Near-field coupled RFID tag for carbon dioxide concentration sensing
Bibliographic record
Abstract
Measuring carbon dioxide (CO2) concentration plays an important role in environmental sciences, agri-food, medicine, packed food, and oil and chemical industry (R. Ali, T. Lang, S.M. Saleh, R.J. Meier and O.S. Wolfbeis, Analytical Chemistry, 83, 2846–2851, 2011). Spoilage of grain can be 3–10% in developed countries and as high as 30% in developing countries. Increased levels of CO2in a stored grain bulk indicate that insects, mould, or excessive respiration is present. A wireless sensor that is able to monitor the evolution of excessive CO2from a stored grain mass would be an inexpensive reliable indicator of deteriorating grain and lead to significant reduction in food loss. Conducting polymer, colorimetric pH indicator and metal-oxide based sensors have previously been applied to sense CO2. These approaches require custom electronics in order to be integrated with RFID technology. In this work we present a new CO2sensor based on a hydrogel pH-sensitive electrode pair. This sensor provides a direct voltage measurement depending on the CO2 concentration in the surrounding environment. We have integrated this sensor into a chipless near-field coupled RFID tag.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".