All-optical Mie void sensor for complex nanoplastic mixtures

The fragmentation and direct release of plastic debris have emerged as a pressing ecological concern. Once dispersed, these particles infiltrate food webs, accumulate within organisms, and bind toxic co-contaminants, posing long-term risks to ecosystems and human health. Despite growing awareness, the characterization of nanoplastics remains highly challenging. Moreover, obtaining additional information, such as particle shape or material composition, further exacerbates these detection hurdles. Here, we introduce a photonic sensing platform based on nanoscale voids that enables the simultaneous material- and morphology-sensitive detection of particles below 500 nm. Void arrays embedded in a high-refractive-index material act in parallel as both sorting elements and color reporters. Spherical and elongated particles are selectively trapped in circular and elliptical voids, while different polymer types are distinguished simultaneously. This approach offers a scalable route toward optical identification of nanoplastics in environmental settings. Its compatibility with high-throughput analysis positions it as a promising tool for real-time studies.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2026-08-07
DOI
https://doi.org/10.1038/s41467-026-76199-8
Primary Topic
Microplastics and Plastic Pollution
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

All-optical Mie void sensor for complex nanoplastic mixtures

Markus Retsch, Mario Hentschel, Harald W. Giessen, Dominik Ludescher et al.
Nature Communications
Microplastics and Plastic Pollution
article

All-optical Mie void sensor for complex nanoplastic mixtures

Markus Retsch, Mario Hentschel, Harald W. Giessen, Dominik Ludescher, Monika Ubl, Julian Schwab, Serkan Arslan, Evelyn Kubacki
article en

Abstract

The fragmentation and direct release of plastic debris have emerged as a pressing ecological concern. Once dispersed, these particles infiltrate food webs, accumulate within organisms, and bind toxic co-contaminants, posing long-term risks to ecosystems and human health. Despite growing awareness, the characterization of nanoplastics remains highly challenging. Moreover, obtaining additional information, such as particle shape or material composition, further exacerbates these detection hurdles. Here, we introduce a photonic sensing platform based on nanoscale voids that enables the simultaneous material- and morphology-sensitive detection of particles below 500 nm. Void arrays embedded in a high-refractive-index material act in parallel as both sorting elements and color reporters. Spherical and elongated particles are selectively trapped in circular and elliptical voids, while different polymer types are distinguished simultaneously. This approach offers a scalable route toward optical identification of nanoplastics in environmental settings. Its compatibility with high-throughput analysis positions it as a promising tool for real-time studies.

Nature CommunicationsVol. 17(1)
University of Stuttgart (DE), Institute for Innovative Process Engineering (DE), Bavarian Polymer Institute (DE), University of Bayreuth (DE)
Carl-Zeiss-Stiftung, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, Universität Stuttgart, Vector Stiftung, HORIZON EUROPE Framework Programme, HORIZON EUROPE European Innovation Council
Openalex Percentile: Top 19%
Microplastics and Plastic Pollution
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.