In Situ Ligand Bonding Management of CsPbI<sub>3</sub> Perovskite Quantum Dots Enables High‐Performance Photovoltaics and Red Light‐Emitting Diodes
Bibliographic record
Abstract
Abstract To fine‐tune surface ligands towards high‐performance devices, we developed an in situ passivation process for all‐inorganic cesium lead iodide (CsPbI3) perovskite quantum dots (QDs) by using a bifunctional ligand, L‐phenylalanine (L‐PHE). Through the addition of this ligand into the precursor solution during synthesis, the in situ treated CsPbI3 QDs display significantly reduced surface states, increased vacancy formation energy, higher photoluminescence quantum yields, and much improved stability. Consequently, the L‐PHE passivated CsPbI3 QDs enabled the realization of QD solar cells with an optimal efficiency of 14.62 % and red light‐emitting diodes (LEDs) with a highest external quantum efficiency (EQE) of 10.21 %, respectively, demonstrating the great potential of ligand bonding management in improving the optoelectronic properties of solution‐processed perovskite QDs.
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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".