AIMC Topic: Carbon

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Machine Learning-Driven Inverse Design for Low-Carbon and Cost-Effective Organic Acid Leaching of Spent Ternary Lithium Batteries.

Environmental science & technology
Organic acid leaching is an effective and sustainable method for simultaneously recovering critical metals from ternary lithium batteries (T-LIBs). However, current methods overlook the structural impact of organic acids and rely on inefficient trial...

Machine learning-assisted construction of a lignin carbon dots sensor array for detecting food colorants.

Food chemistry
Food safety monitoring is crucial due to the widespread use and potential toxicity of synthetic food colorants. High-sensitivity techniques such as chromatography are routinely employed but require costly equipment and skilled operators. Here we show...

Floc image-driven deep learning enhanced by temporal windows and transformers for carbon emission reduction in drinking water treatment plants.

Water research
Using machine learning (ML) and deep learning (DL) algorithms for precise coagulant dosing in drinking water treatment plants (DWTPs) helps ensure drinking water safety and supports greenhouse gas (GHG) emission reduction. The effectiveness of these ...

Carbon Reporting Practices in the NHS: Emissions and Omissions Relating to Artificial Intelligence.

Journal of medical Internet research
Artificial intelligence (AI) is being rolled out across the UK National Health Service (NHS) to improve efficiency; yet, its carbon footprint is largely invisible within mandatory Green Plan reporting. This work shows where NHS carbon reporting omits...

Design of global climate control based on fuzzy systems with concept of carbon emissions.

PloS one
The global carbon-climate system is a highly complex and dynamic network characterized by multiple feedback loops between interconnected components. Addressing the risks of climate change requires active intervention across these components (Atmosphe...

A machine learning model guided by physical principles for biofilter performance prediction.

Scientific reports
Despite the critical role of biofilters in water quality and sustainability, predicting their performance remains challenging due to the complexity of microbial interactions and limitations of sparse, high-dimensional datasets. Here, we introduce Env...

Rubisco-Centric Strategies for Carbon Conservation in Synthetic Biology.

Journal of agricultural and food chemistry
The escalating global climate crisis urgently demands biomanufacturing technologies with higher carbon efficiency. Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), the central enzyme catalyzing carbon dioxide fixation in the Calvin-Benson c...

Prediction of regional cropland soil organic carbon content and distribution using deep learning: a case study of the Northeast China Plain.

Environmental monitoring and assessment
Soil organic carbon (SOC) is a critical component of soil fertility and plays a significant role in global carbon sequestration. The decline in SOC content across global croplands poses significant challenges to both agricultural productivity and env...

Ratiometric Determination and Discrimination of Oxicams via Dual-Excitation Carbon Dots Assisted by Machine Learning.

Analytical chemistry
Oxicams, a major category of nonsteroidal anti-inflammatory drugs, are widely used in daily life. However, excessive consumption of oxicams can pose significant risks to human health. Herein, we introduce an innovative and highly sensitive fluorescen...

Mapping spatiotemporal distribution of forest carbon density in Xizang, China.

PloS one
Climate warming is a major global challenge, and forests, essential carbon sinks, are critical in mitigating its effects. Forest carbon density is a key parameter in assessing the carbon sinks. Traditional estimating methods of forest carbon density ...