Performance at maximum figure of merit for a single quantum dot refrigerator
Bibliographic record
Abstract
In this paper, we examine the optimal performance characteristics for a single-level quantum dot refrigerator within the framework of ballistic electron transport between two reservoirs. Analytical expressions for the maximum figure of merit were derived. The coefficient of performance at the maximum figure of merit, which depends on the Carnot bound, was analyzed for a refrigerator of the quantum dot system and successfully compared with the maximum cooling power coefficient of performance and the Curzon–Ahlborn coefficient of performance. Besides, the coefficient of performance at the maximum figure of merit of the model was demonstrated through numerical analysis. Our results indicate that the coefficient of performance at maximum cooling power and at maximum figure of merit differs from the maximum cooling power coefficient of performance and the Curzon–Ahlborn coefficient of performance in the limit of a small Carnot coefficient of performance. Optimizing the figure of merit results in the highest coefficient of performance, while optimizing for cooling power leads to the lowest values. It is constrained by an upper bound of Carnot coefficient of performance and a lower bound on the coefficient of performance at maximum cooling power and on the Curzon–Ahlborn coefficient of performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".