Enhanced Radio Frequency Biosensor for Food Quality Detection Using Functionalized Carbon Nanofillers
Bibliographic record
Abstract
This paper outlines an improved design of inexpensive, wireless and battery free biosensors for in situ monitoring of food quality. This type of device has an additional advantage of being operated remotely. To make the device, a portion of an antenna of a passive 13.56 MHz radio frequency identification (RFID) tag was altered with a sensing element composed of conductive nanofillers/particles, a binding agent, and a polymer matrix. These novel RFID tags were exposed to biogenic amine putrescine, commonly used as a marker for food spoilage, and their response was monitored over time using a general-purpose network analyzer. The effect of conductive filler properties, including conductivity and morphology, and filler functionalization was investigated by preparing sensing composites containing carbon particles (CPs), multiwall carbon nanotubes (MWCNTs), and binding agent grafted-multiwall carbon nanotubes (g-MWCNTs), respectively. During exposure to putrescine, the amount of reflected waves, frequency at resonance, and quality factor of the novel RFID tags decreased in response. The use of MWCNTs reduced tag cutoff time (i.e., faster response time) as compared with the use of CPs, which highlighted the effectiveness of the conductive nanofiller morphology, while the addition of g-MWCNTs further accelerated the sensor response time as a result of localized binding on the conductive nanofiller surface. Microstructural investigation of the film morphology indicated a better dispersion of g-MWCNTs in the sensing composite as compared to MWCNTs and CPs, as well as a smoother texture of the surface of the resulting coating. These results demonstrated that grafting of the binding agent onto the conductive particles in the sensing composite is an effective way to further enhance the detection sensitivity of the RFID tag based sensor.
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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 | 0.000 |
| 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 | 0.001 | 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".