Magnetic Signal Research Articles

PROGRAM AND ANALYTICAL COMPLEX OF THE INDUSTRIAL SAFETY AND HEALTH ANALYSIS SYSTEM

Aim. To investigate the quantitative characteristics of magnetic fields created during electric contact welding by various methods: contact point, arc-contact, capacitor point, contact-contact continuous and pulsating reflow. The problem being solved is the lack of necessary information on the electromagnetic safety of the specified welding methods. Methodology. A description of the proposed methodical approaches to determining the levels of magnetic fields, means of measuring them, and methods of assessing their impact on the welder's body is provided. Based on the analysis and processing of the received oscillograms and spectrograms of magnetic fields, measurements of their quantitative characteristics were performed. To determine the general level of the multifrequency magnetic field that occurs during contact welding, the proposed generalized indicator of the level of the magnetic field was used. Implications of research. It was established that during contact spot welding with a stationary machine, the magnetic field level exceeds the maximum permissible value at the workplace in the range of 50-1000 Hz at a distance of 0.3 m from the welding electrodes. During manual welding by this method, the level of the magnetic field exceeds the permissible level in the frequency ranges of 5-50, 50-1000 Hz directly near the electric cable. Condenser spot welding with direct current is characterized by exceeding the maximum allowable MP at the workplace in the high-frequency range of 1000–10000 Hz. During arc-joint welding at the workplace, exceeding the maximum allowable levels of the magnetic field was not detected. Practical implications. It is shown that the spectral composition and magnitude of the magnetic field signal is determined by the welding method itself and the initial parameters of the power sources. The obtained results can be used in the field of welding production and labor protection for the improvement of contact welding technologies and the development of measures to protect welders from the magnetic field.

Detecting biomarkers by dynamic nuclear polarization enhanced magnetic resonance.

Hyperpolarization stands out as a technique capable of significantly enhancing the sensitivity of nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI). Dynamic nuclear polarization (DNP), among various hyperpolarization methods, has gained prominence for its efficacy in real-time monitoring of metabolism and physiology. By administering a hyperpolarized substrate through dissolution DNP (dDNP), the biodistribution and metabolic changes of the DNP agent can be visualized spatiotemporally. This approach proves to be a distinctive and invaluable tool for non-invasively studying cellular metabolism in vivo, particularly in animal models. Biomarkers play a pivotal role in influencing the growth and metastasis of tumor cells by closely interacting with them, and accordingly detecting pathological alterations of these biomarkers is crucial for disease diagnosis and therapy. In recent years, a range of hyperpolarized DNP molecular bioresponsive agents utilizing various nuclei, such as 13C, 15N, 31P, 89Y, etc., have been developed. In this context, we explore how these magnetic resonance signals of nuclear spins enhanced by DNP respond to biomarkers, including pH, metal ions, enzymes, or redox processes. This review aims to offer insights into the design principles of responsive DNP agents, target selection, and the mechanisms of action for imaging. Such discussions aim to propel the future development and application of DNP-based biomedical imaging agents.

Formation of a Magnetic Cloud from the Merging of Two Successive Coronal Mass Ejections

On 2022 March 28 two successive coronal mass ejections (CMEs) were observed by multiple spacecraft and resulted in a magnetic cloud (MC) at 1 au. We investigate the propagation and interaction properties of the two CMEs correlated with the MC using coordinated multipoint remote sensing and in situ observations from Solar Orbiter, STEREO-A, Solar and Heliospheric Observatory, and Wind. The first CME was triggered by a filament eruption with a high inclination angle. Roughly 9 hr later, the second CME originating from the same active region erupted with a smaller tilt angle and faster speed compared to the first one. The second CME overtook the preceding CME and formed a merged front at approximately 75 R ☉, which developed into a complex ejecta at 1 au. The descending speed and low proton temperature inside the complex ejecta suggest that the two CMEs have fully merged before reaching 1 au, leading them to begin expanding rather than compressing against each other. The complex ejecta appears to have the magnetic field and plasma signatures of an MC, although there is a discontinuity in the magnetic field implying previous interactions. The cross section of the complex ejecta, reconstructed from in situ data using a Grad–Shafranov technique, exhibits a right-handed flux rope structure. These results highlight that an MC-like complex ejecta lacking interaction features could arise from the complete merging of two CMEs.

