AIMC Topic: Organic Chemicals

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Machine Learning Correlation of Electron Micrographs and ToF-SIMS for the Analysis of Organic Biomarkers in Mudstone.

Journal of the American Society for Mass Spectrometry Dec 19, 2024
The spatial distribution of organics in geological samples can be used to determine when and how these organics were incorporated into the host rock. Mass spectrometry (MS) imaging can rapidly collect a large amount of data, but ions produced are mix...
Spectrometry, Mass, Secondary Ion Biomarkers Organic Chemicals Machine Learning Microscopy, Electron, Scanning Algorithms Geologic Sediments Spectrometry, X-Ray Emission
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Endocrine disruptor identification and multitoxicity level assessment of organic chemicals: An example of multiple machine learning models.

Journal of hazardous materials Dec 15, 2024
Endocrine-disrupting chemicals (EDCs) pollution is a major global environmental issue. Assessing the multiple toxic effects of EDCs is key to managing their risks. This study successfully developed an EDCs classification and recognition model based o...
Algorithms Animals Organic Chemicals Neural Networks, Computer Machine Learning Endocrine Disruptors Environmental Pollutants Molecular Dynamics Simulation
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Identifying Organic Chemicals with Acetylcholinesterase Inhibition in Nationwide Estuarine Waters by Machine Learning-Assisted Mass Spectrometric Screening.

Environmental science & technology Dec 4, 2024
Neurotoxicity is frequently observed in the global aquatic environment, threatening aquatic ecosystems and human health. However, a very limited proportion of neurotoxic effects (∼1%) has been explained by known chemicals of concern. Here, we integra...
Acetylcholinesterase China Cholinesterase Inhibitors Organic Chemicals Estuaries Mass Spectrometry Environmental Monitoring Water Pollutants, Chemical Machine Learning
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Construction of interpretable ensemble learning models for predicting bioaccumulation parameters of organic chemicals in fish.

Journal of hazardous materials Nov 20, 2024
Accurate prediction of bioaccumulation parameters is essential for assessing exposure, hazards, and risks of chemicals. However, the majority of prediction models on bioaccumulation parameters are individual models based on a single algorithm and lac...
Bioaccumulation Organic Chemicals Machine Learning Algorithms Models, Theoretical Fishes Water Pollutants, Chemical Animals Ensemble Learning
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Insights into the characteristics and toxicity of microalgal biochar-derived dissolved organic matter by spectroscopy and machine learning.

The Science of the total environment Nov 20, 2024
Microalgal biochar has potential applications in various fields; however, there is limited research on the properties and risks of microalgal biochar-derived dissolved organic matter (MBDOM). This study examined how different pyrolysis temperatures (...
Charcoal Nitrogen Spectrum Analysis Microalgae Phosphorus Organic Chemicals Water Pollutants, Chemical Machine Learning
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Chemoinformatics for corrosion science: Data-driven modeling of corrosion inhibition by organic molecules.

Molecular informatics Oct 15, 2024
This paper reviews the application of machine learning to the inhibition of corrosion by organic molecules. The methodologies considered include quantitative structure-property relationships (QSPR) and related data-driven approaches. The characterist...
Quantitative Structure-Activity Relationship Cheminformatics Corrosion Machine Learning Organic Chemicals
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Machine learning-based q-RASAR predictions of the bioconcentration factor of organic molecules estimated following the organisation for economic co-operation and development guideline 305.

Journal of hazardous materials Sep 3, 2024
In this study, we utilized an innovative quantitative read-across (RA) structure-activity relationship (q-RASAR) approach to predict the bioconcentration factor (BCF) values of a diverse range of organic compounds, based on a dataset of 575 compounds...
Animals Organic Chemicals Fishes Quantitative Structure-Activity Relationship Water Pollutants, Chemical Machine Learning Algorithms Bioaccumulation Organisation for Economic Co-Operation and Development
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Thermodynamics and explainable machine learning assist in interpreting biodegradability of dissolved organic matter in sludge anaerobic digestion with thermal hydrolysis.

Bioresource technology Aug 28, 2024
Dissolved organic matter (DOM) is essential in biological treatment, yet its specific roles remain incompletely understood. This study introduces a machine learning (ML) framework to interpret DOM biodegradability in the anaerobic digestion (AD) of s...
Machine Learning Sewage Nitrogen Organic Chemicals Thermodynamics Carbon Biodegradation, Environmental Anaerobiosis Hydrolysis
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Meta-Analysis and Machine Learning Models for Anaerobic Biodegradation Rates of Organic Contaminants in Sediments and Sludge.

Environmental science & technology Jul 10, 2024
Anaerobic biodegradation rates (half-lives) of organic chemicals are pivotal for environmental risk assessment and remediation. Traditional experimental evaluation, constrained by prolonged, oxygen-free conditions, struggles to keep pace with emergin...
Geologic Sediments Biodegradation, Environmental Sewage Organic Chemicals Anaerobiosis Machine Learning
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Understanding the phytotoxic effects of organic contaminants on rice through predictive modeling with molecular descriptors: A data-driven analysis.

Journal of hazardous materials Jun 17, 2024
The widespread introduction of organic compounds into environments poses significant risks to ecosystems. Assessing the adverse effects of organic contaminants on crops is crucial for ensuring food safety. However, laboratory research is often time-c...
Oryza Organic Chemicals Machine Learning Photosynthesis Oxidative Stress Soil Pollutants
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