Characterization and optimization of high-efficiency crystalline silicon solar cells: Impact of recombination in the space charge region and trap-assisted Auger exciton recombination
Bibliographic record
Abstract
Since the photoconversion efficiency η of the silicon-based solar cells (SCs) under laboratory conditions is approaching the theoretical fundamental limit, further improvement of their performance requires theoretical modeling and/or numerical simulation to optimize the SCs parameters and design. The existing numerical approaches to modeling and optimizing the key parameters of high-efficiency solar cells based on monocrystalline silicon, the dominant material in photovoltaics, are described. It is shown that, in addition to the four usually considered recombination processes, namely, Shockley–Read-Hall, surface, radiative, and band-to-band Auger recombination mechanisms, the non-radiative exciton Auger recombination and recombination in the space charge region (SCR) have to be included. To develop the analytical SC characterization formalism, we proposed a simple expression to model the wavelength-dependent external quantum efficiency of the photocurrent near the absorption edge. Based on this parameterization, the theory developed allows for calculating and optimizing the base thickness-dependent short-circuit current, the open-circuit voltage, and the SC photoconversion efficiency. The accuracy of the approach to optimizing solar cell parameters, particularly thickness and base doping level, is demonstrated by its application to three Si solar cells reported in the literature: one with an efficiency of 26.63%, another with 26.81%, and a third with a record efficiency of 27.3%. The results show that the developed formalism enables further optimization of solar cell thickness and doping levels, leading to potential increases in 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".