Search Results (284)

Background/Objectives: Magnetic nanoparticles have emerged as powerful tools for biomedical imaging, targeted drug delivery, and hyperthermia therapy. Magnetic particle imaging (MPI) is among the most promising technologies built around its properties: a radiation-free, quantitative tomographic modality that detects superparamagnetic iron oxide nanoparticles (SPIONs) directly against a biologically silent background. This review synthesizes MPI’s physical principles, nanoparticle design strategies, and preclinical applications within the broader landscape of magnetic material engineering for biomedical use. Methods: A systematic review was conducted covering MPI signal generation and image reconstruction, nanoparticle core synthesis and surface coating approaches, and preclinical applications, spanning cell tracking, oncological imaging, vascular perfusion, neuroimaging, and MPI-guided theranostics. Studies were selected to provide quantitative benchmarks and direct comparisons with competing modalities where available. Results: MPI delivers signal-to-background ratios above 1000:1, iron-mass linearity at R² ≥ 0.99, regardless of tissue depth, and acquisition rates up to 46 volumes per second. Tracer architecture—encompassing single-core particles, multicore nanoflowers, and stimuli-responsive cluster designs—is the primary determinant of sensitivity, environmental robustness, and theranostic capability. Preclinical results include detection of cell populations in the low thousands, earlier ischaemia identification than diffusion-weighted MRI, real-time drug release quantification, and spatially confined tumour hyperthermia. Three translational bottlenecks are identified: the absence of a clinically approved tracer with optimal relaxation dynamics, hardware performance losses when scaling to human-bore systems, and overestimation of passive tumour accumulation in murine models. Conclusions: MPI illustrates how progress in magnetic material design directly expands clinical imaging and theranostic possibilities. Successful translation will require indication-driven, interdisciplinary development that integrates materials science, scanner engineering, and regulatory strategy in parallel. Full article

This study proposes a heterogeneous fusion path planning framework for unmanned aerial vehicles (UAVs) operating in complex emergency rescue and civil environments. Existing single-mechanism metaheuristics—including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithms (GAs)—suffer from fundamental limitations in three-dimensional kinematic path planning: PSO converges rapidly but stagnates at local optima due to population variance collapse; ACO offers robust local exploitation but incurs prohibitive cold-start overhead; GAs maintain diversity at the cost of expensive crossover operations. To address these complementary deficiencies simultaneously, the proposed framework introduces a spherical coordinate representation that reduces computational complexity and naturally enforces UAV kinematic constraints, combined with adaptive weight factors and a serial PSO-ACO fusion strategy, and subsequently incorporates adaptive weight factors. A serial fusion strategy is then introduced, wherein the sub-optimal trajectory generated by the Spherical PSO phase is mapped into the ACO pheromone field via a Gaussian Kernel Density Mapping (GKDM) mechanism, enabling the ACO phase to perform fine-grained local exploitation within a kinematically feasible corridor. Various constraints along the flight path are formulated into distinct cost functions, which cover aircraft track length, pitch angle variation, altitude difference variation, obstacle avoidance, and smoothness; the core task of the algorithm is to find the flight path with the minimum total cost. The proposed algorithm is dedicated to UAV path planning in complex emergency rescue environments (disaster-stricken areas, hazardous zones) and is further applicable to civil low-altitude logistics delivery, industrial facility inspection, ecological environment monitoring and urban air mobility (UAM) scenarios with complex obstacle constraints. It can effectively improve the safety and efficiency of UAVs in reaching rescue points, delivering emergency supplies, conducting disaster surveys, and completing various civil low-altitude operation tasks. Full article

This article addresses a current point of contention in the field of single-molecule/single-particle tracking, as well as the relevant literature, and supplements it with some published cell-based experiments to illustrate our conclusions and known theorems. We attempt to explain the controversy surrounding the differing biophysical and cell biological results of studies on the individual molecule and those “at the single-molecule level” as well as at the level of many molecules in such a way that even readers who are unfamiliar with the subject can understand it without having to read all the mathematical, physical, and biophysical references. Given this abundance of studies in the literature, it is obvious that genuine single-molecule studies are urgently needed, i.e., single-molecule studies that focus on increasing the sensitivity of the temporal resolution of single-molecule measurements and not just on spatial resolution. Full article

