Bias Field Research Articles

Frequency Tunable Film Bulk Acoustic Resonator Integrating PMN‐PT/FSMA Multiferroic Heterostructure for Flexible MEMS

AbstractFrequency tunable flexible piezo resonators exhibit significant potential for technological advances in wearable magnetic field sensing, futuristic wireless telecommunication devices, and flexible micro‐electromechanical systems. This study presents a multifunctional and flexible 0.67Pb(Mg1/3Nb2/3)O3−0.33PbTiO3(PMN‐PT)/Ni50Mn35In15 (Ni‐Mn‐In) multiferroic heterostructure‐based bulk acoustic wave (BAW) resonator fabricated over Kapton substrate. The fundamental resonance frequency (fR = 5.31 GHz) of the resonator is tunable with magnetic and electric fields. A significant change in resonance frequency (ΔfR) of 405 MHz has been achieved with 3.37 Hz nT−1 sensitivity using a direct current (DC) magnetic field of 1200 Oe, attributed to the delta‐E effect. The resonator displays a significant magnetic field tunability of 8.83%. Additionally, a substantial ΔfR of 360 MHz, 36 Hz µV−1 sensitivity and 6.78% tunability is attained with 10 V of DC bias voltage. The impact of magnetic field and DC bias voltage on the acoustic characteristics have been studied by fitting the resonance frequency curves with an equivalent modified Butterworth‐Van Dyke model. The fabricated BAW resonator exhibits outstanding flexibility with no discernible change in fR up to 2000 bending cycles. Such PMN‐PT/Ni‐Mn‐In‐based multiferroic BAW resonators displaying magnetic and electric field tunability are propitious for next‐generation flexible electronics and magnetic field sensors.

Investigate ensemble machine learning models to reduce the daily Mean field bias of radar rainfall estimates derived from ZR relationships in the sub-river basins in the middle of Thailand

Investigate ensemble machine learning models to reduce the daily Mean field bias of radar rainfall estimates derived from ZR relationships in the sub-river basins in the middle of Thailand

Observation of field-free spin–orbit torque switching in a single crystalline (Ga,Mn)(As,P) ferromagnetic film with perpendicular anisotropy

We report the observation of field-free spin–orbit torque (SOT) magnetization switching in a single layer of (Ga,Mn)(As,P) ferromagnetic film exhibiting perpendicular magnetic anisotropy. The SOT switching phenomenon is characterized by distinct transitions between two Hall resistance (HR) states during current scans. When subjected to an in-plane bias field, the observed switching chirality in the HR hysteresis loop consistently aligns with SOT induced by spin polarization arising from Rashba- and Dresselhaus-type spin–orbit fields within the tensile-strained crystalline structure of the (Ga,Mn)(As,P) film. Remarkably, in the present experiments, we observe SOT switching even in the absence of an external bias field, and with its chirality depending on the direction of initial magnetization. We attribute this field-free switching to symmetry breaking facilitated by an internal coupling field, the orientation of which is determined by the external field experienced as the magnetization is initialized. Further evidence supporting the presence of such a coupling field includes a shift in the field-scan HR hysteresis depending on the direction of initialized magnetization. Structural analysis reveals a surface layer enriched in Mn and O, indicating the presence of oxide-based magnetic structures that are magnetically coupled to the (Ga,Mn)(As,P) film. The temperature dependence of field-free SOT switching corroborates this explanation, as the internal coupling field disappears above 40 K, consistent with the expected magnetic transition of the Mn3O4 structure. Our discovery of field-free SOT magnetization switching in a single-layer film represents a significant advancement, offering a novel pathway for the development of simpler and more energy-efficient spintronic devices.

