Fibre optic magnetic field sensor based upon double long period fibre grating cascade and Galfenol rod

High-sensitivity optical fiber magnetic field sensor based on non-intrinsic F-P interference

Chemical reactions within living systems are made possible by bioorthogonal catalysis without interfering with regular biological processes. However, under physiological conditions, many transition-metal catalysts become toxic or unstable. By enhancing stability, safeguarding the catalyst, and enabling regulated, magnetically controlled activation and directed delivery to particular locations, magnetic nanoparticles (MNPs) overcome these difficulties. Reliable reactions like palladium-mediated decaging and in situ prodrug activation are supported by their adjustable cores and versatile organic or inorganic coatings. Systems based on Fe-Pd nanowires, silica-confined catalytic shells, magnetothermia-responsive nanoreactors, SPAAC-clickable surfaces, and cascade magnetic sensors show that MNPs may carry out effective and selective catalysis in challenging biological settings. All things considered, MNP platforms offer a viable approach for secure, focused, and manageable bioorthogonal reactions that are beneficial for medical and diagnostic uses. In addition, recent developments in bioorthogonal catalysis activated by external stimuli such as light, ultrasound, and other physical fields are briefly discussed. The present review aims to comprehensively discuss the current state of magnetic nanoreactors for bioorthogonal catalysis, addressing key challenges in biological integration and projecting future directions for clinical and diagnostic translation.

Additive manufacturing enables fabrication of intricate electronic devices embedded in complex-shaped structural components. Here, we add a new member to the family of 3D-printed electronics - a high-performance 3D-printed magnetic field sensor featuring more than 300% magnetoimpedance effect at low frequencies and single point magnetic vector field reconstruction relying on 3D Hall effect magnetometry. The sensors are shaped as mechanically flexible and magnetically controllable springs as well as 3D crosses demonstrating abilities in tailoring the sensor response, operation field range, and operation frequency through rational design of the sensor geometry. The application potential of 3D-printed magnetoelectronics is featured via magnetic toggle switches for smart home, continuous joysticks for robotics control, three-axis magnetometers for volumetric multi-point detection, and self-aware 4D-printed mechatronic actuators. This technology enables 4D-printed structures that fold motion sensing directly into their design and enable each part to interact intelligently within a larger mechanism being aware of their environment and userinteractions.

The Hill coefficient as Fisher information: An information-theoretic identity for allosteric cooperativity.

This paper introduces a weak-magnetic-field measurement system characterized by a large-scale uniform magnetic field and a low magnetic limit of detection (LOD). The system employs a four-ring coil assembly housed within a multi-layer magnetic shielding cavity, generating a uniform magnetic field region of 120 mm while achieving a minimum LOD of less than 10 pT. The performance of the weak-magnetic-field measurement system is appropriately validated using a bulk magnetic-electric (ME) sensor. The experimental results confirm the system's dual functionalities in both magnetic sensor calibration and the measurement of weak magnetic parameters. Notably, this methodology is readily applicable to various forms of weak-magnetic-field measurement.

The aim is to develop and test a framework that integrates real-time catheter shape reconstruction, interactive simulations, and mixed reality visualization to enable accurate monitoring of catheter vessel interactions during endovascular navigation. A finite element model (FEM) of the venous pathway from the right femoral vein to the inferior vena cava was generated from computed tomography data and implemented into an interactive simulation. Catheter motion was imposed as boundary condition, and catheter vessel contact was modeled with a Lagrange multiplier formulation to compute vessel deformation. The framework was integrated and tested in vitro using a sensorized catheter with fiber Bragg grating and electromagnetic sensors to provide real-time 3D shape and location as it was advanced by a catheter driver through a silicone replica of the vascular anatomy. Upon registration, real-time sensor read-outs fed the simulation, and the updated catheter and vessel geometries were streamed to Hololens (HL2). The performance of the simulation and the accuracy of FEM-computed vessel wall displacement were validated vs. experimental ground truth obtained via stereo frames triangulation. Complexity and extent of catheter vessel interaction affected FEM performance by increasing the computational cost. The simulated time exceeded the real temporal extent of the physical phenomenon by 12% during the initial navigation phase and by 45% when the catheter reached the most tortuous portion of the vessel. The HL2 rendering remained stable between 35 and 40 frames per second. Across these two phases, the median relative displacement error between FEM-computed vessel wall displacements and the ground truth remained below 1mm and 2.33 mm, respectively. The study demonstrates the feasibility of integrating interactive biomechanical simulation with real-time sensor data to enable continuous monitoring of catheter vessel interactions, with mixed reality visualization serving as a user interface to support a more engaged and better-informed operator throughout the navigation.

