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Record W1548325242 · doi:10.1002/9780470022184.hmm420

Magnetic Ultrathin Films

2007· other· en· W1548325242 on OpenAlexaff
Bretislav Heinrich, J. F. Cochran

Bibliographic record

VenueHandbook of Magnetism and Advanced Magnetic Materials · 2007
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMaterials scienceThin filmMonolayerMagnetic anisotropySubstrate (aquarium)Surface roughnessPulsed laser depositionAnisotropyFerromagnetismSputter depositionDeposition (geology)SputteringCondensed matter physicsNanotechnologyMagnetizationOpticsComposite materialMagnetic fieldPhysics

Abstract

fetched live from OpenAlex

Abstract The magnetic properties of ultrathin ferromagnetic films a few monolayers thick differ from the magnetic properties of the same material in bulk form because a large fraction of the atoms in such films are located either on the surfaces or within one monolayer of the surfaces. As a result, the magnetic properties of ultrathin films are very sensitive to surface roughness and to the proximity of foreign atoms at the interfaces with a substrate or with an overlayers film. The first part of this chapter provides an overview of three main methods for the preparation of ultrathin films: thermal deposition, laser pulse deposition, and magnetron sputtering. Emphasis is placed on techniques used to prepare high‐quality films having smooth surfaces and important examples are discussed in detail. The second part reviews magnetic anisotropies in ultrathin films: measuring techniques, the theory of magnetic anisotropies, and the effect of surface roughness on thin‐film anisotropies. The chapter closes with a guide to the literature on thin‐film magnetic anisotropy data for several interesting systems: Fe, Co, Ni films grown on various substrates; Fe films grown on GaAs crystals; Fe/Pt alloys grown on MgO; and bcc Ni films grown on GaAs(001).

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.497
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.2160.000

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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations19
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueHandbook of Magnetism and Advanced Magnetic MaterialsSame topicMagnetic properties of thin filmsFrench-language works237,207