Automated Visceral Adipose Tissue Segmentation and Quantification from Abdominal MRI using an Enhanced U-Net and Region-growing

The study focuses on enhancing the accuracy and reliability of visceral adipose tissue (VAT) segmentation and quantification from abdominal MRI images. Accurate segmentation of VAT is crucial for assessing obesity-related health risks, as traditional methods struggle with irregular shapes and varying intensities. The research utilizes a methodology consisting of three key modules: homomorphic filtering for intensity inhomogeneity correction, a U-Net architecture with attention mechanisms for primary segmentation, and a region-growing algorithm for refining segmentation. Homomorphic filtering effectively separates bias fields, enhancing image quality by transforming multiplicative artifacts into additive ones and removing them with high-pass filtering. This process ensures precise segmentation by maintaining high-frequency anatomical details. The U-Net model incorporates attention mechanisms and skip connections to focus on VAT regions, utilizing both local and global image contexts.The Combined Healthy Abdominal Organ Segmentation (CHAOS) challenge dataset and the Cancer Imaging Archive (TCIA) dataset are used to train and evaluate the model. It achieves a Dice Similarity Coefficient (DSC) of up to 0.985 on the CHAOS dataset and 0.972 on the TCIA dataset, outperforming existing methods in terms of segmentation accuracy. The region-growing algorithm further refines the segmentation by expanding VAT regions from high-confidence seed points, ensuring accurate boundary delineation and reducing noise. The study's results, evaluated using k-fold cross-validation, show that the proposed methodology significantly improves VAT segmentation efficiency, achieving a median DSC of 0.96 for the CHAOS dataset and 0.95 for the TCIA dataset in the most comprehensive experimental scenario. Comparative analysis indicates that the proposed approach outperforms other models, with higher sensitivity and specificity values, highlighting its potential for clinical applications in obesity management..

Bias evaluation and minimization for estuarine total dissolved solids (TDS) patterns constructed using spatial interpolation techniques

This study evaluated and minimized bias in estuarine total dissolved solids (TDS) patterns predicted using geostatistical approaches. The acquired TDS data was divided into three parts: 29 points (60 %) for predicting TDS patterns with spatial interpolation techniques, 12 points (25 %) for validation and bias correction, and 7 points (37 %) for testing the corrected bias. Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Universal Kriging (UK) and Regression Kriging (RK) were applied to map TDS patterns. R-square and Relative Error of Mean showed significant discrepancies between observed and predicted TDS levels. The Mean field bias (MFB) correction technique was applied to minimize bias in TDS patterns. After correcting bias, the TDS values predicted by IDW, OK, UK and RK at random locations deviated from measured values by −2.85 %, 0.71 %, 4.66 %, and − 6.03 % respectively. At the test locations, these values deviated by −1.45 %, 1.41 %, 2.11 %, and − 2.65 % respectively.

Prostate Cancer Classification and Interpretation With Multiparametric Magnetic Resonance Imaging and Gleason Grade Score Using DarkNet53 Model.

Prostate Cancer (PCa) increases the mortality rate of males worldwide and is caused by genetics, lifestyle, and age reasons. The existing automated PCa classification systems face difficulties with overfitting issues, and non-generalizability, leading to poor classification performance. On this account, this study proposes an automated classification of PCa from MRI images using a hybrid weighted mean of vectors-optimized DarkNet53 classifier model. The proposed method suggests nonlocal mean filtering for noise reduction, N4ITK bias field correction to enhance image quality, and active contour-based segmentation for accurately identifying the disease region. The feature extraction utilizes the gray level run length matrix and shape features for effective feature extraction. A weighted mean of vectors optimization is used to optimize the feature selection process by hybridizing it with the DarkNet53 model for classification. Finally, the interpretation of achieving the classification has been demonstrated using the explainable AI Grad-CAM model. After comparing the proposed work with various state-of-the-art algorithms, the proposed model achieves 99.31% accuracy, 98.24% sensitivity, and 98.46% specificity, respectively, highlighting the model's accomplishment using the DarkNet53 classifier.

Effect of built‐in bias field on hysteresis loss of FeSiAl/SrFe12O19 soft magnetic composite

AbstractAfter magnetization, the magnetic loss of FeSiAl/SrFe12O19 (SrM) soft magnetic composite is greatly reduced compared with that before magnetization, which has similar effect with FeSiAl magnetic powder core with applied external transverse magnetic field. The experimental results show that the internal bias field provided by SrM ferrite after magnetization increases the average domain size, reduces the number of domain walls and the displacement of domain walls, thereby effectively reducing the hysteresis loss. When the content of SrM ferrite is 8.1 wt.‐%, the magnetic loss is reduced by about 50 %, and the overall performance is the best.

