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Layer-by-layer growth of sodium chloride overlayers on an Fe(001)-p(1 × 1)O surface

2012· article· en· W2003564431 on OpenAlexafffund
Antoni Tekiel, Jessica M. Topple, Yoichi Miyahara, Peter Grütter

Bibliographic record

VenueNanotechnology · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsMaterials scienceLayer (electronics)SodiumSurface layerChlorideSurface (topology)Chemical engineeringComposite materialMetallurgyGeometry

Abstract

fetched live from OpenAlex

Ultra-thin NaCl films epitaxially grown on an Fe(001)-p(1 × 1)O surface have been investigated in ultra-high vacuum by non-contact atomic force microscopy and low energy electron diffraction. It has been found that at temperatures below 145 °C NaCl initially grows as monoatomic thick islands on substrate terraces, while at temperatures above 175 °C biatomic thick islands are also formed at substrate step edges. Both types of islands have the same Fe(001)-O[100] [parallel] NaCl(001)[110] orientation, leading to a (4 × 4) superstructure, where the NaCl unit cell is oriented at 45° with respect to the substrate. Interestingly, no c(2 × 2) superstructure with the NaCl unit cell oriented at 0° has been observed. The oxygen on the iron surface promotes layer-by-layer growth, resulting in atomically flat films with 40-60 nm wide terraces at coverages ranging from 0.75 to 12 ML. Such NaCl films are of much higher quality than MgO films grown on Fe(001) and Fe(001)-p(1 × 1)O surfaces and represent a unique epitaxial system of an alkali halide on a pure metallic substrate. The reduced number of defects and the layer-by-layer mode of growth make this system very attractive for applications where an atomically defined tunnel barrier is required to control the properties of a device.

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 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 score0.139
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 science0.0010.000
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 head0.014
GPT teacher head0.238
Teacher spread0.224 · 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

Citations14
Published2012
Admission routes2
Has abstractyes

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