Enhanced thermoelectric figure of merit in thin GaAs nanowires
Bibliographic record
Abstract
Combining density functional theory and the nonequilibrium Green's function method, we investigate the thermoelectric properties of thin GaAs nanowires (NWs). After identifying the most stable structures for GaAs NWs, either in wurtzite (wz) or zinc blende (zb) stacking, we present a systematic analysis on the thermoelectric properties of these NWs and their dependence on stacking type (wz or zb), size of NWs, and temperature. Although bulk GaAs is a well-known poor thermoelectric material, the thermoelectric figure of merit, ZT, is significantly enhanced in thin GaAs NWs. Typically, the room temperature ZT of a 1.1 nm-diameter GaAs NW reaches as high as 1.34, exhibiting more than 100-fold improvement over the bulk counterpart, which is attributed to both the reduced thermal conduction and enhanced power factor in thin NWs. Adopting their unique electronic characteristics, further enhancement is possible through surface engineering, for example, the introduction of surface roughness or dopants.
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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) | 0.000 | 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".