A Printed LC Resonator-Based Flexible RFID for Remote Potassium Ion Detection
Bibliographic record
Abstract
This article presents a flexible printed radio-frequency identification (RFID) sensor based on a printed inductive–capacitive (LC) resonator circuit and a potassium ion-selective electrode (ISE) for remote potassium ion sensing. The potassium ion concentration of the contact solution can be monitored by measuring the change of the resonant frequency of the RFID sensor. The resonant frequency of the sensor can be directly detected by measuring the induced change in the reflection coefficient ($S_{11}$) of an external interrogator coil that is inductively coupled to the RFID sensor. Results obtained for the RFID sensor exhibited a second-order exponential relationship between the resonant frequency of the sensor and the K+concentration of the solution over 0.001–2 mol/L dynamic range values. Effects of varying separation distance between the sensor and the interrogator coil and the effect of temperature variations on sensor’s measurement are shown. With less than 2-s response time and the long-term stability, the wireless passive printed sensor has potential for low-cost K+monitoring applications such as K+monitoring in food packages.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".