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Record W7117730496 · doi:10.48550/arxiv.2512.23256

Unveiling Solvent Effects on Femtosecond Laser-Irradiated Au/Fe3O4 Colloidal Nanoparticles: The Acetone Effect

2025· preprint· W7117730496 on OpenAlexaff
Stéphane Mottin, Żaneta Świątkowska-Warkocka, Marta Wolny-Marszałek, Tatiana E Itina

Bibliographic record

VenuearXiv (Cornell University) · 2025
Typepreprint
Language
FieldEngineering
TopicLaser-Ablation Synthesis of Nanoparticles
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsNanoparticleAcetoneSolventFemtosecondColloidIrradiationSolvent effects

Abstract

fetched live from OpenAlex

The interplay between laser parameters and liquid environments dictates the outcome of femtosecond laser-induced nanoparticle modification. We present a study of gold and iron oxide nanoparticles in water and a water-acetone mixture, irradiated with femtosecond lasers at 808 nm and 404 nm. While aggregation was observed in pure water at both wavelengths, the results revealed a strong stability and a rather unexpected wavelength-dependency in the acetone-water mixture. In this case, 808 nm irradiation produced some decrease in nanoparticle sizes, while 404 nm led to some nanoparticle growth. As a result, the acetone effect is found to be twofold: (i) on one hand, it helps to prevent aggregation; (ii) on the other hand, it acts as a reactive medium allowing to tune the nanoparticle size and composition simply by changing laser wavelength. So, this work emphasizes that solvent physical properties as well as laser-induced chemical processes in the solvent are not merely secondary effects but can dominate the final morphological outcome, providing a predictive framework for nanoparticle synthesis.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.002

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 head0.029
GPT teacher head0.181
Teacher spread0.153 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
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

Citations0
Published2025
Admission routes1
Has abstractyes

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