Evolution of structural, magnetic and transport properties of 3d-5d based double perovskites Nd2–xSrxCoIrO6

The evolution of the structural, magnetic and transport properties of the intermediate compounds Nd2-xSrxCoIrO6withx= 0.2, 0.4, 0.6, 1 and 1.5 have been studied to establish important roles of sizes and oxidation states of cations on various phases. The replacement of Nd3+by Sr2+primarily influences the oxidation states of Co (Co2+→Co3+) and Ir (Ir4+→Ir5+) ions to maintain the charge neutrality in the entire system. The Sr dopants give rise to an increasing Co/Ir antisite disorder (ASD) to accommodate the variation of charge state and ionic radius of Co and Ir. The nature of magnetic interaction induced by Sr changes from being a ferrimagnetic (FIM) to a more dominant antiferromagnetic. The suppression of the second magnetic transition below 30 K in samples forx>0.2 is entirely due to dilution of the Nd-Nd magnetic interaction. The combined effects of ASD and mixed oxidation state of Co and Ir ions generate various types of magnetic exchange pathways and create competitive magnetic interactions to stabilize a particular magnetic ground state. In the middle compound NdSrCoIrO6, a Griffith like phase in the temperature region 65-150 K and exchange bias field of 658 Oe at 2.3 K under a cooling field of 50 kOe has been observed. The compounds show an insulating kind of behaviour, and with hole doping the value of room temperature resistivity drastically decreases. The nature of conduction is found to follow three dimensional Mott's variable range hopping process.

CTNI-67. AI BASED PREDICTION OF CLINICAL TTFIELDS RESPONSE IN GLIOBLASTOMA PATIENTS (RETROSPECTIVE ANALYSIS)

Abstract RESEARCH QUESTION Accurate and individualized prediction of response to therapies is central to precision medicine and a major goal in neuro-oncology. An extension of overall survival (OS) and progression-free survival (PFS) was observed in patients treated with tumor treating fields (TTFields) plus maintenance temozolomide compared to those treated with maintenance temozolomide alone and TTFields have become an integral part of the treatment of glioblastoma. To identify patients who benefit most from TTFields, we aim to implement a state-of-the-art machine learning approach to create a model capable of identifying responding patients early in treatment. METHODOLOGY In this retrospective analysis, patients with IDH-wildtype glioblastoma treated with TTFields, in addition to RT and temozolomide, as first-line therapy are included. Clinical data and MRI raw data (in DICOM format) are collected. The preprocessing of the MRI data includes reorientation, bias field correction, registration, segmentation, and intensity normalization. This is followed by the extraction of a Radiomics signature. The latter, along with the clinical data, is trained using various machine learning models. The model validation occurs on an independent test cohort. Data collection from various neuro-oncological centers across Europe is ongoing. RESULTS So far, data from 134 TTFields patients and 131 control patients have been collected. Initial results, providing detailed insights into the model architecture and performance, will be presented at the time of the SNO meeting. CONCLUSION This study has the potential to optimize the treatment of glioblastoma patients by using machine learning to allow the early identification of patients that respond to first line therapy.

In situ observation of the structural transformations behind the emergence and disappearance of exchange bias in polycrystalline Ni-Mn/Fe20Ni80 films

The antiferromagnetic L10θ-NiMn compound has a Néel temperature of up to 800 °C and is capable of providing the exchange bias effect in multilayered structures with adjacent ferromagnetic layers, such as Fe20Ni80, up to 350 °C. The value of the exchange bias field can be non-decreasing up to 150 °C, making Ni-Mn a compelling choice for applied devices based on the exchange bias effect. In this work we study the structural transformations occurring in magnetron-sputtered Ni-Mn/Fe20Ni80 films that first lead to the emergence of exchange bias in these structures, and then to its disappearance. Employing in situ X-ray diffractometry and vibrating-sample magnetometry we observe how under annealing the phases of Ni-Mn follow Ostwald’s rule of stages on their way to form the equilibrium θ-NiMn. Our findings suggest that at temperatures of around 350 °C diffusion of iron from the ferromagnetic layer causes decomposition of θ-NiMn, ultimately resulting in the irreversible disappearance of exchange bias. Our results reveal the role of adjacent Fe20Ni80 layers in the transformations, and how crystal texture in the Ni-Mn layer affects exchange bias. The results of our work not only clarify the fundamental mechanisms but also provide valuable insights for engineering and utilizing of applied devices with Ni-Mn, making it of interest to material scientists and engineers of different specializations.