Indoor exposure to particulate matter (PM) depends on ventilation-driven transport, yet sensor placement in real rooms is often based on limited point data. This study develops and experimentally validates a transient CFD framework, using RANS airflow coupled with Lagrangian discrete phase tracking, to map PM_2.5 and PM₁₀ in a full-scale 2.0 × 3.0 × 2.5 m bedroom with a fixed, non-oscillating pedestal fan and an open window. Airflow was verified by grid independence and validated against 10-point velocity measurements (RMSE = 0.108 m·s⁻¹). Incense experiments (≈31 min burn) provided PM time series over the first 60 min at 16 locations on two heights; emission rate, burning time, and air-change rate (1.96–5.39 ACH) were calibrated so that accepted models achieved aggregate R² > 0.90. Spatial mapping on a 0.5 m grid shows that PM behavior is governed primarily by airflow-defined accumulation pockets rather than by source proximity alone. A near-source region consistently captured strong early-time peaks, whereas remote low-exchange pockets remained elevated during the decay phase. For PM_2.5, the most persistent hotspot is a ceiling-adjacent recirculation pocket, while for PM₁₀, gravitational settling shifted the dominant hotspots toward floor-layer, low-velocity regions. An exposure score combining normalized peak and time-averaged concentrations, interpreted together with particle-track persistence metrics, distinguished transiently traversed regions from true retention pockets. The results show that sensor placement should follow the monitoring objective: near-source regions are more responsive to peak events, ceiling pockets are more suitable for persistent PM_2.5 monitoring, and floor hotspots are more critical for PM₁₀. No single fixed sensor location adequately represents both particle sizes in the present bedroom and ventilation configuration. Full article

Ocean turbulence, a fundamental process influencing marine hydrodynamics, holds significant guiding implications for the development of multiple disciplines and has emerged as a research hotspot in ocean science in recent years. However, constrained by traditional oceanographic instruments limited to single-point measurements, current observations and analyses of oceanic turbulence still experience considerable shortcomings. To advance oceanic turbulence observations beyond single-point measurements toward comprehensive three-dimensional (3D) field characterization, this study introduces an innovative Holographic Astigmatic Particle Tracking Velocimetry (HAPTV) technology combined with an integrated in situ underwater measurement and processing system. For the first time, this system has successfully acquired 3D two-component (u, v components) ocean flow fields in natural environments. The measured flow velocities reach up to 15 cm/s, with turbulence dissipation rates on the order of 10⁻⁴ m²/s³, which is consistent with the hydrodynamic conditions in coastal marine environments. These results demonstrate the feasibility of using HAPTV for field-scale turbulence observations, offering a novel volumetric alternative to conventional single-point techniques. Nevertheless, due to factors such as excessively high concentrations of suspended matter in nearshore waters and z-axis positioning limitations, the accuracy of the flow field results obtained from the initial sea trials still needs to be improved. Full article

High-speed solid particles ejected during battery thermal runaway pose severe safety threats, yet their velocity measurement is hindered by high density, microscopic size, and intense glare. This study proposes a non-intrusive velocimetry framework that integrates an enhanced single-stage object detector with a structural similarity matching algorithm. The detector incorporates specialized feature extraction modules and a high-resolution layer to identify microscopic targets in extreme environments, while the matching algorithm employs adaptive direction constraints to ensure precise trajectory tracking. Experimental validation demonstrates that the framework achieves a mean average precision of 92.7% and supports real-time processing. The method successfully quantifies a three-stage velocity evolution in battery failure events, identifying a peak particle speed exceeding 120 m/s. These findings provide critical kinematic data for optimizing battery safety structures and modeling fire propagation mechanisms. Full article

This paper presents a novel bio-inspired fluid dynamics framework that unifies path planning and control within a single continuous navigation process. Unlike conventional approaches that separate trajectory generation and execution, the proposed method models the robot as a particle immersed in an artificial fluid field, where the goal acts as a sink and obstacles modify the flow to produce collision-free motion. To ensure global optimality and eliminate local minima traps, the framework incorporates a sampling-based enhancement that evaluates multiple trajectories within high-flow regions and selects the optimal path using graph-based optimization. A fluid-based control law directly converts the velocity field into robot motion commands, enabling seamless integration between planning and execution. Theoretical stability is established using Lyapunov analysis, guaranteeing convergence to the goal. Extensive experiments on a Pioneer P3-DX robot demonstrate that the proposed approach achieves execution speeds 1.5 to 9.7 times faster than A*, PRM, and RRT*, while producing paths 3.6% to 29.5% shorter. Furthermore, the unified framework provides smooth and accurate motion with tracking errors within ±0.1 m. These results confirm that the proposed method improves path quality, computational efficiency, and real-time navigation performance. Full article

