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Record W2885631644 · doi:10.1038/s41598-018-30802-1

Large-Scale Encapsulation of Magnetic Iron Oxide Nanoparticles via Syngas Photo-Initiated Chemical Vapor Deposition

2018· article· en· W2885631644 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesHydro-QuébecNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsNanoparticleCoatingChemical engineeringMaterials scienceChemical vapor depositionIron oxide nanoparticlesHigh-resolution transmission electron microscopyIron oxideX-ray photoelectron spectroscopyOxideMagnetic nanoparticlesSolventNanotechnologyOrganic chemistryChemistryTransmission electron microscopy

Abstract

fetched live from OpenAlex

Photo-initiated chemical vapor deposition (PICVD) has been adapted for use in a jet-assisted fluidized bed configuration, allowing for the encapsulation of magnetic iron oxide nanoparticles on a larger scale than ever reported (5 g). This new methodology leads to a functional coating with a thickness of 1.4-10 nm, confirmed by HRTEM and TGA. XPS and TOF-SIMS characterization confirm that the coating is composed of both aliphatic and polymerized carbon chains, with incorporated organometallic bonds and oxygen-containing moieties. UV-Vis absorbance spectra show that the coating improved dispersion in non-polar solvents, such as n-dodecane. This process represents a first step towards the large-scale, solvent-free post-synthesis processing of nanoparticles to impart a functional coating.

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 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 score0.438
Threshold uncertainty score0.592

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)0.0000.000

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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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