Rear Surface Passivation for Ink‐Based, Submicron CuIn(S, Se)<sub>2</sub> Solar Cells
Bibliographic record
Abstract
Abstract A N, N‐dimethylformamide and thiourea‐based route is developed to fabricate submicron (0.55 and 0.75 µm) thick CuIn(S,Se)2 (CISSe) thin films for photovoltaic applications, addressing challenges of material usage, throughput, and manufacturing costs. However, reducing the absorber film thickness below 1 µm in a regular CISSe solar cell decreases the device efficiency due to losses at the highly‐recombinative, and mediocre‐reflective Mo/CISSe rear interface. For the first time, to mitigate the rear recombination losses, a novel rear contacting structure involving a surface passivation layer and point contact openings is developed for solution processed CISSe films and demonstrated in tangible devices. An atomic layer deposited Al2O3 film is employed to passivate the Mo/CISSe rear surface while precipitates formed via chemical bath deposition of CdS are used to generate nanosized point openings. Consequently, Al2O3 passivated CISSe solar cells show an increase in the open‐circuit voltage (VOC) and short‐circuit current density when compared to reference cells with equivalent absorber thicknesses. Notably, a VOC increase of 59 mV contributes to active area efficiencies of 14.2% for rear passivated devices with 0.75 µm thick absorber layers, the highest reported value for submicron‐based solution processed, low bandgap CISSe solar cells.
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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".