Machine learning-based prediction of adsorption capacity of metal-doped and undoped activated carbon: Assessing the role of metal doping.

This research developed five ensemble-based machine learning (ML) models to predict the adsorption capacity of both pristine and metal-doped activated carbon (AC) and identified key influencing features. Results indicated that Extreme Gradient Boosting (XGB) model provided the most accurate predictions for both types of AC, with metal-doped AC exhibiting 1.7 times higher adsorption capacity than pristine AC showing 254.

Long-Term Examination of Water Chemistry Changes Following Treatment of Cyanobacterial Bloom with Coagulants and Minerals.

The abundant growth in cyanobacterial blooms poses severe ecological threats with a high risk to aquatic organisms and global public health. Control of cyanobacterial blooms involves spraying cyanobacteria removal materials, including coagulants. However, little is known about the fate of the coagulated-cyanobacteria-laden water.

The removal of Microcystis ichthyoblabe cells and its hepatotoxin microcystin-LR during electrooxidation process using Pt/Ti electrodes.

Electrooxidation is widely used to remove harmful organic and inorganic substances as well as pathogenic microorganisms. This study investigates the removal of Microcystis ichthyoblabe cells and their hepatotoxin microcystin-LR by the electrooxidation process using Pt/Ti electrodes. Additionally, the morphology changes and cell sizes were determined by scanning electron microscopy and a particle size analyzer, respectively.