research·2026

Search research articles

Related Experiment Video

Updated: May 17, 2026

Original Experimental Approach for Assessing Transport Fuel Stability

Original Experimental Approach for Assessing Transport Fuel Stability

Published on: October 21, 2016

Machine Learning Assisted Modeling and Interpretation of Thermally Activated Persulfate-Based Advanced Oxidation

Yanlin Zhang^1,2, Xue Yang³, Dongliang Wang^1,2

¹School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi, Hubei, China.

Water Environment Research : a Research Publication of the Water Environment Federation

|May 15, 2026

Summary

This summary is machine-generated.

Machine learning accurately predicts degradation rates in thermally activated persulfate oxidation for water treatment. The CatBoost model identified temperature, contaminant concentration, and oxidant dose as key factors for optimizing this process.

Keywords:

SHAP analysis advanced degradation processes machine learning thermally activated persulfate

More Related Videos

Temperature-programmed Deoxygenation of Acetic Acid on Molybdenum Carbide Catalysts

Temperature-programmed Deoxygenation of Acetic Acid on Molybdenum Carbide Catalysts

Published on: February 7, 2017

Analyzing the Photo-oxidation of 2-propanol at Indoor Air Level Concentrations Using Field Asymmetric Ion Mobility Spectrometry

Analyzing the Photo-oxidation of 2-propanol at Indoor Air Level Concentrations Using Field Asymmetric Ion Mobility Spectrometry

Published on: June 14, 2018

Related Experiment Videos

Last Updated: May 17, 2026

Original Experimental Approach for Assessing Transport Fuel Stability

Original Experimental Approach for Assessing Transport Fuel Stability

Published on: October 21, 2016

Temperature-programmed Deoxygenation of Acetic Acid on Molybdenum Carbide Catalysts

Temperature-programmed Deoxygenation of Acetic Acid on Molybdenum Carbide Catalysts

Published on: February 7, 2017

Analyzing the Photo-oxidation of 2-propanol at Indoor Air Level Concentrations Using Field Asymmetric Ion Mobility Spectrometry

Analyzing the Photo-oxidation of 2-propanol at Indoor Air Level Concentrations Using Field Asymmetric Ion Mobility Spectrometry

Published on: June 14, 2018

Area of Science:

Environmental Chemistry
Water Treatment Technologies
Computational Chemistry

Background:

Thermally activated persulfate oxidation is a key advanced oxidation process for refractory organic contaminant removal.
Predicting degradation kinetics and parameter interactions is challenging due to complex mechanisms and varied conditions.

Purpose of the Study:

To develop a machine learning framework for predicting degradation rate constants in thermally activated persulfate systems.
To identify key factors influencing degradation kinetics and provide an interpretable approach for process optimization.

Main Methods:

A dataset of 580 points from 53 studies was curated to train and evaluate six supervised regression models.
Models included CatBoost, XGBoost, LightGBM, random forest, support vector regression, and artificial neural networks.
Model interpretation techniques (permutation importance, SHAP, PDP) were used to identify dominant factors.

Main Results:

The CatBoost model demonstrated the most robust predictive performance with high accuracy and generalization.
Temperature, initial contaminant concentration, and oxidant dose were identified as the most significant factors affecting degradation kinetics.
Molecular descriptors showed a secondary influence within the investigated range.

Conclusions:

The developed machine learning framework offers an interpretable, data-driven approach for analyzing and optimizing thermally activated persulfate oxidation.
Model predictions were reliable across most experimental conditions, facilitating practical application in water treatment.