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Surface Ferromagnetism of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mrow><mml:mi>Fe</mml:mi><mml:mi mathvariant="normal">O</mml:mi></mml:mrow></mml:math> Nanoparticles Revealed by <i>Operando</i> Magnetoelectrochemical Measurements

2023· article· lv· W4372279749 on OpenAlexaff
Hengjun Liu, Fangchao Gu, Xiancheng Sang, Yuanyuan Han, Feihu Zou, Zhaohui Li, Yu-Feng Qin, Li Cai, Yuanyuan Pan, Qiang Cao, Guo‐Xing Miao, Qiang Li

Bibliographic record

VenuePhysical Review Applied · 2023
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Waterloo
FundersProject of Shandong Province Higher Educational Science and Technology ProgramNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of China
KeywordsFerromagnetismMagnetometerSpintronicsLithium (medication)Surface (topology)Materials scienceLithium niobateCurie temperatureNanoparticleCondensed matter physicsNanotechnologyAnalytical Chemistry (journal)PhysicsChemistryOptoelectronicsMathematicsGeometryQuantum mechanicsMagnetic field

Abstract

fetched live from OpenAlex

In view of the long-standing controversy over the surface ferromagnetism of $\mathrm{Fe}\mathrm{O}$, we propose to prepare its nanoparticles based on the lithium-battery reaction mechanism and carry out operando magnetometry measurements. This operando magnetoelectrochemical technology allows accurate magnetic detection of electrochemical synthesis products, effectively avoiding various intractable surface reactions during sample transmission processes. Due to the large surface area of nanosized $\mathrm{Fe}\mathrm{O}$, the magnetometry measurements show strong ferromagnetism and a high Curie temperature of 754 K, which can be attributed to surface frustration and the surface-bulk exchange interaction. These critical results confirm the surface ferromagnetism of $\mathrm{Fe}\mathrm{O}$ and provide valuable insights toward the design of spintronic 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)0.001
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.003

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 head0.018
GPT teacher head0.242
Teacher spread0.223 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2023
Admission routes1
Has abstractyes

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