Bibliographic record
Abstract
Thermoelectric effect has attracted much attention recently due to its potential application in cooling and power generation.The related theory (for bulk materials) is not new and the key parameter of interest, i.e., the thermoelectric power TEP or the Seebeck coefficient S can be derived based on charge transport and energy/heat transfer.Over the years, many thermoelectric materials have been identified and exhibited high conversion efficiency.Recent studies on thermoelectric effect in hetero-structures and delta-doped layers have also reported large thermoelectric power and it is of interest to find out if these selectively doped and/or interface layers (sometimes down to 1-2 unit cells) will exhibit the same thermoelectric properties as found in bulk materials.From the perspective of maximizing the thermoelectric power, the question would be whether one ought to favour the use of a quantum well structure or a heterostructure.This work provides a comparative study on the performance of thermoelectric materials built on these layered structures.Analyses suggest that for a moderately doped surface layer, there is little difference between the 2dimensional model and the 3-dimensional model.The difference occurs at high doping when a 'metallic' model has to be used.For the quantum wells and the hetero-structure our results indicate that there will be charge migration into the substrate, which may complicate the analyses.In our opinion, this can be the reason for the observed "giant" thermoelectric power found in the TiO 2 -SrTiO 3 hetero-structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".