We address the challenge of sensing self-motion and environmental information by autonomous underwater vehicles. To overcome the limitations of conventional sensing methods in terms of size, cost, and environmental restrictions, previous work has investigated biomimetic flow sensors. Challenges in application of those biomimetic sensors include measuring complex locomotion of the vehicles and detecting external flow structure during the motion, all within a compact form suitable for a small vehicle. To address the challenges, we present a small and lightweight soft magnetic hair flow sensor that can measure the speed, direction, and oscillation of flow based on its mechanical deflection. We tested the ability of these sensors on underwater robots to estimate their forward, lateral (angular) speeds and orientations. While the robots were swimming, the sensors could also detect the wake behind an upstream object by identifying its characteristic frequency. The bioinspired hydrodynamic sensing, as demonstrated with the proposed sensors in this article, could allow adaptive and efficient underwater exploration.

Accurate measurement of current switching transients in SiC and GaN power converters requires current sensors with bandwidths exceeding 100 MHz, along with high immunity, sensitivity, isolation, and usability. Magnetic coils are typically the least invasive type of sensor but can be challenging to design and may be unsuitable in high EMI environments. This paper explores the use of coreless twin-coil magnetic current sensors in compact power circuits with complex magnetic fields. A design method is presented to achieve the required bandwidth, immunity, and signal-to-noise ratio (SNR), guided by 2D gain and immunity plots to optimise orientation relative to adjacent currents. Equations for gain and bandwidth as functions of geometry are experimentally verified using a vector network analyser. The method is validated in two scenarios: individual gate current sensing on adjacent, parallel-connected power devices, and power loop current sensing on a 30 mm wide PCB trace with a closely spaced return path on an adjacent layer. The gate sensor achieves an SNR above 10, a 660 MHz bandwidth, and closely matches current sense resistor measurements. The power loop current sensor has similar accuracy to a commercial Rogowski coil. These results demonstrate removable high bandwidth AC current sensors that minimally impact compact, optimised circuits, and show promise for application in power modules.

A tri-axial magnetic field fiber optic sensor based on a whisk-shaped single-mode fiber (WSMF) coated with soft magnetic elastomer is proposed. The sensor operates by exploiting the magneto-straining effect of Fe3O4@polydimethylsiloxane (PDMS) composite under an external magnetic field, combined with two orthogonally arranged balloon-like single-mode fiber (BLSMF) structures. When the fiber is bent into a balloon-like shape, variations in the refractive index distribution of the cladding and core induce optical leakage, forming a Mach-Zehnder interferometer. Finite element analysis reveals that a single Fe3O4-PDMS-coated BLSMF exhibits anisotropic deformation characteristics under the magnetic field. By cascading two BLSMFs in orthogonal configuration, the dip shift of the resonant wavelength is correlated not only with the magnitude of the magnetic field but also with its direction. This demonstrates that the whisk-shaped SMF configuration enables three-dimensional magnetic field measurement. In this study, the mass ratio of Fe3O4 nanoparticles in preparing the magnetic elastomer was varied from 10% to 50%. The optimized tri-axial magnetic field sensor achieves a magnetic sensitivity of 242.1 pm/mT and a rapid response time of 0.28 s. The proposed sensor shows great potential for applications in intelligent robotic control and human-machine interactions.

