Dynamics Study and Analysis of Laser-Induced Transport of Nanoferrofluid in Water Using Fluorescein Isothiocyanate (FITC) as Fluorescence Marker
Bibliographic record
Abstract
FITC-conjugated nanoferrofluid (FNFF) was synthesized and characterized to study the dynamic of laser-induced transport of NPs in water. The results confirmed a definite laser-induced enhanced velocity of NPs (100 μm⋅s−1) almost twice as much the without laser (i.e. Brownian motion). The diffusion coefficients of 17 × 10−6 m2⋅s−1 and 55 × 10−6 m2⋅s−1 were found for the cases without and with laser action respectively. The act of laser when switched on after NPs had reached the steady state was very prominent. The laser-induced heat and power generated by NPs were calculated 0.2μW⋅cm−3 and 0.4 pW⋅cm−2 respectively. Our experiment condition was non-adiabatic and that the heat generated was diffused into the surrounding. We considered the Maxwell’s criteria (Kp/Kw −1⋅K−1. Based on the Brownian diffusion and DLVO theory, at earlier times where the NPs are more dispersed within the medium are displaced faster. However, at later stages they become less mobile as they are agglomerated. The mechanisms for the enhanced mobility and laser transport of NPs are thought to be due to e.m.w induced force (i.e. an oscillatory motion) and laser absorptive force (i.e., photothermophoresis). A beam divergence of about 5.24° (or 91 mrad) was determined. A non-linear behaviour of laser beam was observed as a trajectory path within the water due to thermal heating hence causing the change of refractive index of medium and redistribution of NPs concentration.
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) | 0.000 | 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 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".