We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Dataset for high-frequency ultrasound–based microplastic identification and size estimation
AI summary Read the abstract
Researchers created a labeled dataset of high-frequency ultrasound signals from microplastic microspheres of different materials and sizes to support machine-learning-based detection methods. The dataset enables AI models to identify and size microplastic particles non-invasively, which could improve real-time monitoring of microplastics in water and food systems.
This dataset contains high-frequency ultrasound measurements acquired from controlled experiments on microplastic microspheres of different materials and size ranges. The data support research on ultrasound-based detection, material identification, and size estimation of microplastic particles. Raw data The raw data consist of three-dimensional tensors representing the spatial and temporal structure of the recorded ultrasound signals, with dimensions corresponding to the lateral scan coordinates (x, y) and time (t). These tensors were acquired over a defined scan area for samples containing microplastic microspheres. The raw radio-frequency (RF) ultrasound signals are provided in MATLAB (.mat) format and are stored in the raw_data.zip archive. These files contain the original, unprocessed measurements recorded during the experiments. Processed and labeled data Particle-specific signals isolated using a peak-based extraction procedure are stored in the all_labeled_signals.csv file. This file contains signal representations derived from the raw measurements together with associated material and particle size labels. Each signal entry is assigned a unique particle identifier, which enables signals originating from the same particle to be grouped and traced back to the corresponding raw measurements. Intended use The dataset is intended to support the development, evaluation, and benchmarking of machine-learning methods for microplastic characterization using ultrasound.
More Papers Like This
High-frequency ultrasound combined with deep learning enables identification and size estimation of microplastics
AI summary Read the abstract
Researchers developed a high-frequency ultrasound method combined with deep learning — using a 1D convolutional neural network on spectral and temporal echo features — to identify microplastic material type and estimate particle size, offering a faster and non-destructive alternative to FTIR and Raman spectroscopy.
Dataset for high-frequency ultrasound–based microplastic identification and size estimation
AI summary Read the abstract
This is a dataset release containing high-frequency ultrasound measurements from controlled microplastic experiments, supporting the companion research on ultrasound-based microplastic identification and size estimation (related to entries 1060 and 1074).
High-frequency ultrasound combined with deep learning enables identification and size estimation of microplastics
AI summary Read the abstract
Scientists developed a new method using sound waves and artificial intelligence to quickly detect tiny plastic particles (microplastics) in the environment with over 97% accuracy. This technology could help us better monitor microplastic pollution in water and food sources, which is important since these particles can end up in our bodies through what we eat and drink. The new method is much faster than current testing approaches, making it easier to track plastic pollution on a large scale.
Ultrasonic and Artificial Intelligence-Based Detection of Microplastics in Aquatic Environments
AI summary Read the abstract
This review examines how ultrasonic sensing combined with artificial intelligence — including machine learning and deep learning — can detect and characterize microplastics in aquatic environments, offering a scalable and high-throughput alternative to conventional FTIR and Raman spectroscopy methods.
A Microwave-Based Sensing Platform for Microplastic Detection and Quantification: A Machine Learning-Assisted Approach
AI summary Read the abstract
Researchers developed a low-cost microwave sensor combined with machine learning to detect and quantify microplastics in water and identify polymer types in unknown samples. The platform achieved the highest sensitivity reported among microwave-based approaches for microplastic detection, offering a promising low-cost alternative to spectroscopy-based methods.
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.