A Single-Frequency Amplitude-Modulated RFID Portable Backscatter Surface Scanner for Near-Field Permittivity Imaging
Bibliographic record
Abstract
We hereby propose a truly portable, single-frequency microwave near-field permittivity sensor based on RFID back-scattering. Unlike conventional VNA-based frequency-swept methodologies demanding extensive spectral scanning and intensive data processing, this single-point architecture monitors amplitude at 2.45 GHz, achieving superior sensitivity-linearity balance across sub-10 permittivity range. It seamlessly integrates three critical elements into a unified sensing platform. A dual-layer dipole-patch antenna achieves record 9.9 dBi gain at 2.45 GHz through a constructive electromagnetic field interference mechanism, extending interrogation range without requiring bulky array configurations. A compact RFID tag with complementary split ring resonator (CSRR) (30 × 30 mm2patch with 10 × 7 mm2resonator) exploits amplitude-variation sensing at the fixed frequency, eliminating GHz-wide spectral sweeps, by translating permittivity variations directly into monotonicS21attenuation characteristics. A battery-powered interrogator incorporates VCO and power detector components, delivering immediately available voltage readout. Experimental validation demonstrates calibration-free operation across commercial dielectric substrates spanning εr= [2.2, 10.2] with 1 cm spatial resolution through surface scanning trials and robust performance in both laboratory (stationary) and portable field configurations. Morphological diversification of CSRR variants and dual-band tag prototypes showcase platform scalability for multi-parameter sensing and IoT applications integration. By combining high-gain antennas, CSRR-based amplitude interrogation, and direct-voltage RFID readout, this work delivers the first fully portable, wireless, and single-frequency permittivity sensor, enabling disposable tags readable by handheld units for ubiquitous material characterization 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".