Thermal nonreciprocity tuned by wrinkle patterns in graphene

Abstract Thermal nonreciprocity are typically realized by using nonlinear heat conductivity, external field bias or spatiotemporal modulation of thermal parameters, but these methods require specific material properties or accurate modulation means. Low-dimensional materials like graphene provides a platform to engineer heat transport characteristics at the molecular level. Here, by simply tuning graphene’s wrinkle morphol ogy, the approach to generate nonreciprocal heat transfer is reported, which applies to both static and dynamic heat signals. Such nonreciprocity is attributed to the tunable responses of thermal conductivity on both temperatures and wrinkles. Phonon density of states (PDOS) of the wrinkled graphene exhibit distinct spectra, especially at the low frequency (around 17 THz), which results in reduced temperature sensitivity of thermal conductivities and eventually induces thermal nonreciprocity. The findings provide insights into the physics of thermal transport in wrinkled graphene, and paves a new avenue for mechan ically modulating thermal nonreciprocity. Our method to obtain thermal nonreciprocity is reversible, tunable and controllable, meanwhile remains structural integrity, which benefits functional applications such as heat management in electronics.

Parallel-way: Multi-modality-based brain tumor segmentation using parallel capsule network

ABSTRACT Brain tumors present a formidable diagnostic challenge due to their aberrant cell growth. Accurate determination of tumor location and size is paramount for effective diagnosis. Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are pivotal tools in clinical diagnosis, yet tumor segmentation within their images remains challenging, particularly at boundary pixels, owing to limited sensitivity. Recent endeavors have introduced fusion-based strategies to refine segmentation accuracy, yet these methods often prove inadequate. In response, we introduce the Parallel-Way framework to surmount these obstacles. Our approach integrates MRI and PET data for a holistic analysis. Initially, we enhance image quality by employing noise reduction, bias field correction, and adaptive thresholding, leveraging Improved Kalman Filter (IKF), Expectation Maximization (EM), and Improved Vibe Algorithm (IVib), respectively. Subsequently, we conduct multi-modality image fusion through the Dual-Tree Complex Wavelet Transform (DTWCT) to amalgamate data from both modalities. Following fusion, we extract pertinent features using the Advanced Capsule Network (ACN) and reduce feature dimensionality via Multi-objective Diverse Evolution-based selection. Tumor segmentation is then executed utilizing the Twin Vision Transformer with dual attention mechanism. Implemented our Parallel-Way framework which exhibits heightened model performance. Evaluation across multiple metrics, including accuracy, sensitivity, specificity, F1-Score, and AUC, underscores its superiority over existing methodologies.

A hybrid variational level set model based on double Gaussian distribution fitting energy for image segmentation and bias correction

AbstractThe variational level set model has been widely used in image segmentation. However, its performance is significantly hindered by bias fields and noise within the images. To address these limitations, we introduce a novel hybrid variational level set model based on dual Gaussian distribution fitting (DGDF) energy in this paper. The DGDF energy integrates both local and global Gaussian distributions. The local energy is derived from the original image, while the global energy employs the corrected image, enabling effective segmentation of images with intensity inhomogeneity. Furthermore, the model demonstrates low sensitivity to weighting parameters and robust performance for noisy images. We develop an alternating iteration algorithm that combines variational methods with gradient descent to efficiently solve the proposed model. Experimental results validate the effectiveness of the model and the algorithm. In addition, the proposed model shows competitiveness on test images and three datasets compared to several state‐of‐the‐art models, including other variational level set models and deep learning‐based techniques.

