AIMC Topic: Microplastics

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Artificial intelligence in microplastic detection and pollution control.

Environmental research
The rising prevalence of microplastics (MPs) in various ecosystems has increased the demand for advanced detection and mitigation strategies. This review examines the integration of artificial intelligence (AI) with environmental science to improve m...

Machine learning-driven QSAR models for predicting the cytotoxicity of five common microplastics.

Toxicology
In the field of microplastics (MPs) toxicity prediction, machine learning (ML) computer simulation techniques are showing great potential. In this study, six ML algorithms were utilized to predict the toxicity of MPs on BEAS-2B cells based on quantit...

Adsorption behavior and mechanism of heavy metals onto microplastics: A meta-analysis assisted by machine learning.

Environmental pollution (Barking, Essex : 1987)
Microplastics (MPs) have the potential to adsorb heavy metals (HMs), resulting in a combined pollution threat in aquatic and terrestrial environments. However, due to the complexity of MP/HM properties and experimental conditions, research on the ads...

Appraisal of microplastic pollution and its related risks for urban indoor environment in Bangladesh using machine learning and diverse risk evolution indices.

Environmental pollution (Barking, Essex : 1987)
The widespread presence of Microplastics (MPs) is increasing in the indoor environment due to increasing annual plastic usage, which is becoming a global threat to human health. Therefore, this is the first research in Bangladesh to identify, and cha...

Applicability of machine learning techniques to analyze Microplastic transportation in open channels with different hydro-environmental factors.

Environmental pollution (Barking, Essex : 1987)
This research utilized machine learning to analyze experiments conducted in an open channel laboratory setting to predict microplastic transport with varying discharge, velocity, water depth, vegetation pattern, and microplastic density. Four machine...

Differentiating Microplastics from Natural Particles in Aqueous Suspensions Using Flow Cytometry with Machine Learning.

Environmental science & technology
Microplastics (MPs) in natural waters are heterogeneously mixed with other natural particles including algal cells and suspended sediments. An easy-to-use and rapid method for directly measuring and distinguishing MPs from other naturally present col...

Data driven AI (artificial intelligence) detection furnish economic pathways for microplastics.

Journal of contaminant hydrology
Microplastics pollution is killing human life, contaminating our oceans, and lasting for longer in the environment than it is used. Microplastics have contaminated the geochemistry and turned the water system into trash barrel. Its detection in water...

Magnetic Microrobot Swarms with Polymeric Hands Catching Bacteria and Microplastics in Water.

ACS nano
The forefront of micro- and nanorobot research involves the development of smart swimming micromachines emulating the complexity of natural systems, such as the swarming and collective behaviors typically observed in animals and microorganisms, for e...

Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection.

Environmental science & technology
For the first time, we present a much-needed technology for the in situ and real-time detection of nanoplastics in aquatic systems. We show an artificial intelligence-assisted nanodigital in-line holographic microscopy (AI-assisted nano-DIHM) that au...

Innovative methods for microplastic characterization and detection: Deep learning supported by photoacoustic imaging and automated pre-processing data.

Journal of environmental management
Plastic products' widespread applications and their non-biodegradable nature have resulted in the continuous accumulation of microplastic waste, emerging as a significant component of ecological environmental issues. In the field of microplastic dete...