Measurements of Pulsed 532 nm Laser Breakdown Spectroscopy of Synthesized Magnetite Nanoferrofluid
Bibliographic record
Abstract
We describe the results of 532 nm pulse laser-induced breakdown spectroscopy (LIBS) of two samples of magnetite nanoparticles (SPIONs) nanoferrofluid synthesized at room (S1) and elevated temperatures (S2) and at three different laser energy levels and pulse frequency. The size of magnetite nanoparticles, size distribution, magnetic crystalline phase and magnetization were analyzed and measured using transmission electron microscopy (TEM), X-ray diffraction spectroscopy (XRD) and vibrating sample magnetometry (VSM). The SPIONs showed a distribution between 4 - 22 nm with a peak about 12 nm and saturation magnetization of about 65 emu/g. The Saha-Boltzmann analysis of spectra for medium energy level (1050 mJ) yields plasma temperatures of (3881 ± 200) K and (26,047 ± 200) K for Fe I and OV as the lowest and highest temperatures respectively. A range of corresponding electron density (Ne-) of (0.47 - 6.80) × 1020, (0.58 - 8.30) × 1020 and (0.69 - 9.96) × 1020 cm-3 were determined at 860, 1050 and 1260 mJ respectively using the estimated CCD pictures. The results confirmed a higher elements ratio for S1 than S2 and the signal intensity indicated a non-linear behaviour as a function of pulse frequency with the maximum ratio value at 3 Hz. At higher frequency of 6 Hz no such turning point was observed. The highest and lowest temperatures corresponded to Fe I and OV respectively. The LIBS technique can be utilized to study, characterize and determine the elements ratio required in most applications involving the synthesizing process.
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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".