Magnetic sensors offer a physically grounded and non-invasive approach to probing biological processes that remain inaccessible to optical, electrochemical, and radio-frequency techniques in complex agricultural environments. In recent years, advances in both classical and quantum magnetic sensors have enabled the detection of bioelectromagnetic signals across plants, soils, animals, and aquatic systems, spanning spatial scales from ionic currents to organ-level electrophysiology and population-level dynamics, positioning magnetometry as an emerging modality within the broader biosensor landscape. This review surveys the evolution of magnetic sensing technologies for agricultural and animal systems, from robust classical sensors used in navigation and soil mapping to quantum-enabled platforms, including Optically Pumped Magnetometers (OPMs) and Nitrogen-Vacancy (NV) centers, capable of resolving pT to fT biomagnetic signals. We synthesize the characteristic amplitudes, frequency ranges, and physiological origins of agriculturally relevant magnetic signals, and critically assess how techniques originally developed for medical magnetoencephalography, magnetocardiography, and low-field magnetic resonance imaging (LF-MRI) are being translated into field-deployable agricultural applications. Beyond sensing hardware, we highlight the essential role of artificial intelligence in extracting weak biological signals from dominant environmental noise, enabling synthetic gradiometry, low-field image reconstruction, and scalable interpretation in unshielded settings. Finally, we discuss how the integration of magnetic biosensing with digital twins supports predictive, multiscale monitoring of plant, animal, and ecosystem health. Together, these developments position magnetometry as an enabling technology for next-generation biosensors in precision and sustainable agriculture.

This paper proposes a three-component time-domain electromagnetic detection system for borehole applications to meet the requirements of long-range detection in underground wellbore spaces. Under the structural constraints of limited borehole spaces, the system integrates several key components: an electromagnetic pulse-transmission circuit based on adaptive passive constant voltage clamping, multi-channel reception and master control circuits, three-component transmitting coil, and low-noise three-component magnetic field sensors. The detection system, thus, designed and developed has undergone system integration and experimental testing. Test results demonstrate that the system achieves optimized control of high-power pulsed current excitation with fall-edge turn-off, three-component dynamic electromagnetic signal transmission, multi-channel high-precision acquisition/storage of received signals, and data communication. The overall structure and performance indicators meet the requirements of time-domain electromagnetic detection in borehole environments. Thus, this research holds significant exploratory and practical potential for promoting and applying time-domain electromagnetic detection technology in borehole applications.

Wireless power transfer (WPT) systems for electric vehicles require reliable foreign object detection (FOD) mechanisms both during and prior to power transfer to ensure operational safety and efficiency. The primary purpose of this study was to develop a foreign object detection system to ensure that no objects are present in the area of magnetic coupling (between primary and secondary coils) prior to initiating power transfer. Conventional FOD techniques based on impedance, visual light, or thermal monitoring provide limited spatial information and are sensitive to coil misalignment. This paper proposes a machine learning-based FOD approach using a planar Magnetic Inductance Tomography (MIT) sensor array that enables spatial electromagnetic sensing for early detection and localisation of conductive foreign objects. A dataset comprising 17,800 measurement frames was collected using a custom STM32-based data acquisition system in the absence of (prior to) power transfer. Likewise, a dataset comprising 300 sets of measurement frames was collected during power transfer, in which each frame contains 120 electromagnetic sensor readings. This capture methodology coincides with the detection requirements of live WPT systems. Four classification models, including Random Forest, Support Vector Machine, XGBoost, and Multi-Layer Perceptron, were evaluated. To enhance robustness against sensor drift and environmental variations, feature-engineering techniques incorporating statistical, temporal, frequency-domain, and derivative-based features were developed. Experimental results demonstrate high detection accuracy under both controlled and real-world conditions. The proposed approach demonstrates the feasibility of integrating machine learning-based MIT sensing into wireless EV charging infrastructure for reliable foreign object detection.

