Thermal rectification in a polymer-functionalized single-wall carbon nanotube
Bibliographic record
Abstract
Thermal rectification occurs when heat current through a material is favored in one direction but not in the opposite direction. These materials, often called thermal diodes, have the potential to perform logic calculations with phonons. Rectification obtained with existing material systems is either too minor or too difficult to implement practically. Hence, we present a scheme to enable higher rectification using a single-wall carbon nanotube (SWCNT) that is covalently functionalized near one end with polyacetylene (PA) chains. This composite structure allows rectification R up to 204%, which is higher than the values reported for SWCNTs. Here, [Formula: see text], where [Formula: see text] and [Formula: see text] are the heat currents for forward and reverse bias, respectively. The interatomic interactions in the SWCNT-PA nanocomposite are nonlinear, i.e., they are anharmonic, which is a requirement for thermal rectification. Through atomistic simulations, we identify two additional conditions to accomplish thermal rectification at the nanoscale, namely, (1) structural asymmetry, and (2) that the influence of this asymmetry on thermal transport is temperature dependent. The optimum temperature difference to achieve the highest thermal rectification with the structure is 40-80 K.
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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 | 0.000 |
| 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".