Hall effect measurements on Bridgman-grown CuInSe<sub>2</sub>with sodium
Bibliographic record
Abstract
The presence of sodium, either in the substrate or as a co-evaporant during absorber deposition, has been shown to improve the performance of polycrystalline photovoltaic devices made with Cu(In, Ga)Se(2), as well as the ternary CuInSe(2). Investigations have shown Na or Na compounds deposited on the grain boundaries, but none have been found within intact crystal grains, leading to suggestions that grain boundaries may play a role in the improved performance of the cells. Therefore, in this study, ingots containing large monocrystals of CuInSe(2) have been grown, using a vertical-Bridgman method, from melts that also include a varying quantity of sodium. In order to simulate the conditions under which cells are constructed, a proportion of Se above stoichiometry has been added to some of the melts. Resistivity and Hall effect measurements were then performed on the material after growth. The results show no large change in either resistivity or majority hole concentration in either set of samples, although a slight decrease in the latter value was apparent in the excess Se samples with 0.2 and 0.3 at.% Na additions. No clear trend in hole mobility could be discerned, although an increase was seen with 0.2 at.% Na addition for both samples.
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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) | 0.002 | 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".