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Record W4283269044 · doi:10.1038/s43246-022-00262-2

Tracking the evolution of materials and interfaces in perovskite solar cells under an electric field

2022· article· en· W4283269044 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCommunications Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPerovskite (structure)Electric fieldChemical physicsMaterials sciencePhotoemission spectroscopyOptoelectronicsChemical speciesChemistryChemical engineeringX-ray photoelectron spectroscopyPhysicsCrystallography

Abstract

fetched live from OpenAlex

Abstract What causes the instability of perovskite solar cells has been a puzzling problem impeding the development of commercial panels. So far there is limited evidence on the link between device instability and the various materials in each of the stacked layers. Here, we study the chemistry and distribution of various species and the integrity of the functional layers in high-performance inverted perovskite solar cells, with and without an electric field. The distribution of the diffusion species and its impact on the chemical and electronic structures through the transporting layers are measured by photoemission spectroscopy combined with damage-free ion beam sputtering. We find that various species, such as I 2 and PbI 2 , are distributed throughout the organic transporting layers toward the electrode interface. These species are found to be charge neutral, have no impact on the Fermi level, and react little with copper. An electric field, however, can catalyze the electro-decomposition of the perovskite, causing chemical heterogeneity and degradation in device performance.

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.

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)0.001
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 score0.003
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.020
GPT teacher head0.250
Teacher spread0.229 · 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