AIMC Topic: Carbon

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A mechanism study on laser-induced breakdown spectroscopy and machine learning-based characterization method for waste organic polymers.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
The method based on machine learning and laser-induced breakdown spectroscopy (LIBS) is effective for rapid characterization of waste organic polymers (WOP). However, the lack of mechanistic interpretability leads to raises concerns regarding its rel...

Portable and intelligent ratio fluorometry and colorimetry for dual-mode detection of dopamine based on B, N-codoped carbon dots and machine learning.

Talanta
A dual-mode approach was developed for dopamine (DA) assay based on boron (B) and nitrogen (N) co-doped carbon dots (B, N-CDs). This platform enabled highly sensitive and specific detection of DA in biological samples through collaborative ratio fluo...

Machine learning assisted paper-based fluorescent sensor array with metal-doped multicolor carbon quantum dots for identification and inactivation of bacteria.

Talanta
Bacterial infection is a thorny threat in a variety of fields, including medicine, environment, food, and agriculture. A multifunctional platform that meets the demands of both bacterial identification and real-time inactivation is urgently needed. T...

Decoding the AI carbon reduction code: Improving corporate carbon performance from the perspective of industry chain spillovers.

Journal of environmental management
Artificial intelligence (AI) technologies serve as a critical instrument in advancing China's dual climate objectives of carbon emissions peaking and carbon neutrality. This study investigates the mechanisms through which AI adoption influences corpo...

Integrating machine learning and nano-QSAR models to predict the oxidative stress potential caused by single and mixed carbon nanomaterials in algal cells.

Environmental toxicology and chemistry
In silico methods are increasingly important in predicting the ecotoxicity of engineered nanomaterials (ENMs), encompassing both individual and mixture toxicity predictions. It is widely recognized that ENMs trigger oxidative stress effects by genera...

Managing waste for production of low-carbon concrete mix using uncertainty-aware machine learning model.

Environmental research
This study introduces an uncertainty-aware AI-driven optimization framework for designing sustainable concrete mixtures that incorporate waste-derived materials. The primary objectives are to reduce global warming potential (GWP) and promote a circul...

Total nitrogen levels as a key constraint on soil organic carbon stocks across Australian agricultural soils.

Environmental research
Understanding how pedoclimatic drivers regulate soil organic carbon (SOC) stock is crucial for gaining insights into terrestrial carbon-climate feedback and thus adaptation to climate change. However, current data-driven SOC predictive models often n...

Afforestation Surpasses Abandonment in the Recovery of Post-Agricultural Soil Organic Carbon in China as Estimated by Machine Learning Models.

Global change biology
The surface soil organic carbon (SOC) dynamics typically follow a trend of initial loss followed by subsequent accumulation after cropland abandonment. However, the timing of SOC stock increase (referred to as the threshold in this study) remains ins...

Artificial intelligence-driven internet of things-based green supply chain for carbon reduction in sustainable manufacturing.

Journal of environmental management
Recent advancements in sustainable business practices and information technology trends have led to the rise of green smart supply chains. These green sustainable supply chains are a novel approach that involves information technology to enhance the ...

Spatiotemporal variations and driving mechanisms of carbon storage in Central Asia: Insights from the PLUS-InVEST models and machine learning.

Journal of environmental management
Against the backdrop of global climate change and rapid socioeconomic advancement, significant land use/cover changes(LUCC) in Central Asia have profoundly impacted terrestrial ecosystem carbon storage(CS). However, the assessment and spatiotemporal ...