Photonic Control of Nanoparticle Morphology via Continuous-Wave and Femtosecond Laser Irradiation: Mechanisms, Tunability, and Emerging Applications
Bibliographic record
Abstract
Green synthesis combined with laser-assisted modification offers a sustainable route for tailoring the properties of silver nanoparticles (AgNPs).In this study, lemon juice extract was employed as a natural reducing and stabilizing agent to produce AgNPs, which were subsequently exposed to continuous-wave (CW) and femtosecond (fs) laser irradiation.UV-Visible Spectroscopy (UV-Vis) analysis showed a blue shift of the localized surface plasmon resonance (LSPR) peak from 435 nm (control) to 426 nm (CW) and 415 nm (fs), with spectral narrowing that reflected improved size uniformity.Transmission Electron Microscopy (TEM) measurements confirmed a reduction in mean particle size from 31.5 ± 7.8 nm (control) to 22.5 ± 4.5 nm (CW) and 16.8 ± 3.2 nm (fs), with fs irradiation also producing anisotropic features.X-Ray Diffraction (XRD) revealed retention of the face-centered cubic (FCC) structure, while crystallite size decreased from 27.8 nm (control) to 21.2 nm (CW) and 17.5 nm (fs).Fourier Transform Infrared Spectroscopy (FTIR) spectra indicated partial removal of phytochemical capping after fs exposure, and Dynamic Light Scattering (DLS) results demonstrated reduced hydrodynamic size and polydispersity index (0.276 → 0.126).Collectively, the findings highlight fs laser treatment as a powerful tool for achieving smaller, monodisperse, and structurally refined AgNPs, broadening their applicability in biomedical, catalytic, and environmental domains.
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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".