Dynamic rectification in a thermal diode based on fluid-solid interfaces: Contrasting behavior of soft materials and fluids
Bibliographic record
Abstract
We have used molecular dynamics simulations to demonstrate that significant rectification is possible in nano-devices by manipulating solid-fluid interfaces between a thin solid wall and a soft material. In addition, the nature of the rectification is opposite to that for a solid wall and a simpler polar fluid (e.g., water). This thus opens the possibilities of reversing the direction of rectification in a nano-device which could lead to more precise control of heat transfer rates. Our simulations also show that surface modifications of the type introduced here can lead to over six fold increase in heat transfer rates in nano-devices. Dissipating heat from increasingly miniaturized semi-conductors of the future will become increasingly important since size limitations are often dictated by heat dissipation rates. These results also point to some other exciting practical applications. If the outside wall of a house were made hydrophilic, during hot summer days when the intent is to reduce heat transfer from the ambient (Ta > Tin), a small polar fluid like water could be introduced between the two walls. During nights, in order to cool the house (when Tin > Ta) water could be replaced by a less volatile soft material to enhance heat transfer. Thus, the same wall would demonstrate significant rectification in both cases but with different fluids.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".