Increased light-emission efficiency in disordered <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mo stretchy="false">(</mml:mo><mml:mi>In</mml:mi><mml:mo>,</mml:mo><mml:mi>Ga</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mrow><mml:mrow><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:mrow></mml:math> through the correlated reduction of recombination rates
Bibliographic record
Abstract
Experiments have shown that the light-emission efficiency of indium gallium nitride [$(\mathrm{In},\mathrm{Ga})\mathrm{N}$] light-emitting diodes improves with increasing indium concentration. It is widely thought that compositional fluctuations due to indium incorporation suppress diffusion of carriers to nonradiative centers, thus leading to defect-insensitive emission. However, recent experiments have challenged this hypothesis by revealing unexpectedly long diffusion lengths at room temperature. Here, we demonstrate an alternative mechanism involving the correlated reduction in radiative and nonradiative recombination rates that explains the increase in light-emission efficiency of $(\mathrm{In},\mathrm{Ga})\mathrm{N}$ with increasing indium concentration, without invoking the suppression of carrier diffusion. Our analysis challenges the notion that carrier localization gives rise to defect tolerance in $(\mathrm{In},\mathrm{Ga})\mathrm{N}$.
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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) | 0.002 | 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".