A Self-Powered Microwave Sensor Using Perfect Metamaterial Absorbers for Liquid Characterization
Bibliographic record
Abstract
This article introduces a self-powered microwave sensing platform designed for precise liquid characterization, specifically targeting methanol concentrations in water. The system leverages a power harvester based on a high-efficiency perfect metamaterial absorber (PMA), which captures ambient electromagnetic (EM) energy and converts it to direct current (dc) through an integrated two-stage rectifier to sustain an active split-ring resonator (SRR) sensor. With an absorption efficiency exceeding 98%, the PMA harvester utilizes transmission-line metamaterials optimized with lumped inductors and series capacitors, providing robust performance across diverse incident angles and polarizations. The sensor operates at 2.4 GHz and demonstrates high sensitivity to methanol concentrations ranging from 0% to 100% in water, achieving this with a compact and symmetric design that ensures stability under variable conditions. The proposed self-sustaining system presents a low-cost, energy-efficient solution ideal for remote, continuous operation, paving the way for applications in the food, beverage, and chemical industries, where detecting low chemical concentrations is critical. This work advances self-powered microwave sensing technologies, offering new opportunities for reliable, high-resolution material characterization.
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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 | 0.001 |
| 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".