Tunneling resistance and its effect on the electrical conductivity of carbon nanotube nanocomposites
Bibliographic record
Abstract
In this paper, we examined the effect of electron tunneling upon the electrical conductivity of carbon nanotube (CNT) polymer nanocomposites. A CNT percolating network model was developed to account for the random distribution of the CNT network using Monte Carlo simulations, where the tunneling resistance between CNTs was established based on the electron transport theory. Our work shows several novel features that result from this tunneling resistance: (i) direct contact resistance is the result of one-dimensional electron ballistic tunneling between two adjacent CNTs, (ii) the nanoscale CNT-CNT contact resistance should be represented by the Landauer-Büttiker (L-B) formula, which accounts for both tunneling and direct contact resistances, and (iii) the difference in contact resistance between single-walled CNTs (SWCNTs) and multi-walled CNTs (MWCNTs) can be modeled by the channel number in the L-B model. The model predictions reveal that the contact resistance due to electron tunneling effects in nanocomposites with dispersed SWCNTs plays a more dominant role than those with MWCNTs. These results compare favorably with existing experimental data and demonstrate that the proposed model can properly estimate the electrical conductivity of nanocomposites containing homogeneously dispersed percolating CNT network.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".