AIMC Topic: Plastics
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Machine learning constructs the microstructure and mechanical properties that accelerate the development of CFRP pyrolysis for carbon-fiber recycling.
Waste management (New York, N.Y.)
Sep 10, 2024
The increasing use of carbon-fiber-reinforced plastic (CFRP) has led to its post-end-of-life recycling becoming a research focus. Herein, we studied the macroscopic and microscopic characteristics of recycled carbon fiber (rCF) during CFRP pyrolysis ...
Efficient plastic detection in coastal areas with selected spectral bands.
Marine pollution bulletin
Sep 7, 2024
Marine plastic pollution poses significant ecological, economic, and social challenges, necessitating innovative detection, management, and mitigation solutions. Spectral imaging and optical remote sensing have proven valuable tools in detecting and ...
Towards reliable data: Validation of a machine learning-based approach for microplastics analysis in marine organisms using Nile red staining.
Marine pollution bulletin
Sep 5, 2024
Microplastic (MP) research faces challenges due to costly, time-consuming, and error-prone analysis techniques. Additionally, the variability in data quality across studies limits their comparability. This study addresses the critical need for reliab...
Plastic particles and fluorescent brightener co-modify Chlorella pyrenoidosa photosynthesis and a machine learning approach predict algae growth.
Journal of hazardous materials
Aug 2, 2024
Global release of plastics exerts various impacts on the ecological cycle, particularly on primary photosynthesis, while the impacts of plastic additives are unknown. As a carrier of fluorescent brightener, plastic particles co-modify Chlorella pyren...
Data modeling analysis of GFRP tubular filled concrete column based on small sample deep meta learning method.
PloS one
Jul 10, 2024
The meta-learning method proposed in this paper addresses the issue of small-sample regression in the application of engineering data analysis, which is a highly promising direction for research. By integrating traditional regression models with opti...
Quantification of litter in cities using a smartphone application and citizen science in conjunction with deep learning-based image processing.
Waste management (New York, N.Y.)
Jun 28, 2024
Cities are a major source of litter pollution. Determination of the abundance and composition of plastic litter in cities is imperative for effective pollution management, environmental protection, and sustainable urban development. Therefore, here, ...
Bioplastic derived from corn stover: Life cycle assessment and artificial intelligence-based analysis of uncertainty and variability.
The Science of the total environment
Jun 27, 2024
Exploring feasible and renewable alternatives to reduce dependency on traditional fossil-based plastics is critical for sustainable development. These alternatives can be produced from biomass, which may have large uncertainties and variabilities in ...
The use of vibrational spectroscopy and supervised machine learning for chemical identification of plastics ingested by seabirds.
Journal of hazardous materials
Jun 21, 2024
Plastic pollution is now ubiquitous in the environment and represents a growing threat to wildlife, who can mistake plastic for food and ingest it. Tackling this problem requires reliable, consistent methods for monitoring plastic pollution ingested ...
Application of improved machine learning in large-scale investigation of plastic waste distribution in tourism Intensive artificial coastlines.
Environmental pollution (Barking, Essex : 1987)
May 31, 2024
Oceans are ultimately a sink of plastic waste. Complex artificial coastlines pose remarkable challenges for coastal plastic waste monitoring. With the development of machine learning methods, high detection accuracy can be achieved; however, many fal...
Predictive modeling of plastic pyrolysis process for the evaluation of activation energy: Explainable artificial intelligence based comprehensive insights.
Journal of environmental management
May 17, 2024
Pyrolysis, a thermochemical conversion approach of transforming plastic waste to energy has tremendous potential to manage the exponentially increasing plastic waste. However, understanding the process kinetics is fundamental to engineering a sustain...
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