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Record W2607648677 · doi:10.1021/acs.langmuir.7b00773

Nanoparticle Size Control in Microemulsion Synthesis

2017· article· en· W2607648677 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

VenueLangmuir · 2017
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsMicroemulsionMicelleParticle sizeDispersityNanoparticleNanocrystalHydrodynamic radiusChemical engineeringRADIUSNanotechnologyChemical physicsMaterials scienceIonChemistryDynamic light scatteringPulmonary surfactantOrganic chemistryPhysical chemistryAqueous solutionComputer science

Abstract

fetched live from OpenAlex

O nanocrystals of varying size are prepared in Igepal-stabilized microemulsions. Correlations between microemulsion composition, micelle hydrodynamic radius, and final nanoparticle size are established and shed light on the mechanism of particle size control. Under the conditions considered here, size control appears to be primarily governed by the number of micelles and the quantities of precursor ions. More specifically, the number of NPs formed can be successfully correlated with the number of micelles present and final NP size is, in turn, determined by the number of nuclei and the total amount of material available for nanocrystal formation. This insight into nanoparticle formation facilitates the selection of appropriate synthetic conditions for the preparation of populations of a targeted size.

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)0.000
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 score0.097
Threshold uncertainty score0.836

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)0.0010.000

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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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