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Record W3094131261 · doi:10.1021/acsnano.0c05179

Bromine Incorporation and Suppressed Cation Rotation in Mixed-Halide Perovskites

2020· article· en· W3094131261 on OpenAlexafffund
Andrew Johnston, Grant Walters, Makhsud I. Saidaminov, Ziru Huang, Koen Bertens, Niina Jalarvo, Edward H. Sargent

Bibliographic record

VenueACS Nano · 2020
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of VictoriaUniversity of Toronto
FundersOak Ridge National LaboratoryOffice of ScienceNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaUniversity of TorontoU.S. Department of Energy
KeywordsHalideBromineMaterials scienceRotation (mathematics)Perovskite (structure)Chemical physicsInorganic chemistryCrystallographyNanotechnologyChemistry

Abstract

fetched live from OpenAlex

Engineering the composition of perovskite active layers has been critical in increasing the efficiency of perovskite solar cells (PSCs) to more than 25% in the latest reports. Partial substitutions of the monovalent cation and the halogen have been adopted in the highest-performing devices, but the precise role of bromine incorporation remains incompletely explained. Here we use quasi-elastic neutron scattering (QENS) to study, as a function of the degree of bromine incorporation, the dynamics of organic cations in triple-cation lead mixed-halide perovskites. We find that the inclusion of bromine suppresses low-energy rotations of formamidinium (FA), and we find that inhibiting FA rotation correlates with a longer-lived carrier lifetime. When the fraction of bromine approaches 0.15 on the halogen site-a composition used extensively in the PSC literature-the fraction of actively rotating FA molecules is minimized: indeed, the fraction of rotating FA is suppressed by more than 25% compared to the bromine-free perovskite.

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 score0.151
Threshold uncertainty score0.318

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 head0.011
GPT teacher head0.193
Teacher spread0.182 · 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

Citations39
Published2020
Admission routes2
Has abstractyes

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