ArticleNanoscale2025
Dual field magnetic separation for improved size fractionation of magnetic nanoparticles.
Article in Nanoscale, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Magnetic nanoparticles (MNPs) are emerging as key tools in biomedical and technical applications due to their tunable magnetic properties and responsiveness to external magnetic fields. However, the effectiveness of MNPs in applications such as targeted drug delivery, magnetic imaging and magnetic hyperthermia critically depends on achieving a narrow particle size distribution. Conventional gradient magnetic separation techniques often fall short in delivering high resolution size separation, particularly in the challenging 20 to 200 nm range, where the interplay between Brownian motion and magnetophoretic forces reduces separation precision. Therefore, in this study, we propose an enhanced gradient magnetic separation (GMS) method that superimposes a homogeneous alternating magnetic field onto an inhomogeneous gradient field and makes use of size-dependent magnetization dynamics. The proposed dual-field method is first verified in a simple test case, confirming that the desired separation behavior can principally be achieved. Simulations show that the magnetization ratio between particles of different sizes can be significantly increased beyond the predictions of the Langevin function. By systematically varying offset and alternating field strengths, an optimal combination maximizing this ratio is identified. Additionally, the influence of the alternating field frequency is investigated, showing that separation efficiency improves with increasing frequency up to a saturation point. To translate this behavior into effective spatial separation, particle trajectories are simulated while dynamically optimizing the alternating field strength over time to maximize the travelled distance ratio between large and small particles. The results demonstrate that large particles maintain strong alignment with the field, while smaller particles experience reduced time averaged magnetization, resulting in notably reduced mobility. Additionally, travelled distance ratios between particle sizes increase significantly compared to using a gradient field alone. The introduced dual-field method is also shown to remain effective for various particle sizes and under more realistic conditions where hydrodynamic and magnetic radii differ due to surface coatings. Finally, it is shown that the separation cut-off radius can be chosen arbitrarily, confirming the size independence of the method. These findings demonstrate that the proposed method substantially enhances size based separation, enabling improved control over particle size distributions and potentially advancing biomedical applications.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.