The Effect of Nanoparticle Aggregation on the Radiative Properties of Plasmonic Nanofluid during Light-induced Vaporization Process
Bibliographic record
Abstract
The light-induced vaporization process of plasmonic nanofluid plays an essential role in emerging efficient plasma-enhanced processes, ranging from solar energy harvesting to optofluidic control.In the current study, the effect of gold nanoparticle (AuNP) aggregation on the radiation properties during light-induced vaporization process has been investigated based on the finite element modeling (FEM).Specifically, the influence of the number and morphology of AuNP aggregates on the extinction cross section, albedo, and LSPR peak wavelength of particle-bubble complexes (P-B complexes) is studied.The results indicate that with the vaporization process of nanofluid, the radiative properties exhibit an obvious non-linear evolution law on the time scale and a periodic evolution pattern in the reciprocal cycle stages.The aggregation of AuNPs increases the peak extinction cross section.In the presence of nanobubble, whether the albedo of P-B complexes exceeds 0.5 depends on the relative magnitude of the extinction cross section of AuNP aggregates and nanobubble.After nanobubble dissipation, the aggregation of AuNPs increases the albedo of AuNP aggregates, although the albedo is still less than 0.5, showing a strong absorption of incident light.The aggregation of AuNPs causes a red-shift in the peak LSPR wavelength of AuNP aggregates, while the generation of nanobubble causes a blue-shift in the peak LSPR wavelength of AuNPs.In the presence of nanobubble, the change in the peak LSPR wavelength of P-B complexes depends on the competition between the redshift effect of AuNPs aggregation and the blue-shift effect of nanobubble, but in general the red-shifting effect of AuNP aggregation is stronger than the blue-shifting effect of nanobubble.The increase in the number of AuNPs aggregation layers causes the blue-shifted LSPR peak wavelength of AuNP aggregates, the decreased peak extinction cross section, the increased LSPR peak width, and the potential for multiple extinction peaks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".