AIMC Topic: Adsorption

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Artificial intelligence modeling and experimental studies of oily pollutants uptake from water using ZIF-8/carbon fiber nanostructure.

Journal of environmental management
In this study, the experimental and modeling of oily pollutants (crude oil, asphaltene, and maltene) uptake by ZIF-8/carbon fiber nanostructure was investigated. The influence of pollutant type, concentration, ionic strength, and sorption time on upt...

A novel interpretable machine learning and metaheuristic-based protocol to predict and optimize ciprofloxacin antibiotic adsorption with nano-adsorbent.

Journal of environmental management
The existence of antibiotics in water sources poses substantial hazards to both the environment and public health. To effectively monitor and combat this problem, accurate predictive models are essential. This research focused on employing machine le...

Evaluating machine learning performance in predicting sodium adsorption ratio for sustainable soil-water management in the eastern Mediterranean.

Journal of environmental management
Soil salinization is a critical global issue for sustainable agriculture, impacting crop yields and posing a threat to achieving the Sustainable Development Goal (SDG) of ensuring food security. It is necessary to monitor it in detail and uncover its...

Activated biochar production from young coconut waste (Cocos nucifera) as bioadsorbent: a pathway through Artificial Neural Network (ANN) optimization.

Environmental monitoring and assessment
This pioneering work explores the immense potential of young coconut waste, a continuously marginalized residue of the food and beverage industry, to serve as an indispensable feedstock in the production of biochar. Through an examination of the key ...

Effective Removal of Selenium from Aqueous Solution using Iron-modified Dolochar: A Comprehensive Study and Machine Learning Predictive Analysis.

Environmental research
Selenium (Se) is an essential micronutrient for human beings, but excess concentration can lead to many health issues and degrade the ecosystem. This study focuses on the removal of selenium from an aqueous solution using iron-doped dolochar. SEM, ED...

Predicting the performance of lithium adsorption and recovery from unconventional water sources with machine learning.

Water research
Selective lithium (Li) recovery from unconventional water sources (UWS) (e.g., shale gas waters, geothermal brines, and rejected seawater desalination brines) using inorganic lithium-ion sieve (LIS) materials can address Li supply shortages and distr...

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

Journal of environmental management
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...

Leveraging experimental and computational tools for advancing carbon capture adsorbents research.

Environmental science and pollution research international
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...

Exploiting Metal-Organic Frameworks for Vinylidene Fluoride Adsorption: From Force Field Development, Computational Screening to Machine Learning.

Environmental science & technology
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...

Refining hydrogel-based sorbent design for efficient toxic metal removal using machine learning-Bayesian optimization.

Journal of hazardous materials
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...