Evaluating the performance of optically pumped magnetometer magnetoencephalography in measuring inter-trial and inter-region phase-locking value

Magnetoencephalography (MEG) records the brain magnetic field signals. Optically pumped magnetometer (OPM) is a novel technology for MEG measurement in its infancy, it is essential to demonstrate its performance on the key neuroimaging features, such as phase-locking value. In this paper, the inter-trial phase-locking value (ITPL) and inter-region phase-locking value (IRPL) were analyzed by simulation and measurement experiments using OPM-MEG, the superconducting quantum interference device (SQUID) data was applied for the compare. The simulation results show that OPMs could offers good characterization on phase-locking value under higher noise intensity. In experiments, the similarity ratios of ITPL and IRPL between OPM-MEG and SQUID-MEG are >0.90. The significantly increased frequencies of IRPL in pure tone and somatosensory stimulation are at about 10–20 Hz and 8–13 Hz respectively, with Spearman correlation coefficients >0.7. The results provide experimental evidence of the ability of OPM-MEG for measuring phase synchronization and functional connectivity on sensor signals.

The usefulness of the magnetic susceptibility of loess paleosol sequences for paleoclimate and stratigraphic studies: The case of the Quaternary Palouse loess, northwestern United States

Worldwide, Quaternary loess-paleosol sequences archive terrestrial paleoenvironmental information. The Palouse loess in the northwestern USA is one such deposit representing at least a million years of sediment accumulation. Loess paleosol sequences are often studied via the magnetic susceptibility of sediments and the changes in susceptibility with depth. However, since variations in magnetic susceptibility can have different underlying causes, the interpretation of the magnetic susceptibility of loess depends on factors specific to each major loess region. We measured the magnetic susceptibility of sediments along depth profiles in a well-studied exposure of the Palouse loess in eastern Washington, USA (the “CLY-2 site,” 46.3131° N, 118.4874° W, WGS84). Our measurements show that the magnetic susceptibility of sediments in the Palouse loess varies systematically and predictably with depth and can be correlated with other magnetic susceptibility profiles at the outcrop scale and at the regional scale. We also measured changes in grain size, elemental abundance, mineralogy, and other magnetic properties to determine the most likely cause of the magnetic susceptibility signal. Based on these results, variations in magnetic susceptibility in the Palouse are likely driven mostly by changes in coarse sediment supply and accumulation of tephra rather than changes in weathering. Magnetic susceptibility is useful as an inexpensive, non-destructive tool for stratigraphic correlation of Quaternary loess deposits in the Palouse, especially in combination with paleosol stratigraphic markers and known tephra deposits.

Transition of dome formation to sudden explosive eruptions at Popocatépetl, Mexico: magnetic indicators

Transitions from effusive to explosive activity can increase hazards making it crucial to define early indicators such as changes in the magnetic signals. After more than 80 cycles of crater-dome extrusion and destruction from 1996 on, Popocatépetl volcano (Mexico) experienced changes in its behavior from March 15 to 18 July 2019, when no lava domes were observed. Some of the domes behaved as contained lava flows within the crater floor (pancakes) while others were more irregular-shaped. Activity decreased considerably over this 2019 interval except for the unexpected explosions in March and June, that produced ash plumes reaching up to 14,000 m a.s.l. In order to investigate the causes of the transition from effusive to explosive behavior in March and June, we analyzed the time series from the magnetic monitoring network at Popocatépetl volcano between October 2018 and December 2019. The raw signals were analyzed by weighted differences (WD) based on the elimination of non-local changes from the total intensity values of the geomagnetic field and the discrete-time continuous wavelet transform was used to evaluate the local variations of energy within the time series. The high energy periods (linked to negative magnetic anomalies) are induced by magma ascent associated with movement within the conduit. They indicate that the sudden explosions were due to the ascent of several magma batches that were slowed during ascent and were not able to reach the surface. Changes in the rheology of the lava are linked to the influx of several batches of magma with different compositions as well as to compaction by gas loss when ascending andesitic magma pushed out overlying more viscous degassed magma clearing the conduit, which can explain why these sudden explosions were more energetic. Several geophysical data sets as well as tephra compositions were integrated to support this conclusion. The correlated multiparameters also confirm that geomagnetic volcano monitoring has been essential in understanding the processes that drive the observed changes in eruptive behavior. We present new evidence for the detection of transient events produced by magma ascent and changes in the feeding system of Popocatépetl volcano with wavelet analysis. Detailed vulcanomagnetic processing, especially when it is correlated with other monitoring parameters, provides information on ascending magma and several conduit processes that would otherwise be camouflaged. Ascending batches may precede an eruption but they can also ascend in several pulses indicating how dome growth occurs.