Enhanced detection performance based on a differential ME sensor with strong suppression of vibration interference

Magnetoelectric (ME) sensors have enormous potential for detecting weak magnetic fields because of their high sensitivity, low power consumption, compact size and, low cost. However, inevitable vibration interference limits their application in practical environments, especially in the case of mobile platform mounting. Here, we propose a differential ME sensor, consisting of PZT macro-fiber composites (MFCs) and Metglas laminates. The differential ME sensor has two output terminals with weak mutual mechanical coupling and works in longitudinal vibration mode. MFC cores are polarized in parallel mode to guarantee their consistency of electric characteristics and reversed bias field is provided by attached magnets. Experimental results show that the differential-mode response amplitudes have a gain of −17.6 dB for low-frequency vibration at 2 Hz and ∼6.2 dB for an applied magnetic field at 3 Hz, in comparison with the single-ended mode. In addition, our proposed ME sensor also has a low inherent equivalent magnetic noise of 18.3 pT/√Hz at 1 Hz. Finally, a target detection experiment in the presence of heavy lab noise and strong vibration interference is conducted and the improved detection performance of the proposed differential ME sensor is proved.

Numerical simulation of a two-dimensional Blume-Capel ferromagnet in an oscillating magnetic field with a constant bias.

We perform a numerical study of the kinetic Blume-Capel (BC) model to find if it exhibits the metamagnetic anomalies previously observed in the kinetic Ising model for supercritical periods [P. Riego et al., Phys. Rev. Lett. 118, 117202 (2017)0031-900710.1103/PhysRevLett.118.117202; G. M. Buendía et al., Phys. Rev. B 96, 134306 (2017)2469-995010.1103/PhysRevB.96.134306]. We employ a heat-bath Monte Carlo (MC) algorithm on a square lattice in which spins can take values of ±1,0, with a nonzero crystal field, subjected to a sinusoidal oscillating field in conjunction with a constant bias. In the ordered region, we find an equivalent hysteretic response of the order parameters with its respective conjugate fields between the kinetic and the equilibrium model. In the disordered region (supercritical periods), we observed two peaks, symmetrical with respect to zero bias, in the susceptibility and scaled variance curves, consistent with the numerical and experimental findings on the kinetic Ising model. This behavior does not have a counterpart in the equilibrium model. Furthermore, we find that the peaks occur at higher values of the bias field and become progressively smaller as the density of zeros, or the amplitude of the oscillating field, increases. Using nucleation theory, we demonstrate that these fluctuations, as in the Ising model, are not a critical phenomenon, but that they are associated with a crossover between a single-droplet (SD) and a multidroplet (MD) magnetization switching mechanism. For strong (weak) bias, the SD (MD) mechanism dominates. We also found that the zeros concentrate on the droplets' surfaces, which may cause a reduced interface tension in comparison with the Ising model [M. Schick et al., Phys. Rev. B 34, 1797 (1986)0163-182910.1103/PhysRevB.34.1797]. Our results suggest that metamagnetic anomalies are not particular to the kinetic Ising model, but rather are a general characteristic of spin kinetic models, and provide further evidence that the equivalence between dynamical phase transitions and equilibrium ones is only valid near the critical point.

Surface weak ferromagnet coupling induced giant room-temperature spontaneous exchange bias in antiferromagnet Fe3BO6 polycrystals.

The spontaneous exchange bias effect (SEB) has wide application prospects in information storage technologies. In this study, nanoscale raw materials were used to fabricate antiferromagnetic Fe3BO6 polycrystals. The obtained Fe3BO6 exhibited a large SEB effect, where the value of the spontaneous exchange bias field at room temperature was as large as ∼4234 Oe. The room-temperature training effect, temperature-dependence, and maximum field-dependence of the HSEB were investigated. We propose that this giant SEB originates from the exchange-coupling interactions between the weak ferromagnetic surface state and the bulk antiferromagnetic state. The theoretical analysis results were further verified by comparing the magnetic properties of the Fe3BO6 with relatively low crystallinity. The results of this investigation will help find promising candidate materials for devices based on the SEB effect.

First TPymT-Ln complexes (TPymT = 2,4,6-Tris(2-pyrimidyl)-1,3,5-triazine; Ln = Eu, Gd, Tb, Dy): Solvothermal synthesis, structure, magnetic and photoluminescent properties

