Electrical properties of grain boundaries in polycrystalline materials under intrinsic or low doping
Bibliographic record
Abstract
An analytical model is developed to study the electrical properties (electric field and potential distributions, potential energy barrier height and polarization phenomenon) of polycrystalline materials at intrinsic or low doping for detector and solar cell applications by considering an arbitrary amount of grain boundary charge and a finite width of grain boundary region. The general grain boundary model is also applicable to highly doped polycrystalline materials. The electric field and potential distributions are obtained by solving Poisson's equation in both depleted grains and grain boundary regions. The electric field and potential distributions across the detector are analysed under various doping, trapping and applied biases. The electric field collapses, i.e. a nearly zero-average electric field region exists in some part of the biased detector at high trapped charge densities at the grain boundaries. The model explains the conditions of existence of a zero-average field region, i.e. the polarization mechanisms in polycrystalline materials. The potential energy barrier at the grain boundary exists if the electric field changes its sign at the opposite side of the grain boundary. The energy barrier does not exist in all grain boundaries in the low-doped polycrystalline detector and it never exists in intrinsic polycrystalline detectors under applied bias condition provided that there is no charge trapping in the grain.
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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 | 0.001 |
| 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".