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Record W3100967944

NO-assisted molecular-beam epitaxial growth of nitrogen substituted EuO

2012· article· en· W3100967944 on OpenAlexaff
Ryan Christopher Wicks, S. G. Altendorf, Christian Caspers, H. Kierspel, Ronny Sutarto, L. H. Tjeng, A. Damascelli

Bibliographic record

VenueMax Planck Digital Library · 2012
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic and transport properties of perovskites and related materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFerromagnetismMolecular beam epitaxyNitrogenAnalytical Chemistry (journal)EpitaxyThin filmX-ray photoelectron spectroscopyMaterials sciencePhotoemission spectroscopyFermi levelChemistryInorganic chemistryCondensed matter physicsNuclear magnetic resonanceNanotechnologyOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

We have investigated a method for substituting oxygen with nitrogen in EuO thin films, which is based on molecular beam epitaxy distillation with NO gas as the oxidizer. By varying the NO gas pressure, we produce crystalline, epitaxial EuO1-xNx films with good control over the films’ nitrogen concentration. In situ x-ray photoemission spectroscopy reveals that nitrogen substitution is connected to the formation Eu3+4f6 and a corresponding decrease in the number of Eu2+4f7, indicating that nitrogen is being incorporated in its 3− oxidation state. While small amounts of Eu3+ in over-oxidized Eu1-δO thin films lead to a drastic suppression of the ferromagnetism, the formation of Eu3+ in EuO1-xNx still allows the ferromagnetic phase to exist with an unaffected Tc, thus providing an ideal model system to study the interplay between the magnetic f7 (J = 7/2) and the non-magnetic f6 (J = 0) states close to the Fermi level.

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 score0.002

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 head0.007
GPT teacher head0.173
Teacher spread0.166 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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