Coercivity Determines Magnetic Particle Heating
Bibliographic record
Abstract
Diseased cell treatment by heating with magnetic nanoparticles is hindered by their required high concentrations. A clear relationship between heating efficiency and magnetic properties of nanoparticles has not been attained experimentally yet due to limited availability of magnetic nanoparticles with varying size and composition. Here, versatile flame aerosol technology is used for the synthesis of 21 types of ferro-/ferrimagnetic nanocrystals with varying composition, size, and morphology for hyperthermia and thermoablation therapy. Heating efficiency, magnetic hysteresis, and first-order reversal curves of these materials are compared. The maximum heating performance occurs near the transition from superparamagnetic to single domain state, regardless of particle composition. Most importantly, the ratio between saturation magnetization and coercivity can be linked to the heating properties of magnetic nanoparticles. Magnetic interaction is controlled by changes in the architecture of the nanoparticles and closely analyzed by first-order reversal curves. Silica-coated nonstoichiometric Gd-Zn ferrite exhibits the most promising therapeutic capability at relatively low particle concentrations, as shown in vitro with cancerous prostate cells.
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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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".