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Record W2410986776 · doi:10.1038/srep18401

Controlled Phase and Tunable Magnetism in Ordered Iron Oxide Nanotube Arrays Prepared by Atomic Layer Deposition

2016· article· en· W2410986776 on OpenAlexafffund
Yijun Zhang, Ming Liu, Bin Peng, Ziyao Zhou, Xing Chen, Shuming Yang, Zhuangde Jiang, Jie Zhang, Wei Ren, Zuo‐Guang Ye

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsSimon Fraser University
FundersFundamental Research Funds for the Central UniversitiesHigher Education Discipline Innovation ProjectNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsAtomic layer depositionMaterials scienceSuperparamagnetismFerrimagnetismNanotechnologyMagnetismSpintronicsOxideAnnealing (glass)Chemical engineeringThin filmMagnetizationFerromagnetismCondensed matter physicsMetallurgyMagnetic field

Abstract

fetched live from OpenAlex

Highly-ordered and conformal iron oxide nanotube arrays on an atomic scale are successfully prepared by atomic layer deposition (ALD) with controlled oxidization states and tunable magnetic properties between superparamagnetism and ferrimagnetism. Non-magnetic α-Fe2O3 and superparamagnetic Fe3O4 with a blocking temperature of 120 K are in-situ obtained by finely controlling the oxidation reaction. Both of them exhibit a very small grain size of only several nanometers due to the nature of atom-by-atom growth of the ALD technique. Post-annealing α-Fe2O3 in a reducing atmosphere leads to the formation of the spinel Fe3O4 phase which displays a distinct ferrimagnetic anisotropy and the Verwey metal-insulator transition that usually takes place only in single crystal magnetite or thick epitaxial films at low temperatures. The ALD deposition of iron oxide with well-controlled phase and tunable magnetism demonstrated in this work provides a promising opportunity for the fabrication of 3D nano-devices to be used in catalysis, spintronics, microelectronics, data storages 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)0.001
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 score0.044
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.237
Teacher spread0.230 · 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

Citations19
Published2016
Admission routes2
Has abstractyes

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