Reproducible high-quality perovskite single crystals by flux-regulated crystallization with a feedback loop
Bibliographic record
Abstract
Abstract Controlling the linear growth rate, a critical factor that determines crystal quality, has been a challenge in solution-grown single crystals due to complex crystallization kinetics influenced by multiple parameters. Here we introduce a flux-regulated crystallization (FRC) method to directly monitor and feedback-control the linear growth rate, circumventing the need to control individual growth conditions. When applied to metal halide perovskites, the FRC maintains a stable linear growth rate for over 40 h in synthesizing CH3NH3PbBr3 and CsPbBr3 single crystals, achieving outstanding crystallinity (quantified by a full width at half-maximum of 15.3 arcsec in the X-ray rocking curve) in a centimetre-scale single crystal. The FRC is a reliable platform for synthesizing high-quality crystals essential for commercialization and systematically exploring crystallization conditions, maintaining a key parameter—the linear growth rate—constant, which enables a comprehensive understanding of the impact of other influencing factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".