AIMC Topic: Adsorption

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Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials.

Scientific reports
Membrane incompatibility poses significant health risks, including severe complications and potential fatality. Surface modification of membranes has emerged as a pivotal technology in the membrane industry, aiming to improve the hemocompatibility an...

An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach.

Scientific reports
This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inferen...

Exposure experiments and machine learning revealed that personal care products can significantly increase transdermal exposure of SVOCs from the environment.

Journal of hazardous materials
We investigated the impacts of personal care products (PCPs) on dermal exposure to semi-volatile organic compounds (SVOCs), including phthalates, organophosphate esters, polycyclic aromatic hydrocarbons (PAHs), ultraviolet filters, and p-phenylenedia...

Application of supervised learning models for enhanced lead (II) removal from wastewater via modified cellulose nanocrystals (CNCs).

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
Heavy metal ions are acknowledged to impact the environment and human health adversely. CNCs are effective materials for removing heavy metal ions in industrial applications and process innovations since they can be used in static and dynamic adsorpt...

Artificial neural networks to estimate the sorption and desorption of the herbicide linuron in Brazilian soils.

Environmental pollution (Barking, Essex : 1987)
Generally, herbicides used in Brazil follow manufacturer's recommendations, which often do not consider soil attributes. Statistical models that include the physicochemical properties of the soil involved in herbicide retention processes could enable...

Accelerated Screening of Highly Sensitive Gas Sensor Materials for Greenhouse Gases Based on DFT and Machine Learning Methods.

ACS sensors
Greenhouse gases (GHGs) have caused great harm to the ecological environment, so it is necessary to screen gas sensor materials for detecting GHGs. In this study, we propose an ideal gas sensor design strategy with high screening efficiency and low c...

Deep eutectic solvent-modified polyvinyl alcohol/chitosan thin film membrane for dye adsorption: Machine learning modeling, experimental, and density functional theory calculations.

International journal of biological macromolecules
The polyvinyl alcohol/chitosan (PVA/CS) thin film membrane was modified using a deep eutectic solvent (DES) to enhance its adsorption capability and mechanical strength for the removal of brilliant green (BG) dye. Batch adsorption experiments, machin...

Efficient and stable extraction of nano-sized plastic particles enabled by bio-inspired magnetic "robots" in water.

Environmental pollution (Barking, Essex : 1987)
In this research, a rationally-designed strategy was employed to address the crucial issue of removing nano-plastics (NPs) from aquatic environments, which was based on fabricating sea urchin-like structures of FeO magnetic robots (MagRobots). Throug...

MGT: Machine Learning Accelerates Performance Prediction of Alloy Catalytic Materials.

Journal of chemical information and modeling
The application of deep learning technology in the field of materials science provides a new method for predicting the adsorption energy of high-performance alloy catalysts in hydrogen evolution reactions and material discovery. The activity and sele...

Predicting the adsorption of ammonia nitrogen by biochar in water bodies using machine learning strategies: Model optimization and analysis of key characteristic variables.

Environmental research
Biochar adsorption technology has been widely used to remove ammonia nitrogen from water bodies. However, existing methods for predicting adsorption efficiency often lack sufficient accuracy and practical usability. This study evaluated eight machine...