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Record W3004419478 · doi:10.1002/advs.201903166

Heterogeneous Supersaturation in Mixed Perovskites

2020· article· en· W3004419478 on OpenAlexafffund
Chih‐Shan Tan, Yi Hou, Makhsud I. Saidaminov, Andrew H. Proppe, Yu Huang, Yicheng Zhao, Mingyang Wei, Grant Walters, Ziyun Wang, Yong‐Biao Zhao, Petar Todorović́, Shana O. Kelley, Lih J. Chen, Edward H. Sargent

Bibliographic record

VenueAdvanced Science · 2020
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of VictoriaUniversity of Toronto
FundersOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaU.S. NavyMinistry of Science and Technology, TaiwanGovernment of Canada
KeywordsPerovskite (structure)SupersaturationPhotovoltaicsMaterials scienceNucleationHalidePhotovoltaic systemSiliconTransmission electron microscopyEnergy conversion efficiencyDiffractionOptoelectronicsNanotechnologyChemical physicsCrystallographyChemistryOpticsInorganic chemistryElectrical engineering

Abstract

fetched live from OpenAlex

Thin-film solar cells based on hybrid lead halide perovskites have achieved certified power conversion efficiencies exceeding 24%, approaching those of crystalline silicon. This motivates deeper studies of the mechanisms that determine their performance. Twin defect sites have been proposed as a source of traps in perovskites, yet their origin and influence on photovoltaic performance remain unclear. It is found that twin defects-observed herein via both transmission electron microscopy and X-ray diffraction-are correlated with the amount of antisolvent added to the perovskite and that twin defects in the highest-performing perovskite photovoltaics are suppressed. Heterogeneous supersaturation nucleation is discussed as a contributor to efficient perovskite-based optoelectronic 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 score0.055
Threshold uncertainty score0.244

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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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

Citations30
Published2020
Admission routes2
Has abstractyes

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