AIMC Topic: Ozone

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Machine Learning-Assisted Molecular Structure Embedding for Accurate Prediction of Emerging Contaminant Removal by Ozonation Oxidation.

Environmental science & technology
Ozone has demonstrated high efficacy in depredating emerging contaminants (ECs) during drinking water treatment. However, traditional quantitative structure-activation relationship (QSAR) models often fall short in effectively normalizing and charact...

Development of an automated photolysis rates prediction system based on machine learning.

Journal of environmental sciences (China)
Based on observed meteorological elements, photolysis rates (J-values) and pollutant concentrations, an automated J-values predicting system by machine learning (J-ML) has been developed to reproduce and predict the J-values of OD, NO, HONO, HO, HCHO...

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis.

Journal of mass spectrometry : JMS
Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to...

Improve the biodegradability of post-hydrothermal liquefaction wastewater with ozone: conversion of phenols and N-heterocyclic compounds.

Water science and technology : a journal of the International Association on Water Pollution Research
Hydrothermal liquefaction is a promising technology to convert wet biomass into bio-oil. However, post-hydrothermal liquefaction wastewater (PHWW) is also produced during the process. This wastewater contains a high concentration of organic compounds...

Ozone therapy as an adjuvant for endondontic protocols: microbiological - ex vivo study and citotoxicity analyses.

Journal of applied oral science : revista FOB
OBJECTIVES: This study evaluated the antimicrobial efficacy of ozone therapy in teeth contaminated with Pseudomonas aeruginosa, Enterococcus faecalis, and Staphylococcus aureus using a mono-species biofilm model. Parallel to this, the study aimed to ...

Application of central composite design and artificial neural network in modeling of reactive blue 21 dye removal by photo-ozonation process.

Water science and technology : a journal of the International Association on Water Pollution Research
The present study deals with use of central composite design (CCD) and artificial neural network (ANN) in modeling and optimization of reactive blue 21 (RB21) removal from aqueous media under photo-ozonation process. Four effective operational parame...