Influence of Annealing Process on Cu2O Nanofilm and the Efficiency of Annealed p-Cu2O/n Si Nanostructure Solar Cell Prepared by Thermal Evaporation Technique
Bibliographic record
Abstract
Thin film solar cells are one of the significant electronic applications due to their beneficial characteristics, especially adjustable optical features low cost, and high efficiency.In this research, p-type cupric oxide (Cu2O) nanofilms have been successfully deposited onto glass and n-type Si substrates by thermal evaporation technique under 10 -7 mbar, rate of deposition of 0.3 nm/s after that annealed at 200℃.The annealing process leads to an increase in the uniformity and homogeneity distribution of particles on the thin film surface as well as an improvement of the roughness.FE-SEM images of Cu2O nanofilms show that the average size has been increased from 25.15 to 35.36 with the enhancement of the distribution of particles after the annealing process.Average roughness and root mean square have increased from (0.166 to 1.18) nm and (0.213 to 1.490) nm respectively.Electrical characteristics of annealed Al/ Cu2ONPs/Si/Al solar cell were examined by current-voltage measurement.The short circuit current (ISC.) of 15 mA, open circuit voltage (VOC) of 500 mV, fill factor (F.F) of 0.45, and the efficiency (η) was found to be 3.37%.The annealing temperature gives more roughness to the surface and raises the absorption of incident photons and solar cell efficiency.
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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".