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Record W2588988077 · doi:10.1038/srep41372

In situ study of nucleation and aggregation phases for nanoparticles grown by laser-driven methods

2017· article· en· W2588988077 on OpenAlex
M. Barberio, P. Antici

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.

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldEngineering
TopicLaser-Ablation Synthesis of Nanoparticles
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsNucleationNanoparticleNanomaterialsLaser ablationNanotechnologyMaterials scienceLaserNanostructureAbsorption (acoustics)In situChemistryOpticsPhysics

Abstract

fetched live from OpenAlex

In the last decades, nanomaterials and nanotechnologies have become fundamental and irreplaceable in many fields of science and technology. When used in applications, their properties depend on many factors such as size, shape, internal structure and composition. For this, exact knowledge of their structural features is essential when developing fabrication technologies and searching for new types of nanostructures or nanoparticles with specific properties. For the latter, the knowledge of the precise temporal evolution of the growth processes is fundamental when it comes to industrial production and applications. Here we present a method to control, with very high precision, the starting of the aggregation phase during the Laser Ablation in solution growth process. This is obtained by monitoring the optical absorption of the colloidal solution. We apply this control method on the most popular metallic nanoparticle materials (Ag, Al, Co, and Ti) and verify the technique using morphological analysis conducted by AFM and SEM microscopy. The experimental results are explained in terms of Mie extinction theory and Thermal Model for Laser Ablation.

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 score0.011
Threshold uncertainty score0.354

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 communication0.0000.001
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 head0.030
GPT teacher head0.318
Teacher spread0.288 · 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