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Record W2974572630 · doi:10.1002/anie.201909109

Halogen Vacancies Enable Ligand‐Assisted Self‐Assembly of Perovskite Quantum Dots into Nanowires

2019· article· en· W2974572630 on OpenAlexaff
Jun Pan, Xiyan Li, Xiwen Gong, Jun Yin, Dianli Zhou, Lutfan Sinatra, Ren‐Wu Huang, Jiakai Liu, Jie Chen, İbrahim Dursun, Ahmed M. El‐Zohry, Makhsud I. Saidaminov, Hong‐Tao Sun, Omar F. Mohammed, Changhui Ye, Edward H. Sargent, Osman M. Bakr

Bibliographic record

VenueAngewandte Chemie International Edition · 2019
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersKing Abdullah University of Science and Technology
KeywordsQuantum dotNanowireHalogenPerovskite (structure)Self-assemblyNanotechnologyMaterials scienceLigand (biochemistry)ChemistryCrystallographyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Interest has been growing in defects of halide perovskites in view of their intimate connection with key material optoelectronic properties. In perovskite quantum dots (PQDs), the influence of defects is even more apparent than in their bulk counterparts. By combining experiment and theory, we report herein a halide‐vacancy‐driven, ligand‐directed self‐assembly process of CsPbBr 3 PQDs. With the assistance of oleic acid and didodecyldimethylammonium sulfide, surface‐Br‐vacancy‐rich CsPbBr 3 PQDs self‐assemble into nanowires (NWs) that are 20–60 nm in width and several millimeters in length. The NWs exhibit a sharp photoluminescence profile (≈18 nm full‐width at‐half‐maximum) that peaks at 525 nm. Our findings provide insight into the defect‐correlated dynamics of PQDs and defect‐assisted fabrication of perovskite materials and devices.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.223
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations75
Published2019
Admission routes1
Has abstractyes

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