We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Deep Learning Approaches for Microplastic Identification in Microscopic Water Samples
No summary available — this paper's abstract is not included in the open metadata provided by the publisher. Learn why →
More Papers Like This
Advanced Microplastic Identification in Marine Environments via Hybrid Deep Learning
AI summary Read the abstract
Researchers propose a hybrid deep learning architecture combining 3D convolutional neural networks and Vision Transformers applied to hyperspectral imagery to detect and classify microplastics in turbid marine environments, capturing both local spectral signatures and global contextual patterns that single-model approaches miss.
Application of Artificial Intelligence to Improve Microplastic Identification
AI summary Read the abstract
This review examines how artificial intelligence -- including machine learning and computer vision approaches -- can improve the identification, classification, and measurement of microplastics during environmental sampling and analysis. AI-assisted identification addresses a major bottleneck in microplastic research by reducing the time and expertise required to process large sample volumes, enabling faster and more consistent data generation needed to assess human health exposure at scale.
FasterRes-FPN: A Deep Learning Approach for Microplastic Pollution Surveillance in Water Bodies
AI summary Read the abstract
Researchers developed FasterRes-FPN, a deep learning model for automated microplastic detection in water bodies, offering a faster and more cost-effective alternative to traditional microscopy and spectroscopy methods for large-scale aquatic pollution surveillance.
MicroDetect-Net (MDN): Leveraging Deep Learning to Detect Microplastics in Clam Blood, a Step Towards Human Blood Analysis
AI summary Read the abstract
Researchers developed MicroDetect-Net, a deep learning model trained to detect microplastics in clam blood, demonstrating a pathway toward automated detection in human blood samples as plastic production exceeds 368 million tons annually and microplastics are found across all environmental and biological matrices.
Microplastic Detection and Analysis in Water Samples
AI summary Read the abstract
Thermoanalytical and spectroscopic methods enable detection and quantification of microplastics in water samples, which is critical for tracing pollution sources and protecting drinking water and desalination infrastructure. Robust water monitoring methods are essential for assessing human exposure risks from microplastic-contaminated water supplies.
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.