Efficient Analytical Model of Conductivity of CNT/Polymer Composites for Wireless Gas Sensors
Bibliographic record
Abstract
This paper presents an analytical model of conductivity and sensitivity of passive wireless sensors for biohazard gas detection with lower computational cost and reasonable accuracy. Based on the effect of electron tunneling among the carbon nanotubes embedded in a polymer matrix, an analytical model for conductivity of the composite is presented. This model provides significantly lower computational cost as compared to the numerical resistive network models. By incorporation of electron tunneling effects, this model also provides closer approximation to experimental results in comparison to the models based on the percolation theory, which are highly relevant for filler/polymer composite applications designed around the percolation threshold. Using this conductivity model, the conductivity and sensitivity of the composite films are estimated in the presence of an organic gas. The change in the film resistance due to the absorption of the gas is investigated for different filler and gas concentrations. From the phase of the reflected radio frequency signal, the applications of the sensor for passive wireless gas sensing is estimated in a lossless transmission system terminated with a composite film as the load. This paper is useful for design and development of biohazard gas sensors for real-time remote monitoring.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".