Unveiling Solvent Effects on Femtosecond Laser-Irradiated Au/Fe3O4 Colloidal Nanoparticles: The Acetone Effect
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".