Behavioral model for electrical response and strain sensitivity of nanotube-based nanocomposite materials
Bibliographic record
Abstract
An algorithm to study the electrical conductivity of nanocomposite layers, made by dispersing nanotubes inside a polymer structure, is proposed. Conduction is modeled by following the path of electric current through the nanotube network within the polymer. Based on this algorithm, a numerical simulator is developed to study the effect of nanoparticles and nanocomposite film dimensions and concentration on the conductance of a nanocomposite resistor. This simulator is also capable of predicting the behavior of nanocomposite resistors under mechanical strain for devices with different parameters. To verify the simulation results, several test devices with different filler concentrations are fabricated from a composite of SU-8 and multiwall carbon nanotubes. The experimental results agree with the performance anticipated by the simulator, as the applied strain and filler concentration are altered independently. The simulator is capable of illustrating the tradeoffs between conductivity, sensitivity, and repeatability and can be used as a powerful tool to pave the path for designing reliable electronic components from nanocomposite materials.
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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) | 0.003 | 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".