A solid strategy to realize heteroface selective emitter and rear passivated silicon solar cells
Bibliographic record
Abstract
Abstract Passivated emitter and rear cell (PERC) with laser‐doped selective emitter (SE) has become mainstream in the PV industry. In this work, we report a solid strategy to realize heteroface monocrystalline silicon (mono‐Si) wafers for PERC‐SE solar cells by employing alkaline polishing to form a polished surface for the rear side and well‐established metal‐catalyzed chemical etching to form a honeycomb texture for the front side in one wet process successively. The key to success lies in the fact that the two back‐to‐back wafers inserted into one slot in the cassette are tightly attached together in MCCE etching so that only the exposed surfaces are etched to form textures, while the rear polished surfaces are still retained to avoid wrap‐around etching. With the strategy, the mono‐Si PERC‐SE solar cells achieve an average efficiency of over 22.0%, no poorer than that of the reference system (traditional alkaline texturing and rear acidic polishing), and have good light trapping capability for oblique incident light. Moreover, the total Si removal in the novel process is only ~0.4 g, which is far less than that in the traditional process. More importantly, the strategy can also double the throughput of existing texturing processes and significantly reduce the amount of etching waste. Therefore, the work is expected to provide a promising way to mass produce efficient mono‐Si PERC‐SE solar cells with a superior rear surface, achieved without increasing the number of processing steps, and lower cost.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".