Coupling Photogeneration with Thermodynamic Modeling of Light-Induced Alloy Segregation Enables the Identification of Stabilizing Dopants
Bibliographic record
Abstract
Halide segregation in perovskites for photovoltaics and light-emitting diodes is a topic of interest given its impact on long-term device reliability. We sought to develop phase diagrams of alloys that take account not only of temperature and composition but also include the effects of photon fluence: optical excitation that contributes, through the thermalization of excited carriers, to excitation-intensity-dependent phase diagrams. The model accurately replicates the experimentally observed light-induced phase segregation behavior of the MAPb(I,Br) 3 system. From there, we sought to study how best to design new, phase-stable, mixed-halide alloys. Using the model, we explored candidate dopants that could stabilize cubic (FA,Cs)-based mixed-halide perovskites. This leads to the prediction that the pseudohalide anion BF 4 – will suppress phase segregation. Experimentally, we find that BF 4 – incorporates into FA 0.83 Cs 0.17 Pb(I 0.6 Br 0.4 ) 3; and that BF 4 – stabilized absorbers maintain >18% power conversion efficiency (PCE) over 800 h under 1-sun illumination at MPP with no performance loss. The model links photostability with the structure and electronic properties of materials and provides guidance on stabilizing via alloying.
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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".