Extrinsic Doping of Ink‐Based Cu(In,Ga)(S,Se)<sub>2</sub>‐Absorbers for Photovoltaic Applications
Bibliographic record
Abstract
Abstract The addition of cesium into Cu(In,Ga)(S,Se)2‐short CIGSSe‐absorber layers (fabricated via vacuum deposition methods), has most recently culminated in devices with record power conversion efficiencies up to 23.4%. However, research is increasingly being devoted to the development of ink deposition routes to prepare high‐quality CIGSSe thin films while requiring only a fraction of the processing costs. Such non‐vacuum deposition routes must compete with efficiencies of incumbent technologies to find adoption on a wide scale. At present, the performance of ink‐based devices still fall short of their vacuum counterparts with certified champion cell efficiencies up to 17.7%. The recent performance progression for vacuum‐processed CIGSSe exemplifies the importance of controlling the concentration of extrinsic impurities and serves as an inspiration for gains (e.g., morphological, optoelectronic) for devices with ink‐based absorber layers. This article reviews extrinsic doping concepts for CIGSSe‐type absorbers fabricated by ink‐based deposition routes (both nanoparticle dispersions and molecular inks), provides a performance comparison of select high‐efficiency ink‐based devices, and offers an outlook for future process development in general. It is suggested that the mechanisms by which dopant atoms diffuse, interact, and alter the properties of an ink‐based absorber are fundamentally different than those fabricated from vacuum‐based processes, and require further investigation.
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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 | 0.001 | 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".