Four novel mononuclear lanthanide complexes, [Ln(TPymT)(NO3)3(H2O)2] (Ln: Eu3+ (1), Gd3+ (2), Tb3+ (3) and Dy3+ (4)), have been solvothermally synthesized using multidentate 2,4,6-tris(2-pyrimidyl)-1,3,5-triazine (TPymT). Compounds 1–3 have been characterized by means of elemental analysis, FT-IR, and single-crystal/powder X-ray diffraction analysis. Compound 4 was structurally characterized. The Ln3+ ion in 1–4 is ten-coordinated, where TPymT serves as an N3 donor in an isostructural series. The alternating-current magnetic studies showed frequency dependence below ca. 10 K for 3, as an indication of a single-ion magnet. The activation energy for the magnetization reorientation was estimated as Ueff/kB = 95(9) K after applying a dc bias field 2000 Oe for 3. The photoluminescent studies clarified that 1 and 3 behaved as red and green light emitters with quantum yields as high as 17 and 39 %, respectively. The TD-DFT calculation supports the energy level scheme of ground and excited states of TPymT together with the reference compound 4′-phenyl-2,2’:6′,2″-terpyridine. The present work suggests a broad chance and motivation to apply TPymT to the field of 4f coordination chemistry.

Spin transfer torque and field driven 360° domain wall to bimeron conversion

In this work, using micromagnetic simulations we study the mechanism involved in the conversion of a 360° domain wall into stable pairs of bimeron (Q = +1) and antibimeron (Q = -1) in a ferromagnetic background when it is subjected to Zhang Li type of spin transfer torque. We show that the time scales associated with the conversion process can be significantly reduced by applying out of plane bias field. In particular, choosing a constant inclination angle and increasing the intensity of bias field aids in reducing the energy barrier required to stabilize the spin textures. Similarly, for a constant bias field intensity, varying the inclination angle between 0° to 40° reveals that the time required to stabilize the first bimeron is reduced by ∼ 3 ns. Further, tailoring the strength of in-plane current density as well as effective uniaxial anisotropy leads to faster conversion of domain wall to bimerons.

Resonant tunneling properties of laser dressed hyperbolic Pöschl-Teller double barrier potential

We examine the resonant tunneling properties of the laser-dressed hyperbolic Pöschl-Teller double quantum barrier structure. We use the non-equilibrium Green's function method to investigate structure parameters and electric field bias on the transmission properties of the system. The transmission probabilities and resonance energy levels are significantly influenced by the well widths and barrier heights. The barrier height increases, resonance energy levels shift toward higher values, and the resonance peak width narrows, leading to sharper and more selective tunneling behavior. Our results show that increasing the electric field bias leads to a decrease in the transmission probability at the first resonance peak, but this effect is not as strong for the subsequent peaks. Moreover, we find that changes in the laser field's parameter and structure parameters allow for fine control over the electronic spectra, allowing for modifications like red or blue shifts based on particular needs. The significance of comprehending the interaction among structural factors, external fields, and transmission qualities in quantum barrier structures is highlighted by our research, providing valuable information for the development and enhancement of electronic and optoelectronic systems with customized functionality. Our findings show the laser field has a considerable impact on resonant tunneling properties, opening the door to new device applications.

Simulation of Dendrite Growth with a Diffusion-Limited Aggregation Model Validated by MRI of a Lithium Symmetric Cell during Charging

Lithium metal batteries offer high energy density but are challenged by dendrite growth, which can lead to short circuits and battery failure. Multiple models with varying degrees of accuracy and computational cost have been developed to understand and predict dendrite growth. This study presents a simple model to simulate macroscale dendrite growth on lithium metal electrodes. The model uses a 3D single-particle Diffusion-Limited Aggregation (DLA) algorithm with an electric field bias to simulate dendrite growth. The electric field bias was introduced into the model with an important parameter, namely the biasing factor c, which determines the balance between diffusion and electric field effects. Before performing the simulation with the proposed model, the dendrite growth in a lithium symmetric cell during charging was measured by sequential 3D magnetic resonance imaging (MRI). These data were then used to validate the simulation, as the dendrite structure in each measured MRI time frame was used a starting point for a new simulation, the results of which were then validated with the measured dendrite structure of the next time frame. The best agreement between the simulated and measured dendrite structures using the overlap and displacement of deposition sites metrics was obtained at the biasing factor c = 0.7. This agreement was also good in terms with the fractal dimension of the dendrite structures. The proposed method offers a simple, accurate, and scalable framework for predicting dendrite growth over long deposition periods, making it a valuable tool for studying dendrite suppression under real-world battery charging conditions.