Selfconsistent model of photoconversion efficiency for multijunction solar cells
Bibliographic record
Abstract
To accurately calculate efficiencies η of experimentally produced multijunction solar cells (MJSCs) and optimize their parameters, we offer semi-analytical photoconversion formalism that incorporates radiative recombination, Shockley-Read-Hall (SRH) recombination, surface recombination at the front and back surfaces of the cells, recombination in the space charge region (SCR) and the recombination at the heterojunction boundaries. Selfconsistent balance between the MJSC temperature and efficiency was imposed by jointly solving the equations for the photocurrent, photovoltage, and heat balance. Finally, we incorporate into the formalism the effect of additional photocurrent decrease with an increase of subcell number. It is shown that for an experimentally observed Shockley-Read-Hall lifetimes, the effect of re-absorption and re-emission of photons on MJSC efficiency can be neglected for non-concentrated radiation conditions. A significant efficiency η increase can be achieved by improving the heat dissipation using radiators and bringing the MJSC emissivity to unity, that is closer to black body radiation rather than grey body radiation. Our calculated efficiencies compare well with other numerical results available and are consistent with the experimentally achieved efficiencies. The formalism can be used to optimize parameters of MJSCs for maximum photoconversion 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".