Internal Magnetic Structure of Nanoparticles Dominates Time‐Dependent Relaxation Processes in a Magnetic Field
Bibliographic record
Abstract
Magnetic nanoparticles provide a unique combination of small size and responsiveness to magnetic fields making them attractive for applications in electronics, biology, and medicine. When exposed to alternating magnetic fields, magnetic nanoparticles can generate heat through loss power mechanisms that continue to challenge a complete physical description. The influence of internal nanoparticle (intracore) magnetic domain structure on relaxation remains unexplored. Within the context of potential biomedical applications, this study focuses on the dramatic differences observed among the specific loss power of three magnetic iron oxide nanoparticle constructs having comparable size and chemical composition. Analysis of polarization analyzed small angle neutron scattering data reveals unexpected and complex coupling among magnetic domains within the nanoparticle cores that influences their interactions with external magnetic fields. These results challenge the prevailing concepts in hyperthermia which limit consideration to size and shape of magnetic single domain nanoparticles.
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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".