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Record W4415501231 · doi:10.1021/jacs.5c08872

Tuning the f Band for Enhanced Surface Redox in Strained Rare Earth Oxides

2025· article· en· W4415501231 on OpenAlexaff
Hongyang Su, Jing Chai, Zixuan Guan, Hendrik Bluhm, Ziyun Zhang, Yidan Cao, Zhi Liu, Yuanhua Lin, William C. Chueh, Liang Zhang, Di Chen

Bibliographic record

VenueJournal of the American Chemical Society · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsCanadian Light Source (Canada)
FundersNational Key Research and Development Program of ChinaState Key Laboratory of New Ceramics and Fine ProcessingBasic Energy SciencesTsinghua UniversityNational Natural Science Foundation of ChinaStanford University
KeywordsRedoxCatalysisDissociation (chemistry)Band gapDensity functional theoryTensile strainHydrogenStrain (injury)Electronic band structure

Abstract

fetched live from OpenAlex

) are essential in catalysis due to their 4f band-governed surface redox properties, which influence crucial reactions such as hydrogen dissociation and water formation. However, correlating the 4f band structure with catalytic activity has been a long-standing challenge due to the complexities of manipulating and characterizing 4f electrons. Here, we demonstrate that tensile strain effectively modulates the 4f electronic structure, narrowing the band gap and activating surface oxygen, leading to enhanced redox activity. Using atomically flat ceria ultrathin films under up to a 7% biaxial strain range, we observed a five-fold increase in surface reaction kinetics via time-resolved ambient-pressure X-ray photoelectron spectroscopy. Complementary density functional theory calculations reveal that the tensile strain reduces energy barriers for key catalytic steps by narrowing the 4f-2p band gap. These findings highlight the RE 4f electronic structure as a critical descriptor for catalysis and demonstrate the utility of atomically flat model systems.

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 score0.003

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 head0.010
GPT teacher head0.276
Teacher spread0.266 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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