Simultaneous coercivity and size determination of magnetic nanoparticles

Magnetic nanoparticles are increasingly employed in biomedical applications such as disease detection and tumor treatment. To ensure a safe and efficient operation of these applications, a noninvasive and accurate characterization of the particles is required. In this work, a magnetic characterization technique is presented in which the particles are excited by specific pulsed time-varying magnetic fields. This way, we can selectively excite nanoparticles of a given size so that the resulting measurement gives direct information on the size distribution without the need for any a priori assumptions or complex postprocessing procedures to decompose the measurement signal. This contrasts state-of-the-art magnetic characterization techniques. The possibility to selectively excite certain particle types opens up perspectives in “multicolor” particle imaging, where different particle types need to be imaged independently within one sample. Moreover, the presented methodology allows one to simultaneously determine the size-dependent coercivity of the particles. This is not only a valuable structure–property relation from a fundamental point of view, it is also practically relevant to optimize applications like magnetic particle hyperthermia. We numerically demonstrate that the novel characterization technique can accurately reconstruct several particle size distributions and is able to retrieve the coercivity–size relation of the particles. The developed technique advances current magnetic nanoparticle characterization possibilities and opens up exciting pathways for biomedical applications and particle imaging procedures

Fast performance assessment of mechatronic designs integrating CAD and dynamical models with application on servo actuated designs

In this paper an approach is presented that allows to investigate, in a systematic way, how design parameters of a servo actuated system modeled in CAD influence the dynamic behaviour and performance of the system. This is achieved by extracting physical quantities such as mass and inertia in part designs as these are difficult to model analytically when having irregular part shapes. These values are then employed in a symbolic dynamic model to assess the behavior and associated performance through numerical integration. To illustrate the effectiveness of the integrated approach, different slider-crank designs are evaluated for a given motion path. A sensitivity analysis is performed on the basis of gradients extracted from the introduced symbolic dynamical model through algorithmic differentiation. Our approach is flexible and enables precise motion study of actuated mechanisms with calculation times improved by an order of magnitude compared to other methods. In addition, this novel approach depends only on open source software

Advanced analysis of magnetic nanoflower measurements to leverage their use in biomedicine

Magnetic nanoparticles are an important asset in many biomedical applications ranging from the local heating of tumours to targeted drug delivery towards diseased sites. Recently, magnetic nanoflowers showed a remarkable heating performance in hyperthermia experiments thanks to their complex structure leading to a broad range of magnetic dynamics. To grasp their full potential and to better understand the origin of this unexpected heating performance, we propose the use of Kaczmarz' algorithm in interpreting magnetic characterisation measurements. It has the advantage that no a priori assumptions need to be made on the particle size distribution, contrasting current magnetic interpretation methods that often assume a lognormal size distribution. Both approaches are compared on DC magnetometry, magnetorelaxometry and AC susceptibility characterisation measurements of the nanoflowers. We report that the lognormal distribution parameters vary significantly between data sets, whereas Kaczmarz' approach achieves a consistent and accurate characterisation for all measurement sets. Additionally, we introduce a methodology to use Kaczmarz' approach on distinct measurement data sets simultaneously. It has the advantage that the strengths of the individual characterisation techniques are combined and their weaknesses reduced, further improving characterisation accuracy. Our findings are important for biomedical applications as Kaczmarz' algorithm allows to pinpoint multiple, smaller peaks in the nanostructure's size distribution compared to the monomodal lognormal distribution. The smaller peaks permit to fine-tune biomedical applications with respect to these peaks to e.g. boost heating or to reduce blurring effects in images. Furthermore, the Kaczmarz algorithm allows for a standardised data analysis for a broad range of magnetic nanoparticle samples. Thus, our approach can improve the safety and efficiency of biomedical applications of magnetic nanoparticles, paving the way towards their clinical use

Model-based optimized steering and focusing of local magnetic particle concentrations for targeted drug delivery

Magnetic drug targeting (MDT) is an application in the field of targeted drug delivery in which magnetic (nano)particles act as drug carriers. The particles can be steered toward specific regions in the human body by adapting the currents of external (electro)magnets. Accurate models of particle movement and control algorithms for the electromagnet currents are two of the many requirements to ensure effective drug targeting. In this work, a control approach for the currents is presented, based on an underlying physical model that describes the dynamics of particles in a liquid in terms of their concentration in each point in space. Using this model, the control algorithm determines the currents generating the magnetic fields that maximize the particle concentration in spots of interest over a period of time. Such an approach is computationally only feasible thanks to our innovative combination of model order reduction with the method of direct multiple shooting. Simulation results of an in-vitro targeting setup demonstrated that a particle collection can be successfully guided toward the targeted spot with limited dispersion through a surrounding liquid. As now present and future particle behavior can be taken into account, and non-stationary surrounding liquids can be dealt with, a more precise and flexible targeting is achieved compared to existing MDT methods. This proves that the presented methodology can bring MDT closer to its clinical application. Moreover, the developed model is compatible with state-of-the-art imaging methods, paving the way for theranostic platforms that combine both therapy as well as diagnostics

