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Record W7117550083 · doi:10.1021/acs.langmuir.5c05430

Grain Boundary-Induced Work Function Heterogeneity in Yb <sub>2</sub> O <sub>3</sub> Thin Films on ITO

2025· article· en· W7117550083 on OpenAlexafffund
Guillermo Lozano-Onrubia, Peter Brodersen, Amaya‐Roncancio Sebastian, Haozhe Wang, Yun Zhong, Zeke Liu, Mikhail A. Nazarov, Lu Zheng-Hong, Gilbert C. Walker

Bibliographic record

VenueLangmuir · 2025
Typearticle
Languageen
FieldMaterials Science
TopicElectronic and Structural Properties of Oxides
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsWork functionKelvin probe force microscopeCathodeIndium tin oxideElectronGrain boundaryThin filmWork (physics)Ytterbium

Abstract

fetched live from OpenAlex

Ytterbium oxide offers innovations in photovoltaics and organic light emitting as it enhances electron injection between the cathode and the electron injection layer. That said, the tuning as well as the heterogeneity of its work function remain poorly understood. Here, we report a systematic study of the thickness-dependent work function of thermally evaporated Yb 2 O 3 on indium tin oxide using pulsed force Kelvin probe force microscopy (PF-KPFM). The results demonstrate the reduction of the work function as a function of thickness, which has its origin in the formation of the interfacial dipole at the interface of both materials. High-resolution PF-KPFM mapping reveals the heterogeneity of the work function, especially at the grain boundaries. Based on these observations, we postulate a cascade mechanism for the electron transport in which the lateral variations in the work function in the electron injection layer facilitate the transport of electrons from the cathode to the electron transport layer. These results suggest that charge transport in electron injection layers may proceed through multistep rather than few-step pathways.

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 categoriesMeta-epidemiology (narrow)
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 score0.028
Threshold uncertainty score1.000

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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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.

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

Citations1
Published2025
Admission routes2
Has abstractyes

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