Electrical Conductivity of Subsurface Ocean Analogue Solutions from Molecular Dynamics Simulations.

Investigating the habitability of ocean worlds is a priority of current and future NASA missions. The Europa Clipper mission will conduct approximately 50 flybys of Jupiter's moon Europa, returning a detailed portrait of its interior from the synthesis of data from its instrument suite. The magnetometer on board has the capability of decoupling Europa's induced magnetic field to high precision, and when these data are inverted, the electrical conductivity profile from the electrically conducting subsurface salty ocean may be constrained. To optimize the interpretation of magnetic induction data near ocean worlds and constrain salinity from electrical conductivity, accurate laboratory electrical conductivity data are needed under the conditions expected in their subsurface oceans. At the high-pressure, low-temperature (HPLT) conditions of icy worlds, comprehensive conductivity data sets are sparse or absent from either laboratory data or simulations. We conducted molecular dynamics simulations of candidate ocean compositions of aqueous NaCl under HPLT conditions at multiple concentrations. Our results predict electrical conductivity as a function of temperature, pressure, and composition, showing a decrease in conductivity as the pressure increases deeper into the interior of an icy moon. These data can guide laboratory experiments at conditions relevant to icy moons and can be used in tandem to forward-model the magnetic induction signals at ocean worlds and compare with future spacecraft data. We discuss implications for the Europa Clipper mission.

Compact and Stable Diamond Quantum Sensors for Wide Applications

AbstractThis study proposes compact, highly sensitive, and stable diamond quantum sensors for a wide range of applications, including biomedical and energy electronics. For enhanced sensitivity and alignment precision within the objective field, a high‐quality, (111)‐oriented 12C‐enriched chemical vapor deposition (CVD) diamond, featuring a nitrogen‐vacancy (NV) axis in the (111) direction, is employed as the sensor. To increase the fluorescence collection efficiency, the laser beam is irradiated from the side surface of the CVD diamond, and fluorescence is detected using a compound parabolic concentrator (CPC) lens. The floor noise level of the magnetic field signal is 44 pT/Hz0.5. An Allan deviation of 1.2 pT over 1000 s of averaging demonstrates stability. This is attributable to the integration of a balancing circuit to cancel out laser noise, alongside mechanisms to compensate for temperature fluctuations and a copper housing to shield against electromagnetic field noise.

Learning Soft Millirobot Multimodal Locomotion with Sim-to-Real Transfer.

With wireless multimodal locomotion capabilities, magnetic soft millirobots have emerged as potential minimally invasive medical robotic platforms. Due to their diverse shape programming capability, they can generate various locomotion modes, and their locomotion can be adapted to different environments by controlling the external magnetic field signal. Existing adaptation methods, however, are based on hand-tuned signals. Here, a learning-based adaptive magnetic soft millirobot multimodal locomotion framework empowered by sim-to-real transfer is presented. Developing a data-driven magnetic soft millirobot simulation environment, the periodic magnetic actuation signal is learned for a given soft millirobot in simulation. Then, the learned locomotion strategy is deployed to the real world using Bayesian optimization and Gaussian processes. Finally, automated domain recognition and locomotion adaptation for unknown environments using a Kullback-Leibler divergence-based probabilistic method are illustrated. This method can enable soft millirobot locomotion to quickly and continuously adapt to environmental changes and explore the actuation space for unanticipated solutions with minimum experimental cost.

