Quantitative photothermal investigation of nonradiative recombination parameters in GaAs/InAs(QD)/GaAs quantum dot structures using a three-layer laser beam deflection model
Bibliographic record
Abstract
In this paper, we developed a theoretical model for the photothermal deflection technique in order to investigate the electronic parameters of three-layer semiconductor structures. This model is based on the resolution of thermal and photogenerated carrier diffusion-wave equations in different media. Theoretical results show that the amplitude and phase of the photothermal deflection signal is very sensitive to the nonradiative recombination parameters. The theoretical model is applied to one layer of InAs quantum dots (QDs) inserted in GaAs matrix InAs/GaAs QDs in order to investigate the QD density effects on nonradiative recombination parameters in InAs through fitting the theoretical photothermal beam deflection signal to the experimental data. It was found that the minority carrier lifetime and the electronic diffusivity decrease as functions of increasing InAs QD density. This result is also related to the decrease in the mobility from 21.58 to 4.17 (±12.9%) cm2/V s and the minority carrier diffusion length from 0.62 (±5.8%) to 0.14 (±10%) μm, respectively. Furthermore, both interface recombination velocities S2/3 of GaAs/InAs (QDs) and S1/2 of InAs (QDs)/GaAs increase from 477.7 (±6.2%) to 806.5 (±4%) cm/s and from 75 (±7.8%) to 148.1 (±5.5%) cm/s, respectively.
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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".