Molecular engineering towards efficientwhite-light-emitting perovskite
Bibliographic record
Abstract
Abstract Low-dimensional hybrid perovskites have demonstrated excellent performance as white-light emitters. The broadband white emission originates from self-trapped excitons (STEs). Since the mechanism of STEs formation in perovskites is still not clear, preparing new low-dimensional white perovskites relies mostly on screening lots of intercalated organic molecules rather than rational design. Here, we report an atom-substituting strategy to trigger STEs formation in layered perovskites. Halogen-substituted phenyl molecules are applied to synthesize perovskite crystals. The halogen-substituents will withdraw electrons from the branched chain (-R-NH 3 + ) of the phenyl molecule. This will result in positive charge accumulation on -R-NH 3 + , and thus stronger Coulomb force of bond (-R-NH 3 + )-(PbBr 4 2− ), which facilitates excitons self-trapping. Our designed white perovskites exhibit photoluminescence quantum yield of 32%, color-rendering index of near 90 and chromaticity coordinates close to standard white-light. Our joint experiment-theory study provides insights into the STEs formation in perovskites and will benefit tailoring white perovskites with boosting performance.
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 | 0.000 | 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".