Time evolution of charged defect states in tritiated amorphous silicon
Bibliographic record
Abstract
Tritiated hydrogenated amorphous silicon (a-Si:H:T) thin films were deposited on crystalline silicon and high resistivity glass substrates. The time evolution of the density of defect states in these films was studied using the constant photocurrent method (CPM) and isothermal capacitance transient spectroscopy (ICTS). The density of defect states was found to change with time and to recover upon thermal annealing. The ICTS results revealed that, following thermal annealing, in a sample with approximately 1at.% tritium, the concentration of positively charged dangling bonds (D+) decreased by more than an order of magnitude over a period of 300h. The CPM results showed that, over the same period of time, the concentration of negatively charged dangling bonds (D−) increased by over two orders of magnitude. The D+ and D− concentrations followed exponential functions of time, but the rate was different than that of tritium decay. At the same time, the Urbach energy was found to decrease with time to about 1∕2 of its postanneal value. The change in the D+ and D− concentrations is primarily the result of capture of the beta particle generated electrons in dangling bonds and weak bonds, with steady state achieved through the development of a balance between carrier generation and carrier capture processes. The role of excess carriers was confirmed by CPM experiments under electrical bias.
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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) | 0.001 | 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".