Bibliographic record
Abstract
Electromechanical effects are important in semiconductor nanostructures as most of the semiconductors are piezoelectric in nature. These nanostructures find applications in electronic and optoelectronic devices where they may face challenges for thermal management. Low dimensional semiconductor nanostructures, such as quantum dots (QD) and nanowires, are the nanostructures where such challenges must be particularly carefully addressed. In this contribution we report a study on thermoelectromechanical effects in QDs. For the first time a coupled model of thermoelectroelasticity has been applied to the analysis of quantum dots and the influence of thermoelectromechanical effects on bandstructures of low dimensional nanostructures has been quantified. Finite element solutions are obtained for different thermal loadings and their effects on the electromechanical properties and bandstructure of QDs are presented. Our model accounts for a practically important range of internal and external thermoelectromechanical loadings. Results are obtained for typical QD systems based on GaN/AlN and CdSe/CdS (as representatives of III-V and II-VI group semiconductors, respectively), with cylindrical and truncated conical geometries. The wetting layer effect on electromechanical quantities is also accounted for. The energy bandstructure calculations for various thermal loadings are performed. Electromechanical fields are observed to be more sensitive to thermal loadings in GaN/AlN QDs as compared to CdSe/CdS QDs. The results are discussed in the context of the effect of thermal loadings on the performance of QD-based nanosystems.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".