AIMC Topic: Plastics

Clear Filters Showing 1 to 10 of 94 articles

Leveraging Retrieval-Augmented Generation to Accelerate Discoveries on Mealworm Larvae and Plastic Degradation.

Environmental science & technology
Large language models (LLMs) are transforming broad research areas, yet concerns about their trustworthiness remain. This study explored the use of Retrieval-Augmented Generation (RAG) to improve LLMs' knowledge extraction in the field of mealworm-me...

Pioneering approaches to plastic biodegradation and upcycling for sustainability.

Environmental monitoring and assessment
Plastic pollution has become one of the most significant threats to the environment and human health of the twenty-first century, with more than 300 million tons of waste generated annually, and conventional disposal methods are inadequate. To addres...

HAttFFNN: Hybridized attention mechanism-based feedforward neural network deep learning model for the plastic material classification of three stage materials on spectroscopic data.

PloS one
Classification of plastic materials based on spectroscopic data is a very crucial task in a variety of applications, including automated recycling, environmental monitoring, quality control in manufacturing, quality control of products, and analysis ...

Using XGBoost and memetic programming to identify hotspots of sediment plastic pollution.

Environmental pollution (Barking, Essex : 1987)
Despite growing global initiatives on sustainable plastic management, less than 10 % of plastic waste is effectively recycled, resulting in widespread environmental dispersion and pollution. This study examines the relative influence of topographic, ...

Unveiling Hidden Health Risks: Machine Learning Enhanced Modeling of Plastic Additive Release Kinetics in Fresh Produce Packaging.

Environmental science & technology
Fresh produce packaging (FPP) plays a critical role in protecting fruits and vegetables from various environmental factors. However, the presence, migration, and human health risks of additives in FPP have received limited attention. This study inves...

Emerging technologies for assessing the occurrence, fate, effects, and remediation of plastics in the environment.

Environmental monitoring and assessment
Plastic pollution and contamination originates from raw material handling, polymerization, compounding, and fabrication, contributing to environmental accumulation. Advanced analytical techniques such as Fourier transform infrared, Raman spectroscopy...

Zero-shot and few-shot multimodal plastic waste classification with vision-language models.

Waste management (New York, N.Y.)
The construction sector is a large consumer of plastic, generating substantial volumes of plastic waste. Effective recycling of this waste requires accurate classification, as different plastic materials undergo distinct recycling processes to retain...

Efficient and anti-interference plastic classification method suitable for one-shot learning based on laser induced breakdown spectroscopy.

Chemosphere
Efficient recycling of plastics is critical for environmental sustainability. In this work, an efficient and anti-interference method for plastic classification based on one-shot learning and laser-induced breakdown spectroscopy (LIBS) was proposed. ...

Decoding the Plastic Patch: Exploring the Global Microplastic Distribution in the Surface Layers of Marine Regions with Interpretable Machine Learning.

Environmental science & technology
The marine environment is grappling with microplastic (MP) pollution, necessitating an understanding of its distribution patterns, influencing factors, and potential ecological risks. However, the vast area of the ocean and budgetary constraints make...

Machine learning-assisted prediction of gas production during co-pyrolysis of biomass and waste plastics.

Waste management (New York, N.Y.)
A general method for predicting gas yield is crucial in biomass and plastics co-pyrolysis. This study employed two machine learning methods to forecast gas yield in co-pyrolysis. Comparing the predictive performance of Support Vector Regression (SVR)...