Numerical and experimental study of radiation induced conductivity change of carbon nanotube filled polymers
Bibliographic record
Abstract
Measuring the conductivity changes of sensing materials to detect a wide range of radiation energy and dosage is one of the major sensing mechanisms of radiation sensors. Carbon nanotube (CNT) filled composites are suitable for sensing radiation because of the extraordinary electrical properties of CNTs and the CNT-network formed inside the polymer matrix. Although the use of CNT-based nanocomposites as potential radiation sensing materials has been widely studied, there is still a lack of theoretical models to analyze the relationship between electrical conductivity and radiation dosages. In this article, we propose a 3D model to describe the electrical conductivity of CNT-based nanocomposites when being irradiated by ionizing radiation. The Monte Carlo method has been employed to calculate radiation intensity, CNT concentration and alignment's influence on the electrical conductivity. Our simulation shows a better agreement when CNT loading is between the percolation threshold and 3% volume fraction. Radiation experiments have been performed to verify the reliability of our model to illustrate a power function relationship between the electrical conductivity of a CNT-filled polymer and radiation intensity. In addition, the predicted alignment to obtain the best sensitivity for radiation sensing has been discussed to help with CNT-network building in the fabrication process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".