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Record W2006304022 · doi:10.1126/science.aaa2725

Low trap-state density and long carrier diffusion in organolead trihalide perovskite single crystals

2015· article· en· W2006304022 on OpenAlexafffund
Dong Shi, Valerio Adinolfi, Riccardo Comin, Mingjian Yuan, Erkki Alarousu, Andrei Buin, Yin Chen, Sjoerd Hoogland, A. Rothenberger, Khabiboulakh Katsiev, Yaroslav Losovyj, Xin Zhang, P. A. Dowben, Omar F. Mohammed, Edward H. Sargent, Osman M. Bakr

Bibliographic record

VenueScience · 2015
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaKing Abdullah University of Science and Technology
KeywordsTrihalideTrap (plumbing)DiffusionPerovskite (structure)Chemical physicsChemistryMaterials scienceAnalytical Chemistry (journal)CrystallographyInorganic chemistryEnvironmental chemistryThermodynamicsPhysicsHalide

Abstract

fetched live from OpenAlex

The fundamental properties and ultimate performance limits of organolead trihalide MAPbX3 (MA = CH3NH3(+); X = Br(-) or I(-)) perovskites remain obscured by extensive disorder in polycrystalline MAPbX3 films. We report an antisolvent vapor-assisted crystallization approach that enables us to create sizable crack-free MAPbX3 single crystals with volumes exceeding 100 cubic millimeters. These large single crystals enabled a detailed characterization of their optical and charge transport characteristics. We observed exceptionally low trap-state densities on the order of 10(9) to 10(10) per cubic centimeter in MAPbX3 single crystals (comparable to the best photovoltaic-quality silicon) and charge carrier diffusion lengths exceeding 10 micrometers. These results were validated with density functional theory calculations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.015
GPT teacher head0.224
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), 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

Citations5,060
Published2015
Admission routes2
Has abstractyes

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