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Record W2905437733 · doi:10.1002/adfm.201806677

Nitrogen Tuned Charge Redistribution and Orbital Reconfiguration in Fe/MgO Interface for Significant Interfacial Magnetism Tunability

2018· article· en· W2905437733 on OpenAlexaff
Shiru Wang, Mingke Yao, Zirun Li, Chun Feng, Lei Wang, Xiaolei Tang, Peng Kang, Bin Zhang, Wenbo Mi, Guanghua Yu

Bibliographic record

VenueAdvanced Functional Materials · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsMcGill University
FundersBeijing Education Committee Cooperation Building Foundation ProjectNational Natural Science Foundation of China
KeywordsMaterials scienceMagnetismCondensed matter physicsMagnetic anisotropyFerromagnetismAnisotropyMagnetic fieldMagnetizationOpticsPhysics

Abstract

fetched live from OpenAlex

Abstract Modulating the orbital configuration of ferromagnetic metal (FM)/metal‐oxide (MO) interfaces is crucial for obtaining a controllable interfacial magnetism for constructing energy‐efficient magnetic memory and logic devices. The traditional works of orbital regulation depend on external fields, such as electric field, temperature field, and stress field. This work proposes a novel orbital modulation strategy by modifying the coordination environment of FM/MO interface with nitrogen (N) incorporation. By preparing a Fe/MgO bilayer at a N 2 atmosphere, N atoms occupy the interstitial sites of the Fe lattice, which induces a charge redistribution at the Fe/MgO interface and toggles a prominent orbital reconstruction of Fe with an increment of out‐of‐plane orbital occupancy. Therefore, the orbital magnetism is tuned effectively, which remarkably strengthens the interfacial magnetic anisotropy energy by 0.6 erg cm −2 and enables a broad magnetic anisotropy tunability from in‐plane to perpendicular direction. Besides, the Fe thickness for maintaining perpendicular magnetic anisotropy extends from less than 1 to 3 nm, which is favorable for improving the signal‐to‐noise ratio and stability of devices in nanoscale. These findings provide an external‐field‐independent strategy of orbital engineering for tailoring obit‐controlled performance at FM/MO heterointerfaces, which practically advances the magnetic storage and logic devices.

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 score0.025
Threshold uncertainty score0.997

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)0.0040.000

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 head0.243
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.

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
Published2018
Admission routes1
Has abstractyes

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