Sensitivity Mapping of a Terahertz Split-Ring Resonator Metasurface for Local Microplastic Detection in Water: Effects of Particle Position and Shape

Conventional vibrational spectroscopy provides chemically specific identification of microplastics but often requires extensive sample preparation and particle-by-particle analysis. This study numerically examines a terahertz split-ring resonator (SRR) metasurface as a transducer for local dielectric perturbations in water. A finite-element model was used to quantify the spatial response of the active gap. A 5 × 5 sensitivity map yielded frequency shifts of 0.37–0.97 GHz for equivalent local polystyrene perturbations, with maxima at the gap corners, consistent with simulated field localization. An equal-volume shape study compared a spherical inclusion (Δf = 4.34 GHz) with ellipsoids of identical volume and constant height; shifts of 4.25–4.56 GHz demonstrated dependence on in-plane orientation. At the highest-sensitivity cell, shifts of 1.12, 0.97, 0.69, 0.25, and 0.24 GHz were obtained for PE, PS, PET, and two synthetic high-permittivity references, respectively. For three four-corner configurations, the observed shifts agreed with reference-cell estimates within 1.4%. The results identify positional, dielectric, and geometric factors that should be controlled in future microfluidic preconcentration and hybrid-sensing experiments.

Authors

Institutions

Publication Details

Journal
Materials
Published
2026-09-06
DOI
https://doi.org/10.3390/ma19173790
Primary Topic
Microwave and Dielectric Measurement Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Sensitivity Mapping of a Terahertz Split-Ring Resonator Metasurface for Local Microplastic Detection in Water: Effects of Particle Position and Shape

Przemysław Łopato, Michał Herbko, Adam Ruszczynski
Materials
Microwave and Dielectric Measurement Techniques
article

Sensitivity Mapping of a Terahertz Split-Ring Resonator Metasurface for Local Microplastic Detection in Water: Effects of Particle Position and Shape

Przemysław Łopato, Michał Herbko, Adam Ruszczynski
article en

Abstract

Conventional vibrational spectroscopy provides chemically specific identification of microplastics but often requires extensive sample preparation and particle-by-particle analysis. This study numerically examines a terahertz split-ring resonator (SRR) metasurface as a transducer for local dielectric perturbations in water. A finite-element model was used to quantify the spatial response of the active gap. A 5 × 5 sensitivity map yielded frequency shifts of 0.37–0.97 GHz for equivalent local polystyrene perturbations, with maxima at the gap corners, consistent with simulated field localization. An equal-volume shape study compared a spherical inclusion (Δf = 4.34 GHz) with ellipsoids of identical volume and constant height; shifts of 4.25–4.56 GHz demonstrated dependence on in-plane orientation. At the highest-sensitivity cell, shifts of 1.12, 0.97, 0.69, 0.25, and 0.24 GHz were obtained for PE, PS, PET, and two synthetic high-permittivity references, respectively. For three four-corner configurations, the observed shifts agreed with reference-cell estimates within 1.4%. The results identify positional, dielectric, and geometric factors that should be controlled in future microfluidic preconcentration and hybrid-sensing experiments.

MaterialsVol. 19(17)
West Pomeranian University of Technology in Szczecin (PL)
Clean water and sanitation
Openalex Percentile: Top 20%
Microwave and Dielectric Measurement Techniques
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.