Atmospheric aging and light-induced degradation of amorphous and nanostructured silicon using photoconductivity and electron spin resonance
Bibliographic record
Abstract
Previous studies indicate that the dark conductivity in amorphous and microcrystalline silicon may increase or decrease with exposure to deionized water (DIW) or pure oxygen at 80 °C but always decreases with light exposure. While the light-induced effect is linked to paramagnetic dangling bonds (Do), the origin of metastability in microcrystalline silicon remains unclear. In this study, we use steady-state photoconductivity (SSPC), dual-beam photoconductivity (DBP), and electron spin resonance (ESR), to study the behaviors under soaking in DIW and (or) pure oxygen at 80 °C and light-exposure of amorphous (a-Si:H) and nanostructured (nc-Si:H) silicon samples deposited in a capacitively coupled plasma-enhanced chemical vapor deposition system. Powders from thick samples of low and high crystallinity (Xc) peeling off large substrates were collected in quartz tubes for ESR measurements. Dark conductivity decreases upon exposure to pure oxygen at 80 °C for nc-Si:H but remains unchanged for a-Si:H. The ESR signal attributed to Do decreases with soaking in DIW for high and low crystallinity nc-Si:H but the effect is more significant for higher Xc. Changes in SSPC, DBP, and ESR are used to compare the degradation mechanisms because of O2 exposure and light for amorphous and nanostructured silicon.
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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".