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Record W2119254225 · doi:10.1021/jp2092994

Modeling Solvent Influence on Growth Mechanism of Nanoparticles (Au, Co) Synthesized by Surfactant Free Laser Processes

2012· article· en· W2119254225 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

VenueThe Journal of Physical Chemistry C · 2012
Typearticle
Languageen
FieldEngineering
TopicLaser-Ablation Synthesis of Nanoparticles
Canadian institutionsPolytechnique Montréal
FundersCanada Research Chairs
KeywordsLaser ablation synthesis in solutionNanoparticleMaterials scienceChemical engineeringColloidPulmonary surfactantAnnealing (glass)PhotochemistrySolventMicroviscosityOleylamineLaser ablationCoalescence (physics)Absorption (acoustics)Chemical physicsLaserAnalytical Chemistry (journal)Laser power scalingNanotechnologyChemistryMembraneOrganic chemistryOptics

Abstract

fetched live from OpenAlex

Co and Au nanoparticles have been synthesized by femtosecond laser ablation and fragmentation in various liquids ( n -hexane, diethyl ether, toluene, 2-propanol, acetone, and methanol) to investigate their influences on the size of the generated particles. Results suggest that nanoparticle growth with the absence of surfactants occurs from light absorption by the colloids through diffusion coalescence and can be controlled by the solvent polarity, the processing time, and the laser power. Furthermore, the growth has been related to the electrostatic repulsion energy and to the change in the nanoparticle temperature due to the laser light absorption by using the DLVO theory. Nanosecond laser annealing of Au particles in methanol also confirms the proposed model.

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 score0.053
Threshold uncertainty score0.465

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)0.0000.000

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 head0.011
GPT teacher head0.220
Teacher spread0.208 · 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