Over 13% Efficient, Ambient Air‐Processed CuIn(S,Se)<sub>2</sub> Solar Cells via Compositional Engineering of Molecular Inks
Bibliographic record
Abstract
A dimethylformamide (DMF) and thiourea (TU)‐based ink deposition route is used to fabricate narrow bandgap (≈1.0 eV) CuIn(S,Se)2 (CISSe) films with Cu‐poor ([Cu]/[In] = 0.85), stoichiometric ([Cu]/[In] = 1.0), and Cu‐rich ([Cu]/[In] = 1.15) compositions for photovoltaic applications. Characterization of KCN‐ or (NH4)2S‐treated Cu‐rich absorber films using X‐ray diffraction and scanning electron microscopy confirms the removal of copper‐selenide phases from the film surface, while electron backscatter diffraction measurements and depth‐dependent energy‐dispersive X‐ray spectroscopy indicate remnant copper‐selenides in the absorber layer bulk. Contrary to best practice for vacuum‐processed cells, optimum [Cu]/[In] ratios appear to be stoichiometric, rather than Cu‐poor, in DMF–TU‐based CISSe devices. Accordingly, stoichiometric film compositions yield large‐grained (≈2 μm) absorber layers with smooth absorber surfaces (root mean square roughness <20 nm) and active area device efficiencies of 13.2% (without antireflective coating). Notably, these devices reach 70.0% of the Shockley–Queisser limit open‐circuit voltage (i.e., 526 mV at Eg of 1.01 eV), which is among the highest for ink‐based CISSe devices.
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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".