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Magnetic Properties of Mg<sub>0.4</sub>Ca<sub>0.6</sub>Fe<sub>2</sub>O<sub>4</sub> Nanoparticles Synthesized by Sol-Gel Method for Hyperthermia Treatment

2014· article· en· W1972245077 on OpenAlex
Angel Manuel Escamilla‐Pérez, Dora A. Cortés‐Hernández, J.M. Almanza-Robles, Diego Mantovani, Pascale Chevallier

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

VenueKey engineering materials · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Synthesis of Ferrites
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSuperparamagnetismMaterials scienceOrthorhombic crystal systemSpinelFerrite (magnet)Analytical Chemistry (journal)HematiteTransmission electron microscopyEthylene glycolMagnetic refrigerationNuclear chemistryNanocrystalline materialMetallurgyMagnetizationCrystallographyNanotechnologyCrystal structureChemical engineeringChemistryComposite materialMagnetic fieldChromatography

Abstract

fetched live from OpenAlex

Powders of Mg 0.4 Ca 0.6 Fe 2 O 4 were prepared by sol-gel using ethylene glycol and Mg, Ca and Fe nitrates as starting materials. Those powders were heat treated at different temperatures (300, 400, 500 and 600 °C) for 30 min. The materials obtained were characterized by X-ray diffraction (XRD) and vibrating sample magnetometry (VSM). The Ca-Mg ferrite with the most appropriate magnetic properties was further analyzed by transmission electron microscopy (TEM). The heating capability of the nanoferrites was also tested via magnetic induction. The XRD patterns of these Ca-Mg ferrites showed a cubic inverse spinel structure. Furthermore, neither traces of hematite nor orthorhombic Ca ferrite phases were detected. Moreover, all the Ca-Mg ferrites are superparamagnetic and the particle size distribution of these Ca-Mg magnetic nanoparticles exhibits an average diameter within the range of 10-14 nm. The needed temperature for hyperthermia treatment was achieved at around 12 min.

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)0.009
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0060.005
Meta-epidemiology (broad)0.0090.002
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0060.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.015
GPT teacher head0.207
Teacher spread0.192 · 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