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 mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> patch with 10 × 7 mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> resonator) exploits amplitude-variation sensing at the fixed frequency, eliminating GHz-wide spectral sweeps, by translating permittivity variations directly into monotonic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sub> attenuation 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 ε<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>r</i></sub> = [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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".