Ocular trauma, including corneal opacity and intraocular bleeding, are often caused by blast, laser, or shrapnel injuries. Such opacities obstruct optical tracking and thereby impede intraoperative imaging and the visual control of untethered magnetic microrobots. Magnetic sensors have been used as an alternative approach for non-vision-based tracking to cope with visual confinement. However, accurate measurement is challenged by interference from an external magnetic field used for actuation. To resolve this issue, a non-vision-based localization approach using an alternating off–on strategy is introduced: (1) during localization (off), the field is temporarily deactivated to eliminate interference, allowing magnetic sensors to measure the microrobot’s intrinsic magnetic field; (2) during actuation (on), Helmholtz coils generate a controlled magnetic field. In the localization phase, a deep neural network estimates positions under low signal-to-noise conditions (15–17 dB), and a Kalman filter incorporating the dynamic model refines the estimates. Experimental validation in a 20 × 20 × 20 mm³ workspace within 1000 cSt silicone oil demonstrates a non-vision-based localization and feedback control, achieving a mean position error of 1.5 mm and confirming potential suitability for intraocular surgical applications, where visual feedback is limited. ( a ) Schematic representation of the external magnetic field excitation and magnetic field acquisition for microrobot localization using an off-on strategy. Phase I (off period) corresponds to a brief holding period during which magnetic sensors capture the microrobot's magnetic field for localization, while Phase II (on period) involves the application of an external magnetic field gradient for microrobot actuation. ( b ) Diagram of the non-vision-based localization framework for single and swarm microrobots. A single microrobot is localized using a deep neural network (DNN) combined with a Kalman filter (KF) that incorporates the microrobot’s dynamic model, while swarm microrobots are localized using an Extended Kalman Filter (EKF) that integrates their respective dynamic models. • Introduces a non-vision-based localization framework for untethered magnetic microrobots using Hall-effect magnetic sensors and an off–on actuation strategy that temporally separates sensing from magnetic actuation to mitigate interference. • Combines a deep neural network (DNN) with a Kalman filter to estimate microrobot position from magnetic flux measurements under low signal-to-noise conditions (15–17 dB) , enabling reliable localization without visual feedback. • Demonstrates accurate 3D localization and closed-loop control in a 20 × 20 × 20 mm³ workspace by integrating DNN+KF localization with Model Predictive Control (MPC), enabling autonomous trajectory tracking along geometric paths (S, C, star, hourglass) with average tracking errors below ~1.5 mm without visual sensing . • Demonstrates dual-agent localization capability using an Extended Kalman Filter (EKF) , enabling simultaneous tracking of multiple microrobots using magnetic sensor measurements. • Provides proof-of-concept for non-vision-based localization , demonstrating feasibility of magnetic-sensor-based tracking for microrobot navigation in environments with limited visual access.

Analytical calculation method for the amplification factor of superconducting/TMR composite magnetic sensors

Electromagnetic waves are widely used including in defense, biomedicine, and fundamental science. Their efficient detection determines how we communicate, defend against adversaries, diagnose diseases and perform search and rescue operations. In this article, exploiting the precession of a levitated magnetic particle in vacuum, we show that weak electromagnetic waves down to the femtotesla level can be detected. It is also shown that such a sensor has a large dynamic range over a millitesla, is continuously tunable over many gigahertz and can detect frequencies with sub-hertz resolutions. The direction of arrival of the incoming electromagnetic wave can also be found relatively easily.

Scaling wide-band-gap semiconductors to the ultrathin limit offers a transformative pathway for power electronics, with gallium nitride (GaN) representing a cornerstone material in this class. However, the operational resilience and functional tunability of its two-dimensional form (g-GaN) remain underexplored. This work shifts the focus from idealized systems to the complex materials behavior under realistic conditions, investigating how the synergistic effects of point vacancy defects, strain, and external electric fields govern its electronic, magnetic, and sensing landscapes. We demonstrate that these factors are not merely perturbations but are fundamental to modulating the material response. Our first-principles calculations suggest that g-GaN maintains electronic stability under intense electric fields; notably, gallium vacancies are predicted to further extend the theoretical stability limit. While in-plane tension preserves the band gap evolution under an electric field, in-plane compression facilitates low-field metallization. Using nitrogen monoxide (NO) adsorption as a prototype, we find that the interaction is defect-modulated and potentially tunable by electric fields. Analysis of adsorption energetics and diffusion barriers suggests that the gallium vacancy may act as a thermodynamic trap for NO. Targeted hybrid-functional (HSE06) validation confirms the reliability of observed adsorption trends and theoretical metallization thresholds while revealing that precise electronic-exchange treatment is critical for capturing the magnetic ground state of nitrogen vacancies. By systematically examining the geometry, energetics, band structure, density of states, magnetic response, and charge transfer, this study clarifies the interplay between defects and external electric fields, providing insights into theoretical upper bounds for property tuning and semiconductor device engineering.

