0
Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Detection Methods Sign in to save

Dataset for high-frequency ultrasound–based microplastic identification and size estimation

Zenodo (CERN European Organization for Nuclear Research) 2026
Navid Zarrabi, Eric M. Strohm, Hadi Rezvani, Matthew Lisondra, Nariman Yousefi, Sajad Saeedi, M.C. Kolios

Summary

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).

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.

Share this paper