In situ study of nucleation and aggregation phases for nanoparticles grown by laser-driven methods
Bibliographic record
Abstract
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.
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".