AIMC Topic: Adsorption

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Prediction of Cr(VI) and As(V) adsorption on goethite using hybrid surface complexation-machine learning model.

Water research Apr 6, 2024
This study aimed to develop surface complexation modeling-machine learning (SCM-ML) hybrid model for chromate and arsenate adsorption on goethite. The feasibility of two SCM-ML hybrid modeling approaches was investigated. Firstly, we attempted to uti...
Water Pollutants, Chemical Adsorption Machine Learning Kinetics Minerals Chromium Arsenic Iron Compounds Arsenates
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Amino-functionalized novel biosorbent for effective removal of fluoride from water: process optimization using artificial neural network and mechanistic insights.

Environmental science and pollution research international Apr 5, 2024
Aqueous fluoride ( ) pollution is a global threat to potable water security. The present research envisions the development of novel adsorbents from indigenous Limonia acidissima L. (fruit pericarp) for effective aqueous defluoridation. The adsorben...
Water Fluorides Water Pollutants, Chemical Adsorption Kinetics Neural Networks, Computer Water Purification
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Machine learning and statistical physics modeling of tetracycline adsorption using activated carbon derived from Cynometra ramiflora fruit biomass.

Environmental research Apr 1, 2024
The current investigation reports the usage of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN), the two recognized machine learning techniques in modelling tetracycline (TC) adsorption onto Cynometra ramiflora fruit ...
Machine Learning Biomass Neural Networks, Computer Charcoal Water Pollutants, Chemical Fruit Adsorption Tetracycline
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Application of machine learning in prediction of Pb adsorption of biochar prepared by tube furnace and fluidized bed.

Environmental science and pollution research international Mar 20, 2024
Data mining by machine learning (ML) has recently come into application in heavy metals purification from wastewater, especially in exploring lead removal by biochar that prepared using tube furnace (TF-C) and fluidized bed (FB-C) pyrolysis methods. ...
Charcoal Machine Learning Lead Water Pollutants, Chemical Adsorption
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Invariant Molecular Representations for Heterogeneous Catalysis.

Journal of chemical information and modeling Jan 10, 2024
Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory ...
Adsorption Catalysis Animals Cattle Neural Networks, Computer Machine Learning
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The adsorption and release mechanism of different aged microplastics toward Hg(II) via batch experiment and the deep learning method.

Chemosphere Dec 30, 2023
Aged microplastics are ubiquitous in the aquatic environment, which inevitably accumulate metals, and then alter their migration. Whereas, the synergistic behavior and effect of microplastics and Hg(II) were rarely reported. In this context, the adso...
Microplastics Mercury Adsorption Plastics Deep Learning Water Pollutants, Chemical
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Chitosan-based magnetic bioadsorbent beads from eucalyptus sawdust waste for the Direct Violet-51 dye remediation: Eco-friendly strategy and statistical optimization.

International journal of biological macromolecules Oct 30, 2023
In the present study, a new application was proposed for the eucalyptus sawdust waste, which is an environmental passive. Three adsorbent materials composed of chitosan (CS), sawdust (CSW), and magnetic beads (CSWF) were developed and used for the Di...
Magnetic Phenomena Chitosan Water Pollutants, Chemical Water Purification Adsorption Kinetics Eucalyptus Water Hydrogen-Ion Concentration
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Transformer-based deep learning models for adsorption capacity prediction of heavy metal ions toward biochar-based adsorbents.

Journal of hazardous materials Oct 12, 2023
Biochar adsorbents synthesized from food and agricultural wastes are commonly applied to eliminate heavy metal (HM) ions from wastewater. However, biochar's diverse characteristics and varied experimental conditions make the accurate estimation of th...
Adsorption Charcoal Ions Metals, Heavy Deep Learning
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Data-Driven Prediction of Configurational Stability of Molecule-Adsorbed Heterogeneous Catalysts.

Journal of chemical information and modeling Sep 15, 2023
The design of new heterogeneous catalysts that convert small molecules into valuable chemicals is a key challenge for constructing sustainable energy systems. Density functional theory (DFT)-based design frameworks based on the understanding of molec...
Catalysis Adsorption Machine Learning Density Functional Theory
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Evaluation of reproducible cryogel preparation based on automated image analysis using deep learning.

Journal of biomedical materials research. Part A Jun 22, 2023
Cryogels represent a class of porous sponge-like materials possessing unique properties including high-fidelity reproduction of tissue structure and maximized permeability. Their architecture is mainly based on an interconnected network of macropores...
Deep Learning Adsorption Reproducibility of Results Porosity Cryogels Microscopy, Electron, Scanning
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