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Record W2951692015 · doi:10.1021/acsenergylett.9b00892

Suppressed Ion Migration in Reduced-Dimensional Perovskites Improves Operating Stability

2019· article· en· W2951692015 on OpenAlexafffund
Ziru Huang, Andrew H. Proppe, Hairen Tan, Makhsud I. Saidaminov, Furui Tan, Anyi Mei, Chih‐Shan Tan, Mingyang Wei, Yi Hou, Hongwei Han, Shana O. Kelley, Edward H. Sargent

Bibliographic record

VenueACS Energy Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaU.S. NavyGovernment of Canada
KeywordsPerovskite (structure)IodideHalideMaterials scienceOxidizing agentIonDegradation (telecommunications)Photovoltaic systemOptoelectronicsChemical engineeringChemistryInorganic chemistryCrystallographyElectronic engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Impressive progress in halide perovskite solar cells motivates further work to improve operating stability. It is known that ion-migration-driven decomposition represents a degradation pathway in perovskite solar cells and that it can occur within the perovskite material even in well-encapsulated devices. Here we find that quasi-two-dimensional (2.5D) perovskites suppress this ion-migration-induced degradation. Using TOF-SIMS, we confirm that iodide migration occurs in bulk perovskite photovoltaic devices operating at their maximum power point (MPP). We observe that iodine ions migrate across the spiro-OMeTAD layer to the spiro/gold contact interface, oxidizing and deteriorating the gold at the interface. In contrast, we find that large ⟨ n ⟩ 2.5D perovskites exhibit a significantly reduced rate of ion migration compared to 3D devices and exhibit less than 1% relative PCE loss in over 80 h of continuous operation at MPP, whereas the PCE of 3D devices diminishes by more than 50% within the first 24 h.

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 score0.001
Threshold uncertainty score0.004

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.183
Teacher spread0.177 · 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

Citations199
Published2019
Admission routes2
Has abstractyes

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Same venueACS Energy LettersSame topicPerovskite Materials and ApplicationsFrench-language works237,207