Modeling magnetic nanoparticles: application to hyperthermia
Bibliographic record
Abstract
Using the Landau-Lifshitz-Gilbert (LLG) equation in micromagnetic simulations, we model magnetic nanoparticles composed of nanorods for application in magnetic nanoparticle hyperthermia, a developing cancer treatment. We use a scaling approach based on the renormalization group (RG) to calculate magnetization-field hysteresis loops that are invariant with simulation cell size, with the objective of decreasing the simulation time at clinically relevant field parameters. In addition, we introduce a time scaling approach that involves the sweep rate of the oscillating external field and the damping constant α in the LLG equation, which allows for up to three orders of magnitude faster simulations. Equipped with the RG and time scaling tools, we explore a macrospin model in which a complex nanoparticle is represented by a single magnetization vector with appropriate effective magnetic parameters. To evaluate this model, we calculate loops for single particles and particles interacting in pairs, chains and triangles of three particles, and in a cluster of thirteen nanoparticles. Motivated by recent experimental studies that reported successful hyperthermia treatment in the absence of perceptible heating of tissue, we report on local hysteresis loops of individual nanoparticles within clusters, highlighting the role of magnetostatic interactions between nanoparticles in the complex heating and magnetization dynamics of groups of nanoparticles.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".