Stretchable RFID for Wireless Strain Sensing With Silver Nano Ink
Bibliographic record
Abstract
Flexible and stretchable inductor-capacitor (LC) resonator-based chipless radio frequency identification (RFID) tags have been fabricated by the direct stamping with silver nano ink. The tags are optimized based on the sympathetic oscillation of LC circuit at specifically designed resonant frequencies ranged from 1.12 to 1.7 GHz by adjusting dimensions of the inductor and capacitor in a tag. Pressure applied to the layer of silver nano ink during the stamping procedure helps densification of silver nanoparticles inside trenches of the stamp before heat-annealing of them. Transfer stamping process is simulated to demonstrate stress distribution across the layer of silver nanoparticles. Compaction of silver nanoparticles, in turn, positively affects mechanical strength of the final silver electrode and enables RFID strain sensors on polydimethylsiloxane to be stretchable up to 7%. By the stretchable RFID strain sensors, wireless strain sensing is demonstrated with a gauge factor of 0.51. Multiple encoded identifications with combination of double tags and stretching behavior of fabricated tags are also demonstrated. This stretchable RFID sensor is promising for biomedical applications such as real-time monitoring of motion detection.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".