AIMC Topic: Adsorption

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Machine learning-driven prediction of phosphorus adsorption capacity of biochar: Insights for adsorbent design and process optimization.

Journal of environmental management Sep 4, 2024
Phosphorus (P) pollution in aquatic environments poses significant environmental challenges, necessitating the development of effective remediation strategies, and biochar has emerged as a promising adsorbent for P removal at the cost of extensive re...
Water Purification Water Pollutants, Chemical Charcoal Phosphorus Adsorption Machine Learning Algorithms
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Leveraging experimental and computational tools for advancing carbon capture adsorbents research.

Environmental science and pollution research international Sep 3, 2024
CO emissions have been steadily increasing and have been a major contributor for climate change compelling nations to take decisive action fast. The average global temperature could reach 1.5 °C by 2035 which could cause a significant impact on the e...
Carbon Machine Learning Climate Change Carbon Dioxide Adsorption Carbon Sequestration
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Exploiting Metal-Organic Frameworks for Vinylidene Fluoride Adsorption: From Force Field Development, Computational Screening to Machine Learning.

Environmental science & technology Sep 1, 2024
Metal-organic frameworks (MOFs) represent a distinctive class of nanoporous materials with considerable potential across a wide range of applications. Recently, a handful of MOFs has been explored for the storage of environmentally hazardous fluorina...
Machine Learning Adsorption Metal-Organic Frameworks
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Refining hydrogel-based sorbent design for efficient toxic metal removal using machine learning-Bayesian optimization.

Journal of hazardous materials Aug 30, 2024
Hydrogel-based sorbents show promise in the removal of toxic metals from water. However, optimizing their performance through conventional trial-and-error methods is both costly and challenging due to the inherent high-dimensional parameter space ass...
Adsorption Metals Bayes Theorem Metals, Heavy Water Pollutants, Chemical Hydrogels Machine Learning Water Purification
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Deep learning artificial neural network framework to optimize the adsorption capacity of 3-nitrophenol using carbonaceous material obtained from biomass waste.

Scientific reports Aug 30, 2024
The presence of toxic chemicals in water, including heavy metals like mercury and lead, organic pollutants such as pesticides, and industrial chemicals from runoff and discharges, poses critical public health and environmental risks leading to severe...
Neural Networks, Computer Deep Learning Water Pollutants, Chemical Adsorption Water Purification Biomass Carbon Nitrophenols
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Synthesis and characterization of Fe(III)-doped beta-cyclodextrin-grafted chitosan cryogel beads for adsorption of diclofenac in aqueous solutions: Adsorption experiments and deep-learning modeling.

International journal of biological macromolecules Aug 28, 2024
Diclofenac (DCF) is frequently detected in aquatic environments, emphasizing the critical need for its efficient removal globally. Here, we present the synthesis of Fe(III)-doped β-CD-grafted chitosan (Fe/β-CD@CS) cryogel beads designed for adsorbing...
Cryogels Solutions Diclofenac Chitosan Iron Hydrogen-Ion Concentration beta-Cyclodextrins Kinetics Water Pollutants, Chemical Adsorption Deep Learning Water Water Purification
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Adsorption behavior and mechanism of heavy metals onto microplastics: A meta-analysis assisted by machine learning.

Environmental pollution (Barking, Essex : 1987) Jul 29, 2024
Microplastics (MPs) have the potential to adsorb heavy metals (HMs), resulting in a combined pollution threat in aquatic and terrestrial environments. However, due to the complexity of MP/HM properties and experimental conditions, research on the ads...
Metals, Heavy Adsorption Machine Learning Water Pollutants, Chemical Microplastics
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Machine learning for the adsorptive removal of ciprofloxacin using sugarcane bagasse as a low-cost biosorbent: comparison of analytic, mechanistic, and neural network modeling.

Environmental science and pollution research international Jul 22, 2024
Contamination with traces of pharmaceutical compounds, such as ciprofloxacin, has prompted interest in their removal via low-cost, efficient biomass-based adsorption. In this study, classical models, a mechanistic model, and a neural network model we...
Ciprofloxacin Saccharum Neural Networks, Computer Water Pollutants, Chemical Machine Learning Adsorption Cellulose
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Sustainable utilization of FeO-modified activated lignite for aqueous phosphate removal and ANN modeling.

Environmental research Jul 14, 2024
Lignites are widely available and cost-effective in many countries. Sustainable methods for their utilization drive innovation, potentially advancing environmental sustainability and resource efficiency. In the present study, FeO (∼25.1 nm) supported...
Water Pollutants, Chemical Adsorption Ferric Compounds Coal Kinetics Phosphates Neural Networks, Computer Water Purification Hydrogen-Ion Concentration
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Predicting and refining acid modifications of biochar based on machine learning and bibliometric analysis: Specific surface area, average pore size, and total pore volume.

The Science of the total environment Jul 6, 2024
Acid-modified biochar is a modified biochar material with convenient preparation, high specific surface area, and rich pore structure. It has great potential for application in the heavy metal remediation, soil amendments, and carrying catalysts. Spe...
Machine Learning Bibliometrics Charcoal Porosity Adsorption
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