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Record W2000616038 · doi:10.1063/1.4916818

Atomic layer deposition of superparamagnetic and ferrimagnetic magnetite thin films

2015· article· en· W2000616038 on OpenAlexaff
Yijun Zhang, Ming Liu, Yuepeng Zhang, Xing Chen, Wei Ren, Zuo‐Guang Ye

Bibliographic record

VenueJournal of Applied Physics · 2015
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSuperparamagnetismFerrimagnetismMaterials scienceThin filmAnisotropy energySpintronicsAtomic layer depositionAnnealing (glass)MagnetiteMagnetic anisotropyNanotechnologyRemanenceMagnetizationGrain sizeCondensed matter physicsFerromagnetismMetallurgyMagnetic field

Abstract

fetched live from OpenAlex

One of the key challenges in realizing superparamagnetism in magnetic thin films lies in finding a low-energy growth way to create sufficiently small grains and magnetic domains which allow the magnetization to randomly and rapidly reverse. In this work, well-defined superparamagnetic and ferrimagnetic Fe3O4 thin films are successfully prepared using atomic layer deposition technique by finely controlling the growth condition and post-annealing process. As-grown Fe3O4 thin films exhibit a conformal surface and poly-crystalline nature with an average grain size of 7 nm, resulting in a superparamagnetic behavior with a blocking temperature of 210 K. After post-annealing in H2/Ar at 400 °C, the as-grown α−Fe2O3 sample is reduced to Fe3O4 phase, exhibiting a ferrimagnetic ordering and distinct magnetic shape anisotropy. Atomic layer deposition of magnetite thin films with well-controlled morphology and magnetic properties provides great opportunities for integrating with other order parameters to realize magnetic nano-devices with potential applications in spintronics, electronics, and bio-applications.

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 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 score0.256
Threshold uncertainty score0.413

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)0.0000.000

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 head0.013
GPT teacher head0.212
Teacher spread0.198 · 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

Citations25
Published2015
Admission routes1
Has abstractyes

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