Giant piezoresistivity in aligned carbon nanotube nanocomposite: account for nanotube structural distortion at crossed tunnel junctions
Bibliographic record
Abstract
High piezoresistivity is critical for multifunctional carbon nanotube polymer composites with sensing capability. By developing a new percolation network model, this work reveals theoretically that a giant piezoresistivity in the composites can be potentially achieved by controlled nanotube alignment resulting from field based alignment techniques. The tube-tube and/or tube-matrix interaction in conjunction with the aligned carbon nanotube networks are fully considered in the newly proposed model. The structural distortion of nanotubes is determined self-consistently by minimizing the pseudo-potential energy at crossed-tube junctions based on the Lennard-Jones potential and simulation of coarse grain molecular dynamics. The tunneling transport through crossed-tube junctions is calculated by the Landauer-Büttiker formula with empirical fitting by first-principle calculation. The simulation results also reveal that the piezoresistivity can be further improved by using low carbon nanotube loadings near the percolation threshold, carbon nanotubes with a small aspect ratio, high intrinsic conductivity and polymers with a small Poisson's ratio. This giant piezoresistive effect offers a tremendously promising future, which needs further thorough exploration.
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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 | 0.000 |
| 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) | 0.001 | 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".