Validation of EUHFORIA cone and spheromak coronal mass ejection models

We present validation results for calculations of arrival times and geomagnetic impact of coronal mass ejections (CMEs) using the cone and spheromak CME models implemented in EUropean Heliospheric FORecasting Information Asset (EUHFORIA). Validating numerical models is crucial for ensuring their accuracy and performance with respect to real data. We compared CME plasma and magnetic field signatures measured in situ by satellites at the L1 point with the simulation output of EUHFORIA. The validation of this model was carried out using two datasets in order to ensure a comprehensive evaluation. The first dataset focuses on 16 CMEs that arrived at Earth, offering specific insights into the model's accuracy in predicting arrival time and geomagnetic impact. Meanwhile, the second dataset encompasses all CMEs observed over eight months within Solar Cycle 24, regardless of whether or not they arrived at Earth, covering periods of both solar minimum and maximum activity. This second dataset enables a more comprehensive evaluation of the model's predictive precision in term of CME arrivals and misses. Our results show that EUHFORIA provides good estimates in terms of arrival times, with root mean square error (RMSE) values of 9 hours. Regarding the number of correctly predicted ICME arrivals and misses, we find a 75<!PCT!> probability of detection in a 12 hour time window and 100<!PCT!> probability of detection in a 24 hour time window. The geomagnetic impact forecasts measured by the $K_p$ index provide different degrees of accuracy ranging from 31<!PCT!> to 69<!PCT!>. These results validate the use of cone and spheromak CMEs for real-time space weather forecasting.

Broadband dark matter axion detection using a cylindrical capacitor

Cosmological axions/axion-like particles can compose a significant part of dark matter; however, the uncertainty of their mass is large. Here, we propose to search the axions using a cylindrical capacitor, in which the static electric field converts dark matter axions into an oscillating magnetic field. Due to the odd CPs, the axions couple to the electric field differently compared to the magnetic field. The axion couples to the electric field via a derivative that carries spatial information of incoming dark matter flux, while the coupling to the magnetic field depends on the dark matter density. This difference could be helpful in searching the axions and studies of the integrity of the theory, especially when the axions are very light, in which case the magnetic field-induced signal is DC-like. Orientation dependence could also be used to reduce the kinetic fluctuation-induced noise when multiple detectors operate simultaneously. In addition, a cylindrical setup shields the electric field to the laboratory and encompasses the axion-induced magnetic field within the capacitor. The induced oscillating magnetic field can then be picked up by a sensitive magnetometer. Adding a superconductor ring-coil system into the scheme can further boost the sensitivity and maintain the axion dark matter inherent bandwidth. This proposed setup could be capable of wide mass range searches.

Amplification- and Enzyme-Free Magnetic Diagnostics Circuit for Whole-Genome Detection of SARS-CoV-2 RNA.

Polymerase chain reaction (PCR) requires thermal cycling and enzymatic reactions for sequence amplification, hampering their applications in point-of-care (POC) settings. Magnetic bioassays based on magnetic particle spectroscopy (MPS) and magnetic nanoparticles (MNPs) are isothermal, wash-free, and can be quantitative. Realizing them amplification- and enzyme-free on a benchtop device, they will become irreplaceable for POC applications. Here we demonstrate a first-in-class magnetic signal amplification circuit (MAC) that enables detection of whole genome of SARS-CoV-2 by combining the specificity of toehold-mediated DNA strand displacement with the magnetic response of MNPs to declustering processes. Using MAC, we detect the N gene of SARS-CoV-2 samples at a concentration of 104 RNA copies/μl as determined by droplet digital PCR. Further, we demonstrate that MAC can reliably distinguish between SARS-CoV-2 and other human coronaviruses. Being a wash-, amplification- and enzyme-free biosensing concept and working at isothermal conditions (25 °C) on a low-cost benchtop MPS device, our MAC biosensing concept offers several indispensable features for translating nucleic acid detection to POC applications.

