Photodeterioration and recovery treatment for silicon nanocrystal luminescence
Bibliographic record
Abstract
Silicon nanocrystals (Si-nc) embedded in silica exhibit intense visible photoluminescence (PL) at room temperature. However, under continuous wavelength (CW) laser excitation at 405 nm, the Si-nc PL intensity decreases with time, approximately with two decay constants. The fast decay component is unchanged by repetitive laser exposures, it is related to the local sample heating induced by the laser. The slower time constant corresponds to a permanent decrease of the PL emission. This photodeterioration strongly affects the precision of optical gain measurements using VSL (Variable Stripe Length) or P&P (Pump and Probe) techniques, hindering the development of Si-nc technology for photonics applications. In this context, a procedure that would restore the PL intensity of Si-nc samples or minimize this deterioration is highly desirable. UVC light (254 nm) irradiation of samples followed by an annealing at different temperatures for 1 h under nitrogen flux increases the PL emission of Si-nc embedded in silica that have been previously exposed to a CW laser pumping. Although this procedure does not prevent the decrease of the PL intensity associated with the increase of sample temperature under CW pumping (the fast decay component), it contributes significantly to reduce the permanent deterioration of the PL intensity. This procedure can also be applied to non-irradiated samples. The PL emission collected from treated samples was studied as a function of laser irradiation time, and compared to that of non-treated samples. The resistance to degradation of light-emitting silicon nanocrystals can be increased by UVC irradiation followed by annealing at an optimal temperature of 400 °C under nitrogen environment. Following this treatment, a reliable optical gain measurement can be performed once the local heating has been stabilized (the fast decay component).
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".