Size and concentration characterization of microplastic particles in aqueous samples using sensitivity-enhanced coupled planar microwave resonators
Bibliographic record
Abstract
This study presents a novel microwave sensing platform for real-time monitoring of microplastic (MP) particle size and concentration in liquid media, with enhanced sensitivity achieved through the integration of an interdigital capacitor with the traditional split-ring resonator structure. A disposable sample holder (< $1) allows multiplex testing without cross-contamination. The sensing principle relies on electromagnetic interactions between suspended MP particles in liquid media and the microwave resonator, which is coupled with a planar microwave reader. Initially, MP particles are homogeneously distributed, but as they settle over time, they result in a dynamic shift in the resonance frequency that depends on MP size. The resonance frequency shift plateaus once all particles have settled, providing a measure of MP concentration. The sensor was designed and optimized using HFSS simulations and tested at three temperatures (10-30°C) in four host media (DI water, tap water, NaCl and urea solutions). Its performance was evaluated by detecting MPs of varied sizes (20, 70, and 275 µm) at concentrations of 100k, 1,000k, and 10,000k particles/L. The average detection slopes across the tested concentrations were 8.64 kHz for 20 µm, 38.52 kHz for 70 µm, and 110.78 kHz for 275 µm. This novel sensor demonstrates strong potential for on-site MP size and concentration monitoring, offering a cost-effective solution for environmental applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".