Automated Reaction Exploration of Ozonation Processes for Model Olefins in Water

Area of Science:

• Environmental Chemistry
• Computational Chemistry
• Water Treatment Technologies

Background:

• Evaluating pollutant degradation and identifying hazardous byproducts in chemical oxidation are critical challenges.
• Automated computational methods are needed to elucidate complex reaction pathways.

Purpose of the Study:

• To demonstrate high-throughput computational chemistry for elucidating reaction pathways in pollutant abatement.
• To evaluate the predictive accuracy of automated chemical reaction network (CRN) explorations using quantum chemistry.

Main Methods:

• Utilized the Software for Chemical Interaction Networks (SCINE) for automated CRN explorations.
• Benchmarked quantum chemical methods for structure optimization and energy calculations.
• Generated CRNs, identified reaction mechanisms, and performed microkinetic modeling for ozone-olefin reactions.

Main Results:

• CRN explorations accurately reproduced experimental mechanisms and products for ethene ozonolysis.
• The approach matched main products for tetramethylethene ozonolysis but highlighted limitations in exploration depth and solvation models.
• Successfully predicted pollutant transformation pathways and kinetics based on initial reactant concentrations.

Conclusions:

• Automated CRN explorations offer a powerful tool for predicting pollutant transformation pathways in water treatment.
• This computational approach aids in identifying potentially hazardous transformation products.
• The findings support chemical analysis and the assessment of environmental and human health impacts.