Compact Fractal-Shaped Frequency Selective Surface for Distant Liquid Sensing With Multiple Sensitive Spots
Bibliographic record
Abstract
This article introduces compact battery-less wireless radio frequency (RF) sensors using fractal-shaped electromagnetic surfaces. Fractal structures allow a compact total size of$0.75\lambda _{{0}} \times 0.75\lambda _{{0}}$at 2.9 GHz. In conventional sensors based on a frequency selective surface (FSS), the material under test (MUT) needs to be placed in all the unit cells or play the role of a substrate. However, in the proposed sensors, the MUT covers only 3%–7% of the electromagnetic surface with a volume of 1 mL. In contrast with conventional RF sensors with one local sensitive spot, the proposed surface can provide multiple sensitive spots. This capability has been tested using several different liquids including isopropyl alcohol (IPA), methanol, ethanol, acetone, and deionized (DI) water. The proposed sensing platform exhibits remarkable sensitivity while the reader antenna is over 10 cm away from the FSS. Changing the MUT from air to IPA and DI water results in a 40 and 350 MHz shift in the resonant frequency, respectively. This sensor can tolerate polarization misalignment between the reader antenna and the FSS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.001 | 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 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".