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Record W4411940113 · doi:10.1088/2053-1583/adeabe

Atomic resolution detection of gallium-filled 2D silicon vacancies at the epitaxial graphene/SiC interface

2025· article· en· W4411940113 on OpenAlexafffund
Hesham El‐Sherif, Bita Pourbahari, Natalie Briggs, Joshua A. Robinson, Nabil Bassim

Bibliographic record

Venue2D Materials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcMaster University
FundersAir Force Office of Scientific ResearchNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceSiliconEpitaxyInterface (matter)GalliumGrapheneSilicon carbideOptoelectronicsNanotechnologyComposite materialMetallurgyLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract In this study, the generation of Si vacancies during epitaxial graphene (EG) formation and the mechanism of Ga filling the Si vacancies during Ga intercalation are determined. The formation of EG and the subsequent generation of Si vacancies were investigated. Prior to Ga intercalation during the EG formation process, Si vacancies form in the topmost layer of the SiC substrate. Furthermore, these vacancies change the stacking sequence of SiC from its original 6H configuration to a 3C structure. After metal intercalation, the topmost SiC layer showed increased high-angle annular dark-field (HAADF) intensity relative to the bulk, suggesting the incorporation of heavier metals into this layer. This top SiC layer showed a sensitivity to e-beam damage, similar to the metallic bonding observed in two-dimensional (2D) metals. Finally, EELS analysis confirmed the presence of a 2D metal layer at the topmost SiC layer, supporting the conclusion that Ga atoms occupy the Si vacancies formed during Si sublimation.

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)0.001
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 score0.009
Threshold uncertainty score0.897

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 science0.0000.000
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 head0.015
GPT teacher head0.281
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 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

Citations2
Published2025
Admission routes2
Has abstractyes

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