A Self-Sustaining Regenerative Amplifier Sensor Using Perfect Metamaterial Absorber for Liquid Concentration Prediction
Bibliographic record
Abstract
This paper presents a self-powered microwave sensing platform for precise methanol concentration detection in water. The system features a high-efficiency perfect metamaterial absorber (PMA) that converts ambient electromagnetic energy into DC via a dual-stage rectifier, powering an active split-ring-resonator (SRR) sensor. The PMA harvester, optimized with lumped inductors and series capacitors, achieves over 98% absorption efficiency, resulting in stable performance across different angles and polarizations. Operating at 2.4 GHz, the sensor effectively detects methanol concentrations ranging from 0% to 100%. Sensor data is analyzed using an LSTM model for liquid concentration prediction, providing robustness in noisy environments with measurement anomalies. This approach achieves a 98% accuracy rate in predicting methanol concentrations. The compact, energy-efficient system is well-suited for remote monitoring in the food, beverage, and chemical industries, advancing self-powered microwave sensing technology for reliable 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 | 0.001 | 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".