We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Deep Learning-Powered Dark-Field Microscopy for Simultaneous Size and Concentration Analysis of Nanoplastics in Water
AI summary Read the abstract
Researchers developed a convolutional neural network (CNN)-powered dark-field microscopy approach for simultaneously measuring the size and concentration of nanoplastics in water samples. The system achieved accurate size and count analysis for particles as small as tens of nanometers, demonstrating that AI-enhanced dark-field microscopy can overcome the detection limits of conventional light microscopy for nanoplastic quantification.
Nanoplastics have become a significant environmental and health concern due to their widespread presence. Accurately analyzing both size and concentration of nanoplastics is essential for assessing their environmental behavior and potential toxicity; however, this remains a significant challenge. In this study, we developed a novel approach of convolutional neural networks (CNNs) powered dark-field microscopy (DFM) to achieve concurrent size and concentration analysis of nanoplastics. DFM images of polystyrene nanoplastics (PSNPs) down to 150 nm were facilely acquired based on their scattering characteristics, which were subsequently extracted and studied by combining contour recognition algorithms with a streamlined VGGNet. The established approach achieves high accuracy (over 0.99 on test sets) and sensitivity (limit of detection: 1.7 ng mL-1) in identifying PSNPs ranging from 150 to 600 nm. Spiked recovery results yield 93.55-103.8% recovery rates across 200 to 400 nm PSNPs, demonstrating the ability of the developed method to simultaneously determine size and concentration of nanoplastics. Therefore, the proposed strategy can offer a reliable and visual alternative for nanoplastics analysis with potential applications in environmental and biological monitoring.
More Papers Like This
Lensless shadow microscopy-based shortcut analysis strategy for fast quantification of microplastic fibers released to water
AI summary Read the abstract
Researchers developed a rapid analysis system for quantifying microplastic fibers in water using a high-resolution lensless shadow microscope combined with deep learning algorithms. The approach replaces the slow manual counting process with automated imaging on a chip, significantly increasing both speed and accuracy. The study offers a practical tool for routine monitoring of microplastic fiber pollution in water treatment and environmental settings.
Development of Microplastics Detector and Quantifier Utilizing Deep Learning Based Algorithm
AI summary Read the abstract
Researchers developed a microplastics detector and quantifier using deep learning-based image analysis, training a neural network to identify and count microplastic particles in microscopic images. The system achieved high accuracy and offers a faster, more objective alternative to manual counting.
Quantitative analysis of microplastics in water environments based on Raman spectroscopy and convolutional neural network
AI summary Read the abstract
Researchers developed a method combining Raman spectroscopy with a convolutional neural network to measure microplastic concentrations in water. The approach achieved high accuracy across six different sizes of polyethylene particles in five real-world water environments, outperforming other machine learning models and offering a practical tool for quantitative microplastic monitoring.
Efficient Microplastic Detection in Water Using ResNet50 and Fluorescence Imaging
AI summary Read the abstract
Researchers applied a ResNet50 deep learning model to fluorescence microscopy images of water samples, achieving high-accuracy classification of microplastics, demonstrating that deep learning can efficiently automate microplastic identification from microscopy data.
Rapid Classification of Microplastics by Using the Application of a Convolutional Neural Network
AI summary Read the abstract
Researchers used convolutional neural networks (deep learning) to automatically classify microplastic particles in microscopy images into four categories: fragments, pellets, films, and fibers. The models achieved high classification accuracy, reducing the time and labor needed for manual identification. Automated AI classification could greatly accelerate large-scale microplastic monitoring programs.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.