AIMC Topic: Organic Chemicals
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First report on Quantitative Structure-Toxicity Relationship modeling approaches for the prediction of acute toxicity of various organic chemicals against rotifer species.
The Science of the total environment
Apr 10, 2025
Nowadays, organic chemicals are crucial components in virtually every aspect of daily life, serving as indispensable elements for modern society. The ongoing synthesis of chemicals and the various potential harmful effects on living organisms are pro...
Unsupervised Machine Learning-Based Image Recognition of Raw Infrared Spectra: Toward Chemist-like Chemical Structural Classification and Beyond Numerical Data.
Journal of chemical information and modeling
Mar 19, 2025
Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemic...
Predicting oleogels properties using non-invasive spectroscopic techniques and machine learning.
Food research international (Ottawa, Ont.)
Feb 26, 2025
Oleogelators are considered food additives that require approval from regulatory authorities. Therefore, classifying these ingredients that may have characteristics (e.g., waxiness), cost and origin (e.g., animal or vegetable) is crucial to ensure co...
Advancing micro-nano supramolecular assembly mechanisms of natural organic matter by machine learning for unveiling environmental geochemical processes.
Environmental science. Processes & impacts
Jan 22, 2025
The nano-self-assembly of natural organic matter (NOM) profoundly influences the occurrence and fate of NOM and pollutants in large-scale complex environments. Machine learning (ML) offers a promising and robust tool for interpreting and predicting t...
CAM/TMA-DPH as a promising alternative to SYTO9/PI for cell viability assessment in bacterial biofilms.
Frontiers in cellular and infection microbiology
Jan 21, 2025
INTRODUCTION: Accurately assessing biofilm viability is essential for evaluating both biofilm formation and the efficacy of antibacterial treatments. Traditional SYTO9 and propidium iodide (PI) live/dead staining in biofilm viability assays often ace...
Interpretable Machine Learning Models Delivering a New Perspective for the Reaction Mechanism between Organic Pollutants and Oxidative Radicals.
Environmental science & technology
Jan 7, 2025
Machine learning (ML) is expected to bring new insights into the impact of organic structures on the reaction mechanisms in reactive oxygen species oxidation. However, understanding the underlying chemical mechanisms still faces challenges due to the...
The abiologically and biologically driving effects on organic matter in marginal seas revealed by deep learning-assisted model analysis.
The Science of the total environment
Jan 3, 2025
The biogeochemical processes of organic matter exhibit notable variability and unpredictability in marginal seas. In this study, the abiologically and biologically driving effects on particulate organic matter (POM) and dissolved organic matter (DOM)...
Environmental drivers of dissolved organic matter composition across central European aquatic systems: A novel correlation-based machine learning and FT-ICR MS approach.
Water research
Dec 20, 2024
Dissolved organic matter (DOM) present in surface aquatic systems is a heterogeneous mixture of organic compounds reflecting its allochthonous and autochthonous organic matter (OM) sources. The composition of DOM is determined by environmental factor...
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...
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...
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