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Record W2045730250 · doi:10.1103/physrevb.66.115414

Morphology of the rutile (110) surface after low sputter dose and annealing

2002· article· en· W2045730250 on OpenAlexafffund
Aiguo Cai, P. Piercy

Bibliographic record

VenuePhysical review. B, Condensed matter · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnnealing (glass)RutileMaterials scienceSputteringArgonLow-energy electron diffractionSurface diffusionIonElectron diffractionCondensed matter physicsAnalytical Chemistry (journal)CrystallographyPhysicsAtomic physicsDiffractionOpticsThin filmChemistryNanotechnologyAdsorption

Abstract

fetched live from OpenAlex

The kinetics of diffusion-mediated smoothing of a ${\mathrm{TiO}}_{2}$ rutile (110) surface is studied over atomic length scales, by using a spot profile analysis of low-energy electron-diffraction data to characterize the morphology of the surface during thermal annealing. After the random removal of less than 1 ML of atoms by sputtering with an argon ion beam, the interface width and the distribution of terrace heights were found to stay nearly constant during annealing at 800 K, with terraces at just two heights making up \ensuremath{\approx}90% of the surface area over lateral distances of \ensuremath{\approx}400 \AA{}. Meanwhile, the coarsening of this nearly two-dimensional island structure is characterized by the growth of the average terrace width l from 20 to 60 \AA{} with annealing time, following $l\ensuremath{\sim}{t}^{\ensuremath{\beta}}$ with exponent $\ensuremath{\beta}=0.24\ifmmode\pm\else\textpm\fi{}0.04.$ In addition, the stability of an 8% occupation of a third terrace height indicates negligible diffusion flux between layers during annealing. These results are compared with existing models for the microscopic dynamics involved.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.998

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)0.0200.003

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 head0.252
Teacher spread0.238 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations3
Published2002
Admission routes2
Has abstractyes

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