Magnetic nanocarriers: from material design to magnetic manipulation
Bibliographic record
Abstract
Magnetic nanocarriers play an increasing role in various biomedical applications such as the separation of magnetically 'tagged' DNA, drug delivery or identification of biological species. Recent developments in nanotechnology allow the fabrication of both artificial nanocarriers and magnetic separation devices which may achieve great performances at an incredibly small-volume sample handling. However, as the sizes of magnetic carriers and separation devices diminish, important theoretical and experimental challenges may occur mainly due to the important variations of the surface-to-volume ratio. Under these circumstances, intrinsic properties of individual carriers and their influence on functionality and performances of the magnetic manipulation have to be investigated with a higher degree of accuracy. In this paper, we present the state-of-the-art techniques for extracting accurate information about individual magnetic entities from magnetic measurements performed on ordered arrays or clusters of magnetic nano-objects of various dimensionalities and geometries. As the mutual magnetic interactions may be responsible for collective effects, both analytical and numerical techniques for evaluating the mutual interactions between magnetic nanoparticles and nanowires are reviewed, special emphasis being put on the dimensionality of their assemblages. As the efficiency of the manipulation and capture of individual magnetic carriers in magnetic confinement devices depend strongly on their size, the theoretical description of the motion of magnetic particles under various conditions of flow and field configurations become very important especially with the transition to the nanoscale regime. The equations of motion of magnetic nanocarriers in continuous-flow microfluidic devices are reviewed and some solutions for superparamagnetic interacting clusters, non-interacting beads and ferromagnetic contiguous or codebar nanowires are presented. The influence of both carrier size and transition toward the nanoscale regime on the trappability of some magnetic nanocarriers is evaluated in terms of finite-length Navier boundary conditions and applied in order to compare the motion of superparamagnetic beads and ferromagnetic nanowires in conventional continuous-flow magnetic confinement devices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".