Flexible anisotropic magnetoresistive sensors for novel magnetic flux leakage testing capabilities

Rigid magnetic field sensors such as anisotropic magnetoresistive (AMR), giant magnetoresistive (GMR) and Hall sensors have been used for years and have become industry standard for electromagnetic non-destructive testing (NDT). Recent technological developments in the field of flexible electronics allow for the fabrication of reshapeable magnetic field sensors on flexible substrates via thin-film deposition or printing. The magnetic properties of these sensors have comparable characteristics to industry-standard rigid magnetic field sensors, with the added ability of adapting to the surface of complex components and scanning in contact with the sample surface. This improves defect detectability and magnetic signal strength by minimizing the scanning lift-off (LO) distance. In this article flexible AMR sensors mounted on a rotative mechanical holder were used to scan a semi-circular ferromagnetic sample with 3 reference defects via magnetic flux leakage (MFL) testing, thus demonstrating the applicability of this type of sensors for the scanning of curved samples. In order to benchmark the performance of these sensors in comparison to industry standard rigid magnetic field sensors, a ferromagnetic sample with 10 reference defects of different depths was scanned employing flexible AMR and rigid GMR sensors. Defects with depths ranging from 110μm up to 2240μm were detected with an signal-to-noise ratio (SNR) of 2.7 up to 27.9 (for flexible AMR sensors) and 6.2 up to 72.3 (for rigid GMR sensors), respectively. A 2D magnetometer mapping of the sample with a spatial scanning step of 10×50μm2 (flexible AMR) and 16×100μm2 (rigid GMR) was obtained. The results show that this type of sensor can be used for high-resolution and high-detail mapping of defects on the surface of planar and non-planar ferromagnetic samples since the scanning lift-off distance is equal to the substrate thickness of 20μm for in-contact scanning. The SNR comparison between flexible and rigid sensors shows that the performance of the flexible AMR sensors employed is not very far behind the performance of the rigid GMR sensors used.

Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods

Background Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare. Methods The study selected CS patients undergoing cervical spine surgery and investigated their medical data. A total of 945 patients were recruited, with 570 males and 375 females. The mean number of LOS calculated for the total sample was 8.64 ± 3.7 days. A LOS equal to or <8.64 days was categorized as the AHD-negative group (n = 539), and a LOS > 8.64 days comprised the AHD-positive group (n = 406). The collected data was randomly divided into training and validation cohorts using a 7:3 ratio. The parameters included their general conditions, chronic diseases, preoperative clinical scores, and preoperative radiographic data including ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), cervical instability and magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operative indicators and complications. ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. Calibration curve and decision curve analysis (DCA) were performed to test the calibration performance and clinical utility. Results For these participants, 25 statistically significant parameters were identified as risk factors for AHD. Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. These factors were gender, age, body mass index (BMI), American Spinal Injury Association (ASIA) scores, magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operated segment, intraoperative bleeding volume, the volume of drainage, and diabetes. After model validation, the AUC was 0.753 in the training cohort and 0.777 in the validation cohort. The calibration curve exhibited a satisfactory agreement between the nomogram predictions and actual probabilities. The C-index was 0.788 (95% confidence interval: 0.73214–0.84386). On the decision curve analysis (DCA), the threshold probability of the nomogram ranged from 1 to 99% (training cohort) and 1 to 75% (validation cohort). Conclusion We successfully developed an ML model for predicting AHD in patients undergoing cervical spine surgery, showcasing its potential to support clinicians in AHD identification and enhance perioperative treatment strategies.

An Advanced Hall Element Array-Based Device for High-Resolution Magnetic Field Mapping.

