Improving the <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math> </inline-formula>-Factor of Printed HF RFID Loop Antennas on Flexible Substrates by Condensing the Microstructures of Conductors
Bibliographic record
Abstract
High frequency (HF) radio frequency identification (RFID) loop antennas are popular for HF RFID, energy transfer and near field communication applications. One of the major parameters defining the working range of HF RFID antennas is theirQ-factor. Printing techniques are the ideal method for mass fabrication of HF RFID loop antennas. However, due to the relatively low conductivity of the inks available on the market, theQ-factor of the printed HF loop antenna tends to be low and in many cases, fails to meet the working range requirements. This paper reports two methods to condense the microstructures of the conductors in order to improve theQ-factors of printed HF RFID loop antennas. Both thermal compression (pressing the sample at elevated temperature) and near-infrared annealing are studied, and the results have demonstrated that both approaches are efficient in improving theQ-factors of printed loop antennas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.024 |
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".