Mechanisms of temperature- and field-dependent effective drift mobilities and impact ionization coefficients in amorphous selenium
Bibliographic record
Abstract
The mechanisms of electric-field- and temperature-dependent effective drift mobility and impact ionization coefficient of both holes and electrons in amorphous selenium (a-Se) are investigated in this paper. An analytical model for the microscopic mobility, momentum relaxation mean free path, and hence the effective drift mobility and impact ionization coefficient of carriers, is proposed in this paper by considering the density of states distribution, field enhancement release rate from the shallow traps, and carrier heating. The results of the model are fitted with the published experimental results on effective mobility and impact ionization coefficient with wide variations of the applied electric field and temperature. A better fitting considering thermally activated tunneling for the field-enhancement release rate indicates that the effective drift mobility at extremely high fields is mainly controlled by the neutral defect states near the band edges. The density of state function near the band edges, consisting of an exponential tail and a Gaussian peak, can successfully describe the electric-field- and temperature-dependent effective drift mobility characteristics in a-Se. The momentum relaxation mean free path decreases with increasing field and decreasing temperature, which is required to describe the electric-field- and temperature-dependent behaviors of impact ionization coefficient in a-Se.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".