Protein-Mediated Aqueous Synthesis of Stable Methylammonium Lead Bromide Perovskite Nanocrystals: Implications for Biological and Environmental Applications
Bibliographic record
Abstract
Lead halide perovskites (HPs) hold great potential for the next generation of optoelectronic devices. However, their promise for real-world applications has not been realized because of their poor phase stability and decomposition when subjected to heat, moisture, and light. Here, we report a facile strategy for synthesizing highly stable, compositionally rich, and size-controlled methylammonium lead HP [CH 3 NH 3 PbX 3 (X = Cl, Br, and I)] nanocrystals (HPNCs) in an aqueous environment, assisted by diverse proteins as capping agents. Freeing HPNC production of the complications of organic solvents provides much needed flexibility for the further cost-effective and efficient development of these structures. Stabilized by a delicate ionic balance during synthesis and via interactions with proteins, the synthesized protein-HPNCs exhibit high aqueous and colloidal stability over months. Protein capping also yields promising optical characteristics, including narrow emission wavelength and a photoluminescence quantum yield of up to ∼50%. Furthermore, we demonstrate that this approach can be extended to the synthesis of protein-mediated HPNCs with different chemistries and protein compositions. We anticipate that this method can serve as a general platform that can be used for the fabrication of a wide range of metal HPs for many biological and environmental applications including cell imaging and sensing.
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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) | 0.000 | 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".