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Record W4224103215 · doi:10.1002/smll.202200414

A Confinement‐Driven Nucleation Mechanism of Metal Oxide Nanoparticles Obtained via Thermal Decomposition in Organic Media

2022· article· en· W4224103215 on OpenAlexfundno aff
Geoffrey Cotin, Benoı̂t Heinrich, Francis Perton, Céline Kiefer, Grégory Francius, Damien Mertz, Barbara Freis, Benoît P. Pichon, Jean‐Marc Strub, Sarah Cianférani, Nathaly Ortiz Peña, Dris Ihiawakrim, David Portehault, Ovidiu Ersen, Amir Khammari, Matthieu Picher, Florian Banhart, Clément Sánchez, Sylvie Bégin‐Colin

Bibliographic record

VenueSmall · 2022
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsnot available
FundersCHIST-ERAInstitut National Du CancerUniversiti Teknikal Malaysia MelakaChina Scholarship CouncilUniversité de LorraineUniversité de StrasbourgConseil Régional d'AlsaceAgence Nationale de la RechercheCentre National de la Recherche ScientifiqueCNIBEquipex
KeywordsNucleationNanoreactorNanoparticleMaterials scienceStearateThermal decompositionChemical engineeringOxideNanotechnologyChemistryOrganic chemistryComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Thermal decomposition is a very efficient synthesis strategy to obtain nanosized metal oxides with controlled structures and properties. For the iron oxide nanoparticle synthesis, it allows an easy tuning of the nanoparticle's size, shape, and composition, which is often explained by the LaMer theory involving a clear separation between nucleation and growth steps. Here, the events before the nucleation of iron oxide nanocrystals are investigated by combining different complementary in situ characterization techniques. These characterizations are carried out not only on powdered iron stearate precursors but also on a preheated liquid reaction mixture. They reveal a new nucleation mechanism for the thermal decomposition method: instead of a homogeneous nucleation, the nucleation occurs within vesicle-like-nanoreactors confining the reactants. The different steps are: 1) the melting and coalescence of iron stearate particles, leading to "droplet-shaped nanostructures" acting as nanoreactors; 2) the formation of a hitherto unobserved iron stearate crystalline phase within the nucleation temperature range, simultaneously with stearate chains loss and Fe(III) to Fe(II) reduction; 3) the formation of iron oxide nuclei inside the nanoreactors, which are then ejected from them. This mechanism paves the way toward a better mastering of the metal oxide nanoparticles synthesis and the control of their properties.

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)0.000
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 score0.037
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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.

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

Citations18
Published2022
Admission routes1
Has abstractyes

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