OPTIMIZATION OF A-SIC: HASBUFFER LAYER FOR EFFICIENCY ENHANCEMENT OF AMORPHOUS SILICON SOLAR CELLS
Bibliographic record
Abstract
Thin film single junction hydrogenated amorphous silicon (a-Si:H) solar cells have been fabricated using RF-Plasma Enhanced Chemical Vapor Deposition method.The p-,i-, and n-layers have been optimized individually for achieving cell efficiency of 5.11 %(V oc =0.84 V, J sc =10.13 mA/cm2, and FF=0.59).In this study an effort has been made to optimize and incorporate hydrogenated amorphous silicon carbide (a-SiC:H) layer as a buffer layer between the doped a-Si:H forming the emitter (p-layer) and the absorber layer (i-layer) to enhance the cell efficiency further.The buffer layer has been studied for electrical, optical, structural properties and layer thickness by varying the process parameters such as CH 4 , SiH 4 and H 2 gas concentrations.The optimized buffer layer was about 12 nm thick with an optical band gap of 1.88 eV.Insertion of this film between the p-and i-layers resulted in an increased power conversion efficiency of 5.88% and J sc =11.0mA, compared to the conventional cells.The observed improvement is related mainly to minimum absorption loss and capability of driving out the photo generated carriers with minimum recombination losses, with transportation of carriers to the outer circuit with minimum electrical resistance.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".