The precise mapping of magnetic fields emitted by various objects holds critical importance in the fabrication of industrial products. To meet this requirement, this study introduces an advanced magnetic detection device boasting high spatial resolution. The device's sensor, an array comprising 256 unpackaged gallium arsenide (GaAs) Hall elements arranged in a 16 × 16 matrix, spans an effective area of 19.2 mm × 19.2 mm. The design maintains a 1.2 mm separation between adjacent elements. For enhanced resolution, the probe scans the sample via a motorized rail system capable of executing specialized movement patterns. A support structure incorporated into the probe minimizes the measurement distance to below 0.5 mm, thereby amplifying the magnetic signal and mitigating errors from nonparallel probe-sample alignment. The accompanying interactive software utilizes cubic spline interpolation to transform magnetic readings into detailed two- and three-dimensional magnetic field distribution maps, signifying field strength and polarity through variations in color intensity and amplitude sign. The device's efficacy in accurately mapping surface magnetic field distributions of magnetic and magnetized materials was corroborated through tests on three distinct samples: a neodymium-iron-boron magnet, the circular magnetic array from a smartphone, and a magnetized 430 steel plate. These tests, focused on imaging quality and magnetic field characterization, underscore the device's proficiency in nondestructive magnetic field analysis.

Coexistence of antiferromagnetism and glassy magnetic state and signature of quantum interference effects in the frustrated ternary silicides HoScSi and ErScSi

Coexistence of antiferromagnetism and glassy magnetic state and signature of quantum interference effects in the frustrated ternary silicides HoScSi and ErScSi

Causal Forest Machine Learning Analysis of Parkinson's Disease in Resting-State Functional Magnetic Resonance Imaging.

In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson's disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F1 score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson's Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups.

Enigmatic mixture of magnetite magnetofossils and diagenetic greigite as the magnetic carriers of the Early Miocene lacustrine sediments from the Most Basin in Central Europe

The Early Miocene lacustrine sediments of the Most Basin in the Czech Republic preserve a European continental paleoenvironmental archive. A number of paleoenvironmental and magnetostratigraphic studies have been carried out on sediment cores from boreholes due to ongoing coal mining in the basin. However, the magnetic carriers of the studied sediments have not been identified clearly. Here, we present a detailed paleo-rock magnetic study from the Burdigalian sediments near the Bilina mining area, Most Basin. The studied clay sediments cover the period of local lakes and a basin-wide lake above the main coal seam. Our results suggest that the magnetic carriers of the studied section in the Most Basin are mixtures of authigenic greigite and magnetite magnetofossils with overlapping magnetic signatures. Greigite is formed by migration of pore water through the sediment column, where iron from siderite grains reacts with these fluids with limited H2S, which then favors greigite precipitation. The co-existence of greigite and magnetite indicates a partial dissolution of magnetofossils due to H2S deficiency. Diagenetic greigite has been problematic in paleomagnetic studies due to an unknown time lag between the depositional remanence and the chemical remanent magnetization (CRM). A ghost polarity interval reveals that greigite acquired at least ∼45 kyr delayed CRM. The revealed timing of remanence acquisition brings a new perspective to the chronostratigraphic structure of the Most Basin.

Theoretical limits of magnetic detection of structural surface defects at the nanometre scale

ABSTRACT We present a theoretical study on the magnetic signals of structural surface defects like cracks or indents combined with inhomogeneities on the surface or subsurface inclusions of soft ferromagnetic metals like body-centred cubic Fe or amorphous CoFeB. We discuss limits of early detection of small surface defects on the basis of calculated magnetic stray fields few tens of nm above the surface. The considered surface imperfections have extensions of a few nm which correspond to low multiples of the magnetic exchange lengths of Fe or CoFeB. The detection of such small inhomogeneities requires that the sensor is about as close to the surface as the size of the inhomogeneity is. Furthermore, the step width of a scanning sensor must be of the same size as well. Both these requirements may be fulfilled for instance by scanning microscopy with diamond nitrogen-vacancy-centre quantum sensors.

Ferromagnetic Target Detection in the Ocean Using Drone-based Magnetic Anomaly Detection

Magnetic anomaly signals from the ferromagnetic targets such as ships in the sea are measured by drone-based magnetic anomaly detection. A quantum magnetometer is suspended from the drone by 4 strings. Flight altitude and speed of drone are 100 m and 5 m/s, respectively. We obtain magnetic anomaly signals of few nT from the ships clearly. We analyze the signal characteristics by the ferromagnetic target through simulation using COMSOL multiphysics.