ROLE OF NÉEL AND BROWNIAN RELAXATION MECHANISMS FOR WATER-BASED <font>Fe<sub>3</sub>O<sub>4</sub></font> NANOPARTICLE FERROFLUIDS IN HYPERTHERMIA
Bibliographic record
Abstract
Hydrophilic Fe3O4 nanoparticles with a mean diameter size of 15 nm were synthesized by means of the direct reduction of FeCl3 and FeCl2 in an aqueous solution. The heating properties of Fe3O4 nanoparticles under an alternating magnetic field were investigated in a frequency range of 50–500 kHz and a magnitude range of 3.1–5.0 kA/m. To distinguish the roles of Néel and Brownian relaxation mechanisms, polydimethylsiloxane (PDMS) and water were employed as a medium, in which PDMS plays a significant role of eliminating the effect of Brownian relaxation. It is experimentally and theoretically confirmed that, for the Fe3O4 /water system, high-specific absorption rates are due to a combination of both mechanisms, with an enhanced contribution due to Néel relaxation with increasing frequency and magnitude of the alternating electromagnetic field. The contribution efficiency of Néel relaxation increases from 36–56% by increasing the electromagnetic field frequency from 50–300 kHz, and then retains a saturated value of ~ 56% at 300–500 kHz. Moreover, a linear increase of the contribution efficiency of Néel relaxation at 300 kHz was observed from 55–65% by increasing the magnitude of electromagnetic field at 3.1–5.0 kA/m. The current research can be widely-expanded to explain the electromagnetic heating effect of other ferrofluid systems.
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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) | 0.000 | 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".