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Record W3089150396 · doi:10.1002/cjce.23889

Synthesis of single‐phase and controlled monodisperse magnetite <scp>Fe<sub>3</sub>O<sub>4</sub></scp> nanoparticles

2020· article· en· W3089150396 on OpenAlexafffundvenue
Arnaud Gandon, Chinh Chien Nguyen, Serge Kaliaguine, Trong On

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMagnetiteMaterials scienceDispersityParticle sizeChemical engineeringNanoparticleFourier transform infrared spectroscopyTransmission electron microscopyParticle (ecology)X-ray photoelectron spectroscopyIron oxideNanotechnologyAnalytical Chemistry (journal)ChemistryOrganic chemistryPolymer chemistry

Abstract

fetched live from OpenAlex

Abstract We report a simple solvothermal method for the synthesis of monodisperse magnetite nanoparticles with a controlled particle size within the range of 40 to 200 nm from available and inexpensive single iron precursor (FeCl 3 ) and, as co‐capping agents, sodium acetate and ethylene diamine. The particle size can be easily controlled by the reaction time of synthesis. Transmission electron microscopy, x‐ray diffraction, Fourier transform infrared spectroscopy, and x‐ray photoelectron spectroscopy techniques were used to investigate the obtained particles. The results revealed that the resulting iron oxide particles exhibit a single magnetite Fe 3 O 4 phase and high stability in air even for four months. The high air stability of these magnetite could be due to the surfactant capped on the particle surface. This method of synthesis has some advantages including simplicity, high product quality, and acceptable reproducibility. Therefore, the magnetite particles can be used for different applications such as in catalysis and drug delivery.

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)0.002
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 score0.006
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.180
Teacher spread0.171 · 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

Citations16
Published2020
Admission routes3
Has abstractyes

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