Features of photoconversion in highly efficient silicon solar cells
Bibliographic record
Abstract
The photoconversion efficiency η in highly efficient silicon-based solar cells (SCs) is analyzed depending on the total surface-recombination rate S s on illuminated and rear surfaces. Solar cells based on silicon p-n junctions and α-Si:H or α-SiC:H-Si heterojunctions (so-called HIT structures) are considered in a unified approach. It is shown that a common feature of these SCs is an increased open-circuit voltage V oc associated with an additional contribution of the rear surface. Within an approach based on analysis of the physical features of photoconversion in SCs, taking into account the main recombination mechanisms, including Shockley-Read-Hall recombination, radiative recombination, surface recombination, recombination in the space-charge region, and band-to-band Auger recombination, expressions for the photoconversion efficiency of such SCs are obtained. The developed theory is compared with experiments, including those for SCs with record parameters, e.g., η = 25% and 24.7% for SCs with a p-n junction for HIT structures, respectively, under AM1.5 conditions. By comparing theory and experiment, the values of S s achieved as a result of recombination-loss minimization by various methods are determined. The results of calculations of the maximum possible value ηmax in silicon SCs are compared with the data of other papers. Good agreement is observed.
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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) | 0.001 | 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".