This paper proposes and experimentally demonstrates a surface plasmon resonance (SPR)-based vector magnetic-field sensor using a U-shaped seven-core photonic crystal fiber (7C-PCF). The device employs a multimode fiber-7C-PCF-multimode fiber (MMF-7C-PCF-MMF) cascaded configuration, in which a gold film on the 7C-PCF forms the SPR-active region and a magnetic fluid serves as the magneto-sensitive medium, enabling magnetic-field-induced modulation of the SPR resonance wavelength. Finite-element simulations show that U-shaped bending enhances evanescent-field interaction at the gold interface, thereby strengthening the SPR response. Experimentally, the sensor achieves wavelength sensitivities of 3180 nm/RIU and 6680 nm/RIU over refractive-index ranges of 1.33-1.37 and 1.37-1.40, respectively. Magnetic-field measurements reveal a pronounced orientation-dependent magnetic field intensity response. In the weak-field regime (0-5 mT), the magnetic field intensity sensitivity reaches 10.3 nm/mT when the bending plane is perpendicular to the field (θ = 90°), but drops to 0.23 nm/mT when the sensor is aligned with the field (θ = 180°). For directional sensing, a maximum angular sensitivity of 0.5 nm/° is obtained within specific angular intervals. These results indicate that the proposed U-shaped 7C-PCF-SPR sensor enables high-sensitivity, direction-resolvable magnetic-field sensing in weak fields, offering a compact, fabrication-feasible platform for fiber-optic vector magnetometry.

The low signal-to-noise ratio (SNR) of existing magnetic sensors limits the detection of magnetic nanoparticles (MNPs) in microfluidic biosensing. We present a novel microfluidic coil-based impedance detection system for quantifying magnetic particles, including Fe filings and citrate-coated Fe3O4 MNPs, with potential applications in magnetically guided biosensing. Unlike conventional approaches that directly measure the magnetic properties of dispersed particles, our method employs an external collector magnet to concentrate particles within a copper coil detector. The accumulated particles alter the coil’s electromagnetic response through changes in the sample’s dielectric properties, producing an amplified impedance signal proportional to sample volume. We evaluated detection performance for 1–10 mg of ferromagnetic Fe filings and citrate-coated Fe3O4 MNPs across a broad frequency range. Results show a strong linear correlation between particle mass and impedance change, with SNR values from 25 dB to over 45 dB, demonstrating high sensitivity and precision. Coil sensitivity was further optimized by varying the number of turns (5, 10, and 15), enabling frequency-specific customization. This approach provides a scalable, low-cost platform adaptable to polymer-coated MNPs targeting biological analytes.

Although the ability to sense Earth's magnetic field is phylogenetically widespread, receptors for the magnetic sense have not been identified unequivocally in any animal. Because magnetic fields penetrate biological tissue, magnetoreceptors could hypothetically exist anywhere in the body. To investigate the location of magnetoreceptors in juvenile loggerhead sea turtles (Caretta caretta), we attached a weak magnet, which generated a magnetic disturbance over a small localized area, to three anatomical regions (head, mid-body and posterior). We then observed responses of turtles to magnetic map cues they had been conditioned to associate with food. Responses to the food-associated field decreased significantly when a magnet was attached to the head, but no such disruption occurred when the magnet was placed at the other locations. The results provide the first evidence for cephalic magnetoreceptors in turtles and provide a simple methodology that can be used to help localize receptors in other magnetoreceptive animals.