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Synthesis and Magnetic Properties of Fe–Ni Alloy Nanoparticles Obtained by Hydrothermal Reaction

2011· article· en· W1995950375 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced materials research · 2011
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAlloyCoercivityMaterials scienceHydrothermal circulationNanoparticleHydrateChemical engineeringParticle sizeHydrazine (antidepressant)Morphology (biology)Particle (ecology)Reducing agentCrystal structureNuclear chemistryNanotechnologyMetallurgyCrystallographyChemistryOrganic chemistryChromatography

Abstract

fetched live from OpenAlex

The magnetic properties of Fe-Ni alloy nanoparticles with particle size in the range 35-45 nm were prepared by almost simultaneously reducing Fe(II) and Ni(II) solution using hydrazine hydrate as a reducing agent in strong alkaline media for two hours at 80 °C. Chemical composition, crystal structure, morphology, thermal stability and magnetic properties of as synthesized Fe-Ni alloy nanoparticles were systematically characterized by means of XRD, TEM, TG-DSC and VSM. These results indicate that there is a vitally important relationship among particle size, particle morphology, and different mol ratio of FeSO 4 to NiSO 4 . The saturation magnetization ( M s ). and coercivity ( H c ) strongly affected by the composition of Fe-Ni alloy nanoparticles. The hydrothermal reaction is a simple, effective, and low-cost synthetic method to prepare FeNi 3 alloy nanoparticles.

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.

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 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 score0.002
Threshold uncertainty score1.000

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)0.0010.000

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 head0.049
GPT teacher head0.284
Teacher spread0.235 · 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