Modeling and characterization of carbon nanotube agglomeration effect on electrical conductivity of carbon nanotube polymer composites
Bibliographic record
Abstract
This paper investigated the effect of carbon nanotube (CNT) agglomeration on the electrical conductivity of CNT-polymer composites by experimental characterization and theoretical modeling. The present experimental results show that the acid treatment of CNTs has significantly alleviated the CNT agglomeration in CNT-polymer composites and improved the electrical conductivity of the composites compared with CNT-polymer composites made from the same pristine CNTs. The improvement by the acid treatment is further studied by a multiscale CNT percolation network model that considers the CNT agglomeration based on experimental observation. Numerical results are in good agreement with the experimental data. The smaller the size of CNT agglomerates is in the experiments, the closer the measured electrical conductivity of CNT-polymer composites is to its theoretical limit. The current study verifies that (i) the CNT agglomeration is the main cause that leads to a lower electrical conductivity of CNT-polymer composites than their theoretical limit, and (ii) the current multiscale percolation network model can quantitatively predict the electrical conductivity of CNT-polymer composites with CNT agglomeration. The comprehensiveness of the developed modeling approach enables an evaluation of results in conjunction with experimental data in future works.
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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) | 0.000 | 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 | 0.001 |
| 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".