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Record W2730311289 · doi:10.1021/acs.jpcc.7b04684

Mobile-Ion-Induced Degradation of Organic Hole-Selective Layers in Perovskite Solar Cells

2017· article· en· W2730311289 on OpenAlexafffund
Yicheng Zhao, Wenke Zhou, Hairen Tan, Rui Fu, Qi Li, Fang Lin, Dapeng Yu, Grant Walters, Edward H. Sargent, Qing Zhao

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2017
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoMinistry of Science and Technology of the People's Republic of ChinaNederlandse Organisatie voor Wetenschappelijk OnderzoekNational Natural Science Foundation of China
KeywordsMaterials sciencePerovskite (structure)HalideIonLayer (electronics)Degradation (telecommunications)Perovskite solar cellSolar cellNanotechnologyOptoelectronicsSemiconductorChemical engineeringInorganic chemistryChemistryOrganic chemistryTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

Organometal halide perovskites are mixed electronic–ionic semiconductors. It is imperative to develop a deeper understanding of how ion-migration behavior in perovskites impacts the long-term operational stability of solar cells. In this work, we found that ion penetration from the perovskite layer into the adjacent organic hole-selective layer is a crucial cause of performance degradation in perovskite solar cells. The monovalent cation, namely, methylammonium (MA + ), is the main ion species that penetrates into the organic hole-selective layer of Spiro-MeOTAD because of the built-in electric field during operation. The incorporation of MA + induces deep-level defects in the Spiro-MeOTAD layer and thereby deteriorates the hole-transporting ability of Spiro-MeOTAD, degrading solar cell performance. Our work points to two ways to improve the stability of perovskite solar cells: one is to insert a compact ion-blocking layer between Spiro-MeOTAD and perovskite, and the other is to find a hole-selective layer that is insensitive to extraneous ions (MA + ).

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 score0.001
Threshold uncertainty score0.262

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 head0.009
GPT teacher head0.235
Teacher spread0.225 · 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

Citations140
Published2017
Admission routes2
Has abstractyes

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