AIMC Topic: Kinetics
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Artificial neural network modeling for the oxidation kinetics of divalent manganese ions during chlorination and the role of arsenite ions in the binary/ternary systems.
Water research
Jun 3, 2024
This study investigated the coexistence and contamination of manganese (Mn(II)) and arsenite (As(III)) in groundwater and examined their oxidation behavior under different equilibrating parameters, including varying pH, bicarbonate (HCO) concentratio...
OrganoIDNet: a deep learning tool for identification of therapeutic effects in PDAC organoid-PBMC co-cultures from time-resolved imaging data.
Cellular oncology (Dordrecht, Netherlands)
May 28, 2024
PURPOSE: Pancreatic Ductal Adenocarcinoma (PDAC) remains a challenging disease due to its complex biology and aggressive behavior with an urgent need for efficient therapeutic strategies. To assess therapy response, pre-clinical PDAC organoid-based m...
Adsorptive removal of perfluorooctanoic acid from aqueous matrices using peanut husk-derived magnetic biochar: Statistical and artificial intelligence approaches, kinetics, isotherm, and thermodynamics.
Chemosphere
May 21, 2024
Removal of perfluorooctanoic acid (PFOA) from water matrices is crucial owing to its pervasiveness and adverse ecological and human health effects. This study investigates the adsorptive removal of PFOA using magnetic biochar (MBC) derived from FeCl-...
Artificial neural network-based modeling of Malachite green adsorption onto baru fruit endocarp: insights into equilibrium, kinetic, and thermodynamic behavior.
International journal of phytoremediation
May 17, 2024
In this study, artificial neural network (ANN) tools were employed to forecast the adsorption capacity of Malachite green (MG) by baru fruit endocarp waste (B@FE) under diverse conditions, including pH, adsorbent dosage, initial dye concentration, co...
Construction of an enzyme-constrained metabolic network model for Myceliophthora thermophila using machine learning-based k data.
Microbial cell factories
May 15, 2024
BACKGROUND: Genome-scale metabolic models (GEMs) serve as effective tools for understanding cellular phenotypes and predicting engineering targets in the development of industrial strain. Enzyme-constrained genome-scale metabolic models (ecGEMs) have...
Unveiling the potential of machine learning in cost-effective degradation of pharmaceutically active compounds: A stirred photo-reactor study.
Chemosphere
May 5, 2024
In this study, neural networks and support vector regression (SVR) were employed to predict the degradation over three pharmaceutically active compounds (PhACs): Ibuprofen (IBP), diclofenac (DCF), and caffeine (CAF) within a stirred reactor featuring...
Coupling machine learning and theoretical models to compare key properties of biochar in adsorption kinetics rate and maximum adsorption capacity for emerging contaminants.
Bioresource technology
May 1, 2024
Insights into key properties of biochar with a fast adsorption rate and high adsorption capacity are urgent to design biochar as an adsorbent in pollution emergency treatment. Machine learning (ML) incorporating classical theoretical adsorption model...
KINNTREX: a neural network to unveil protein mechanisms from time-resolved X-ray crystallography.
IUCrJ
May 1, 2024
Here, a machine-learning method based on a kinetically informed neural network (NN) is introduced. The proposed method is designed to analyze a time series of difference electron-density maps from a time-resolved X-ray crystallographic experiment. Th...
Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Waste: Gradient Boosting Machine Learning-Assisted Bayesian Optimization for Improved Adsorption Process.
International journal of molecular sciences
Apr 27, 2024
This work aimed to describe the adsorption behavior of Congo red (CR) onto activated biochar material prepared from waste (). The carbon precursor was soaked with phosphoric acid, followed by pyrolysis to convert the precursor into activated biochar...
High-Precision Viral Detection Using Electrochemical Kinetic Profiling of Aptamer-Antigen Recognition in Clinical Samples and Machine Learning.
Angewandte Chemie (International ed. in English)
Apr 9, 2024
High-precision viral detection at point of need with clinical samples plays a pivotal role in the diagnosis of infectious diseases and the control of a global pandemic. However, the complexity of clinical samples that often contain very low viral con...
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