Probing particle size dependency of frequency mixing magnetic detection with dynamic relaxation simulation

Engelmann, U.; Pourshahidi, Ali Mohammad; Shalaby, Ahmed; Krause, H.

doi:10.1016/j.jmmm.2022.169965

Cited by 5 publications

(5 citation statements)

References 54 publications

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“…Particle Properties: , ^_ , 5 wxx , , Figure 1 presents the core-size-dependent FMMD signal intensity as a function of the static magnetic offset field H0 for particles of different core diameters in the range of = 10, … , 30) nm. All four simulated frequency mixing components + v show a steadily increasing signal intensity with increasing core size, in accordance with previous works [30].…”

Section: Dependency On Intrinsicsupporting

confidence: 91%

“…, 30 ) nm. All four simulated frequency mixing components f 1 + n f 2 show a steadily increasing signal intensity with increasing core size, in accordance with previous works [30].…”

Section: Dependency On Intrinsic Particle Propertiessupporting

confidence: 91%

“…This causes both the increase in peak intensity as well as the gradual narrowing of the peak width; however, this effect is less pronounced than in the isolated core-size variation ( Figure 1 ), since the larger particles are not numerous (note that the ensemble is limited to 1000 particles per simulation run, Section 2.2 ). This knowledge is of practical relevance, since we were able to predict the core-size distribution of a real system of MNP from experimentally measured FMMD signals using the presented simulation framework recently [ 30 ]. However, the nature of magnetic anisotropy contributions remains unknown until now.…”

Section: Discussionmentioning

confidence: 99%

“…In the following, we use coupled Néel–Brownian stochastic magnetic relaxation dynamics simulations to close this gap, investigating the optimal signal generation in FMMD. The simulation framework has been successfully applied to MPI [ 27 ] and MFH [ 28 ], been extended to FMMD and compared to Langevin theory [ 29 ], as well as experimentally validated [ 30 ]. Such an in silico study allows to assess the individual and isolated influence of key contributions to signal generation over a wide range.…”

Section: Introductionmentioning

confidence: 99%

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Key Contributors to Signal Generation in Frequency Mixing Magnetic Detection (FMMD): An In Silico Study

Engelmann,

Simsek,

Shalaby

et al. 2024

Sensors

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Frequency mixing magnetic detection (FMMD) is a sensitive and selective technique to detect magnetic nanoparticles (MNPs) serving as probes for binding biological targets. Its principle relies on the nonlinear magnetic relaxation dynamics of a particle ensemble interacting with a dual frequency external magnetic field. In order to increase its sensitivity, lower its limit of detection and overall improve its applicability in biosensing, matching combinations of external field parameters and internal particle properties are being sought to advance FMMD. In this study, we systematically probe the aforementioned interaction with coupled Néel–Brownian dynamic relaxation simulations to examine how key MNP properties as well as applied field parameters affect the frequency mixing signal generation. It is found that the core size of MNPs dominates their nonlinear magnetic response, with the strongest contributions from the largest particles. The drive field amplitude dominates the shape of the field-dependent response, whereas effective anisotropy and hydrodynamic size of the particles only weakly influence the signal generation in FMMD. For tailoring the MNP properties and parameters of the setup towards optimal FMMD signal generation, our findings suggest choosing large particles of core sizes dC>25 nm with narrow size distributions (σ<0.1) to minimize the required drive field amplitude. This allows potential improvements of FMMD as a stand-alone application, as well as advances in magnetic particle imaging, hyperthermia and magnetic immunoassays.

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Section: Dependency On Intrinsicsupporting

confidence: 91%

“…, 30 ) nm. All four simulated frequency mixing components f 1 + n f 2 show a steadily increasing signal intensity with increasing core size, in accordance with previous works [30].…”

Section: Dependency On Intrinsic Particle Propertiessupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Key Contributors to Signal Generation in Frequency Mixing Magnetic Detection (FMMD): An In Silico Study

Engelmann,

Simsek,

Shalaby

et al. 2024

Sensors

Self Cite

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“…At present, the commonly used sensors for detecting yarn tension are based on phase detection systems [ 6 ], amplitude detection systems [ 7 ], the phase lock loop (PLL) detection method [ 8 ], direct digital synthesizer (DDS) scanning detection [ 9 ], and mixing detection method [ 10 ]. Among them, phase detection and amplitude detection systems are used in the largest amounts; however, both of them are low sensitivity [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Frequency Stability of SAW Yarn Tension Sensor by Using the Dual Differential Channel Surface Acoustic Wave Oscillator

Feng

Liu

Wang

2023

Sensors

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This paper presents a 60 MHz surface acoustic wave (SAW) yarn tension sensor incorporating a novel SAW oscillator with high-frequency stability. A SAW delay line was fabricated on ST-X quartz substrate using the unbalanced-split electrode and bi-directional engraving slots. The dual differential channel delay linear acoustic surface wave oscillator is designed and implemented to test yarn tension, which can effectively remove the interference of temperature, humidity, and other peripheral factors through differential design. The yarn tension sensor using the surface acoustic wave has high-precision characteristics, and the SAW delay line oscillator is designed to ensure the test system’s stable operation. The effect of time and tension on oscillator frequency stability is studied in detail, and the single oscillator and the dual differential channel system were tested, respectively. After using the dual differential channel system, the short-term frequency stability from is reduced from 1.0163 ppm to 0.17726 ppm, the frequency accuracy of the tension sensor is improved from 134 Hz to 27 Hz, and the max frequency jump steady is reduced from 2.2395 ppm to 0.45123 ppm.

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Ferrite Nanoparticles for Sensing Applications

Raturi¹,

Khan

Joshi

et al. 2023

Materials Horizons: From Nature to Nanomaterials

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Probing particle size dependency of frequency mixing magnetic detection with dynamic relaxation simulation

Cited by 5 publications

References 54 publications

Key Contributors to Signal Generation in Frequency Mixing Magnetic Detection (FMMD): An In Silico Study

Key Contributors to Signal Generation in Frequency Mixing Magnetic Detection (FMMD): An In Silico Study

Enhanced Frequency Stability of SAW Yarn Tension Sensor by Using the Dual Differential Channel Surface Acoustic Wave Oscillator

Ferrite Nanoparticles for Sensing Applications

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