Present Status of Solution‐Processing Routes for Cu(In,Ga)(S,Se)<sub>2</sub> Solar Cell Absorbers
Bibliographic record
Abstract
Abstract Photovoltaic technologies offer a sustainable solution to the challenge of meeting increasing energy demands. Chalcopyrite Cu(In,Ga)(S,Se)2, short CIGS—thin‐film solar cells—having intrinsically p‐type absorbers with a tunable direct bandgap—exhibits one of the highest stabilized power conversion efficiencies of 23.35%, utilizing absorbers typically fabricated via vacuum deposition methods. Research is increasingly devoted to absorbers deposited by solution processing techniques, which may inherently improve material usage, increase throughput, and lower financial barriers to commercialization. However, the performance of current devices with solution‐processed absorbers is still falling short of their vacuum‐processed counterparts with record power conversion efficiencies up to 18.7% reported to date. While hydrazine solvent‐based routes offer reduced residual impurities, their toxicity poses hindrances to widespread adoption. Alternatively, less toxic and environmentally friendly routes based on protic and aprotic solvents are being researched and are showing promising device efficiencies well above 14%. This review describes the current status of CIGS solar cell absorber layers fabricated by pure solution‐based deposition methods, provides a comparison of champion solution‐processed devices (with and without hydrazine), and offers an outlook for future improvements.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".