TY - JOUR A1 - Engelmann, Ulrich M. A1 - Simsek, Beril A1 - Shalaby, Ahmed A1 - Krause, Hans-Joachim T1 - Key contributors to signal generation in frequency mixing magnetic detection (FMMD): an in silico study JF - Sensors N2 - 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 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. KW - key performance indicators KW - magnetic biosensing KW - coupled Néel–Brownian relaxation dynamics KW - frequency mixing magnetic detection KW - magnetic relaxation KW - micromagnetic simulation KW - magnetic nanoparticles Y1 - 2024 U6 - https://doi.org/10.3390/s24061945 SN - 1424-8220 N1 - This article belongs to the Special Issue "Advances in Magnetic Sensors and Their Applications" VL - 24 IS - 6 PB - MDPI CY - Basel ER - TY - JOUR A1 - Karschuck, Tobias A1 - Poghossian, Arshak A1 - Ser, Joey A1 - Tsokolakyan, Astghik A1 - Achtsnicht, Stefan A1 - Wagner, Patrick A1 - Schöning, Michael Josef T1 - Capacitive model of enzyme-modified field-effect biosensors: Impact of enzyme coverage JF - Sensors and Actuators B: Chemical N2 - Electrolyte-insulator-semiconductor capacitors (EISCAP) belong to field-effect sensors having an attractive transducer architecture for constructing various biochemical sensors. In this study, a capacitive model of enzyme-modified EISCAPs has been developed and the impact of the surface coverage of immobilized enzymes on its capacitance-voltage and constant-capacitance characteristics was studied theoretically and experimentally. The used multicell arrangement enables a multiplexed electrochemical characterization of up to sixteen EISCAPs. Different enzyme coverages have been achieved by means of parallel electrical connection of bare and enzyme-covered single EISCAPs in diverse combinations. As predicted by the model, with increasing the enzyme coverage, both the shift of capacitance-voltage curves and the amplitude of the constant-capacitance signal increase, resulting in an enhancement of analyte sensitivity of the EISCAP biosensor. In addition, the capability of the multicell arrangement with multi-enzyme covered EISCAPs for sequentially detecting multianalytes (penicillin and urea) utilizing the enzymes penicillinase and urease has been experimentally demonstrated and discussed. KW - Field-effect biosensor KW - Capacitive model KW - Enzyme coverage KW - Multianalyte detection KW - Penicillin Y1 - 2024 U6 - https://doi.org/10.1016/j.snb.2024.135530 SN - 0925-4005 (Print) SN - 1873-3077 (Online) N1 - Corresponding Author: Michael J. Schöning VL - 408 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Clausnitzer, Julian A1 - Kleefeld, Andreas T1 - A spectral Galerkin exponential Euler time-stepping scheme for parabolic SPDEs on two-dimensional domains with a C² boundary JF - Discrete and Continuous Dynamical Systems - Series B N2 - We consider the numerical approximation of second-order semi-linear parabolic stochastic partial differential equations interpreted in the mild sense which we solve on general two-dimensional domains with a C² boundary with homogeneous Dirichlet boundary conditions. The equations are driven by Gaussian additive noise, and several Lipschitz-like conditions are imposed on the nonlinear function. We discretize in space with a spectral Galerkin method and in time using an explicit Euler-like scheme. For irregular shapes, the necessary Dirichlet eigenvalues and eigenfunctions are obtained from a boundary integral equation method. This yields a nonlinear eigenvalue problem, which is discretized using a boundary element collocation method and is solved with the Beyn contour integral algorithm. We present an error analysis as well as numerical results on an exemplary asymmetric shape, and point out limitations of the approach. KW - Nonlinear eigenvalue problems KW - Boundary integral equations, KW - Exponential Euler scheme, KW - Parabolic SPDEs Y1 - 2024 U6 - https://doi.org/10.3934/dcdsb.2023148 SN - 1531-3492 SN - 1553-524X (eISSN) VL - 29 IS - 4 SP - 1624 EP - 1651 PB - AIMS CY - Springfield ER - TY - INPR A1 - Grieger, Niklas A1 - Mehrkanoon, Siamak A1 - Bialonski, Stephan T1 - Preprint: Data-efficient sleep staging with synthetic time series pretraining T2 - arXiv N2 - Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed "frequency pretraining" to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces. Y1 - 2024 ER - TY - JOUR A1 - Turdumamatov, Samat A1 - Belda, Aljoscha A1 - Heuermann, Holger T1 - Shaping a decoupled atmospheric pressure microwave plasma with antenna structures, Maxwell’s equations, and boundary conditions JF - IEEE Transactions on Plasma Science N2 - This article addresses the need for an innovative technique in plasma shaping, utilizing antenna structures, Maxwell’s laws, and boundary conditions within a shielded environment. The motivation lies in exploring a novel approach to efficiently generate high-energy density plasma with potential applications across various fields. Implemented in an E01 circular cavity resonator, the proposed method involves the use of an impedance and field matching device with a coaxial connector and a specially optimized monopole antenna. This setup feeds a low-loss cavity resonator, resulting in a high-energy density air plasma with a surface temperature exceeding 3500 o C, achieved with a minimal power input of 80 W. The argon plasma, resembling the shape of a simple monopole antenna with modeled complex dielectric values, offers a more energy-efficient alternative compared to traditional, power-intensive plasma shaping methods. Simulations using a commercial electromagnetic (EM) solver validate the design’s effectiveness, while experimental validation underscores the method’s feasibility and practical implementation. Analyzing various parameters in an argon atmosphere, including hot S -parameters and plasma beam images, the results demonstrate the successful application of this technique, suggesting its potential in coating, furnace technology, fusion, and spectroscopy applications. KW - 3-D printing KW - Furnace KW - Fusion KW - Hot S-parameter KW - Mode converter Y1 - 2024 U6 - https://doi.org/10.1109/TPS.2024.3383589 SN - 0093-3813 (Print) SN - 1939-9375 (Online) IS - Early Access SP - 1 EP - 9 PB - IEEE ER - TY - CHAP A1 - Kramer, Pia A1 - Bragard, Michael A1 - Ritz, Thomas A1 - Ferfer, Ute A1 - Schiffers, Tim T1 - Visualizing, Enhancing and Predicting Students’ Success through ECTS Monitoring T2 - 2024 IEEE Global Engineering Education Conference (EDUCON) N2 - This paper serves as an introduction to the ECTS monitoring system and its potential applications in higher education. It also emphasizes the potential for ECTS monitoring to become a proactive system, supporting students by predicting academic success and identifying groups of potential dropouts for tailored support services. The use of the nearest neighbor analysis is suggested for improving data analysis and prediction accuracy. KW - Monitoring KW - Engineering education KW - Data visualization KW - Accuracy KW - Data analysis Y1 - 2024 U6 - https://doi.org/10.1109/EDUCON60312.2024.10578652 SN - 2165-9559 SN - 2165-9567 (eISSN) N1 - 2024 IEEE Global Engineering Education Conference (EDUCON), 08-11 May 2024, Kos Island, Greece PB - IEEE CY - New York, NY ER -