Looking Inside a Working SiLED
Bibliographic record
Abstract
In this study, we investigate for the first time morphological and compositional changes of silicon quantum dot (SiQD) light-emitting diodes (SiLEDs) upon device operation. By means of advanced transmission electron microscopy (TEM) analysis including energy filtered TEM (EFTEM) and energy dispersive X-ray (EDX) spectroscopy, we observe drastic morphological changes and degradation for SiLEDs operated under high applied voltage ultimately leading to device failure. However, SiLEDs built from size-separated SiQDs operating under normal conditions show no morphological and compositional changes and the biexponential loss in electroluminescence seems to be correlated to chemical and physical degradation of the SiQDs. By contrast, we found that, for SiLEDs fabricated from polydisperse SiQDs, device degradation is more pronounced with three main modes of failure contributing to the reduced overall lifetime compared to those prepared from size-separated SiQDs. With this newfound knowledge, it is possible to devise ways to increase the lifetimes of SiLEDs.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".