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Carbon

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Nanomaterial Texture-Based Machine Learning of Ciprofloxacin Adsorption on Nanoporous Carbon.

International journal of molecular sciences
Drug substances in water bodies and groundwater have become a significant threat to the surrounding environment. This study focuses on the ability of the nanoporous carbon materials to remove ciprofloxacin from aqueous solutions under specific experi...

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...

Machine learning prediction of fundamental sewage sludge biochar properties based on sludge characteristics and pyrolysis conditions.

Chemosphere
Sewage sludge biochar (SSBC) has significant potential for resource recovery from sewage sludge (SS) and has been widely studied and applied across various fields. However, the variability in SSBC properties, resulting from the diverse nature of SS a...

Developing novel spectral indices for precise estimation of soil pH and organic carbon with hyperspectral data and machine learning.

Environmental monitoring and assessment
Accurate soil pH and soil organic carbon (SOC) estimations are vital for sustainable agriculture, as pH affects nutrient availability, and SOC is crucial for soil health and fertility. Hyperspectral imaging provides a faster, non-destructive, and eco...

Can big data policy drive urban carbon unlocking efficiency? A new approach based on double machine learning.

Journal of environmental management
In recent years, data has increasingly become the "new oil" for 21st-century economic development. However, there is still a gap in how the development of big data promotes the improvement of urban carbon unlocking efficiency (UCUE). Utilizing advanc...

High-throughput point-of-care serum iron testing utilizing machine learning-assisted deep eutectic solvent fluorescence detection platform.

Journal of colloid and interface science
In this study, a high-throughput point-of-care testing (HT-POCT) system for detecting serum iron was developed using a hydrophobic deep eutectic solvent (HDES) fluorescence detection platform. This machine learning-assisted portable platform enables ...

Global forecasting of carbon concentration through a deep learning spatiotemporal modeling.

Journal of environmental management
Given the global urgency to mitigate climate change, a key action is the development of effective carbon concentration reduction policies. To this end, an influential factor is the availability of accurate predictions of carbon concentration trends. ...

Interpretable causal machine learning optimization tool for improving efficiency of internal carbon source-biological denitrification.

Bioresource technology
Interpretable causal machine learning (ICML) was used to predict the performance of denitrification and clarify the relationships between influencing factors and denitrification. Multiple models were examined, and XG-Boost model provided the best pre...

Predictions of the Optical Properties of Brown Carbon Aerosol by Machine Learning with Typical Chromophores.

Environmental science & technology
The linkages between BrC optical properties and chemical composition remain inadequately understood, with quantified chromophores explaining less than 25% of ambient aerosol light absorption. This study characterized 38 typical chromophores in aeroso...