Model-based feedforward targeting of magnetic microparticles in fluids using dynamic optimization

External magnetic field gradients originating from electromagnets can generate forces on ferromagnetic microparticles to aid and enable precise local targeting of these particles. To steer these magnetic particles from their initial position to a desired target zone in a fluid, a control strategy on the proper activation of the electromagnets is required. We propose a model-based control strategy that performs dynamical optimization with respect to a given metric that results in an optimal particle trajectory. Here, minimum power consumption of the electromagnet is considered as metric. Furthermore, a dynamical model containing the magnetic fluidic forces acting on the particles is incorporated in the dynamic optimization. Results show the benefits of following the presented approach since it allows control of the electromagnets in open loop

Quantitative reconstruction of a magnetic nanoparticle distribution using a non-negativity constraint

Magnetorelaxometry (MRX) is a non-invasive method for the specific quantification of magnetic nanoparticles (MNP). Here, we investigate experimentally the reconstruction of the MNP concentration in an extended volume. A phantom with varying but known MNP distribution was subsequently magnetized by 48 planar coils at different locations. The MRX signal was measured using the PTB 304 SQUID-magnetometer system. The inverse problem was solved by means of a non-negative least squares (NNLS) algorithm and compared to a minimum norm estimation (TSVD-MNE). The reconstruction by NNLS shows a deviation of the total MNP amount of less than 3 % (10% by TSVD-MNE). Hence, adapted non-invasive MRX methods can reliable reconstruct the MNP content in extended volumes

Magnetic measurement methods to probe nanoparticle–matrix interactions

Magnetic nanoparticles (MNPs) are key elements in several biomedical applications, e.g., in cancer therapy. Here, the MNPs are remotely manipulated by magnetic fields from outside the body to deliver drugs or generate heat in tumor tissue. The efficiency and success of these approaches strongly depend on the spatial distribution and quantity of MNPs inside a body and interactions of the particles with the biological matrix. These include dynamic processes of the MNPs in the organism such as binding kinetics, cellular uptake, passage through cell barriers, heat induction and flow. While magnetic measurement methods have been applied so far to resolve the location and quantity of MNPs for therapy monitoring, these methods can be advanced to additionally access these particle–matrix interactions. By this, the MNPs can further be utilized as probes for the physical properties of their molecular environment. In this review, we first investigate the impact of nanoparticle–matrix interactions on magnetic measurements in selected experiments. With these results, we then advanced the imaging modalities magnetorelaxometry imaging and magnetic microsphere tracking to spatially resolve particle–matrix interactions

Magnetic measurement methods to probe nanoparticle–matrix interactions

Magnetic nanoparticles (MNPs) are key elements in several biomedical applications, e.g., in cancer therapy. Here, the MNPs are remotely manipulated by magnetic fields from outside the body to deliver drugs or generate heat in tumor tissue. The efficiency and success of these approaches strongly depend on the spatial distribution and quantity of MNPs inside a body and interactions of the particles with the biological matrix. These include dynamic processes of the MNPs in the organism such as binding kinetics, cellular uptake, passage through cell barriers, heat induction and flow. While magnetic measurement methods have been applied so far to resolve the location and quantity of MNPs for therapy monitoring, these methods can be advanced to additionally access these particle–matrix interactions. By this, the MNPs can further be utilized as probes for the physical properties of their molecular environment. In this review, we first investigate the impact of nanoparticle–matrix interactions on magnetic measurements in selected experiments. With these results, we then advanced the imaging modalities magnetorelaxometry imaging and magnetic microsphere tracking to spatially resolve particle–matrix interactions

Magnetic nanoparticles in theranostic applications

Nanomedicine research started exploring the combination of therapy and diagnostics, so-called theranostics, to offer a more flexible care with improved outcome. As magnetic nanoparticles show great potential in many diagnostic and therapeutic applications, they are prime candidates to be used in a theranostic setting. This perspective gives an overview of state-of-the-art magnetic nanoparticle-based imaging techniques and theranostic applications and discusses their opportunities and challenges. To address these challenges and exploit these opportunities to the fullest, we provide three promising research directions. The first considers novel magnetic field sequences, utilizing the rich magnetic dynamics of the particles, which boost the capabilities of many nanoparticle-based applications. Secondly ,we introduce the concept of smart theranostics based on feedback mechanisms between the particle applications and their supporting imaging procedure to enhance the performance of both and allow real-time monitoring of treatment efficiency. Finally, we show how data-driven models could enhance therapy and diagnostics, and handle the platform's large amount of data and decision support algorithms. The latter research track also includes hybrid models in which physics-based and data-driven models are combined to overcome challenges of applications with limited data, as well as to uncover unknown nanoparticle dynamics. Contrasting other literature, which mainly focuses on developing magnetic nanoparticles with the right characteristics, we put forward advances in magnetic nanoparticle imaging techniques and applications to enable the use of a broader range of magnetic nanoparticles in theranostics. We encourage researchers to also investigate these aspects to advance theranostic applications of magnetic nanoparticles to clinical environments.Comment: 16 pages, 7 figure