Laser metal deposition operates on the principle of layer-by-layer material addition, wherein each layer is formed by overlapping individual single tracks. Consequently, clads formed serve as the fundamental building blocks for this technology. Their quality directly affects the overall build quality, particularly the geometric characteristics, which are also critical to process productivity. In the present work, geometric characteristics of TiC/Ti6Al4V single tracks fabricated via laser metal deposition are optimised. An artificial neural network model was developed to predict the clad width, height, and dilution using processing parameters, laser power, scan speed, and powder feed rate, as model inputs. The Particle Swarm Optimisation algorithm was employed for hyperparameter selection. The hyperparameter-optimised model achieved a mean squared error of 0.00183 and an R² score of 0.979 during training, and a mean squared error of 0.00709 and an R² score of 0.887 during testing. Although the small discrepancy between training and testing metrics suggests slight overfitting, likely due to the size of the dataset, the model achieved a mean absolute percentage error of less than 10% during testing. Subsequently, process plots generated by the model predictions were used to identify suitable parameters, and a processing map was developed to highlight the window that achieves suitable dilution (14–24%), defect-free sound bonding, and thick and dense clads. Full article

Intracellular transport is essential for cellular organization and function. This process is driven by molecular motors that ferry cargo along microtubules, but is characterized by intermittent motility, where cargoes switch between directed runs and prolonged pauses. The fundamental nature of these pauses has remained a mystery, specifically whether they are periods of motor detachment and passive drifting or states of active motor engagement. By combining single-particle tracking with large-scale motion analysis, we discovered that pauses are not passive. Instead, they are active, motor-driven states. We uncovered a unifying quantitative law: the diffusivity of a vesicle during a pause scales with the square of its velocity during a run. This parabolic relationship, D_eff ∝ v², holds true for both kinesin and dynein motors, different cargo types, and a variety of cellular perturbations. We show that this coupling arises because the number of engaged motors governs motility in both states. When we reduce motor engagement, vesicles move more slowly and become trapped in longer, less mobile pauses, collectively causing them to fail to reach their destination. Our work redefines transport pauses as an essential, motor-driven part of microtubule-based cargo delivery, revealing a quantitative principle that contributes to robust cargo transport through the crowded cellular environment. Full article

Silicon nitride (Si₃N₄) ceramic bearings inevitably contain crack-like defects, yet their compressive capacity degradation and crack-driven failure mechanisms remain unclear. This study proposes a discrete element method (DEM) numerical framework within PFC2D to simulate a bearing containing a single prefabricated crack. First, a bearing DEM model was established and calibrated to reproduce the compressive mechanical response. Then, particle deletion introduced controllable central cracks in the ball and raceway with prescribed inclination angles. Finally, displacement-controlled compression-splitting simulations, serving as a surrogate for a quasi-static overload scenario relevant to quality screening, tracked crack initiation, propagation, and failure modes; under a fixed raceway-crack inclination, crack length was varied to quantify size effects. Results show that a single crack markedly reduces compressive strength. Failure progresses through elastic deformation, crack propagation, and final fracture, with cracks initiating at stress concentrators near crack tips. Crack inclination significantly regulates capacity: raceway cracks are most detrimental near 45°, while ball cracks exhibit an overall decrease in initiation and peak stresses with increasing inclination (with local non-monotonicity). Crack length has a stronger weakening effect than inclination, with accelerated capacity loss beyond 0.3 mm and a pronounced drop in initiation stress beyond 0.6 mm. The framework enables controllable defect parametrization and micro-scale failure interpretation for defect sensitivity assessment under compressive overload. Thus, this study focuses on simulating monotonic fracture events to elucidate fundamental defect–property relationships, which provides a foundation distinct from the prediction of rolling contact fatigue life under cyclic service conditions. Full article

Recombinant adeno-associated viral vectors (rAAVs) are the leading gene delivery vehicles in clinical development, yet efficient nuclear delivery remains a major barrier to effective transduction. This limitation is partly due to the incomplete understanding of rAAV’s complex subcellular trafficking dynamics. Here, we establish a longitudinal confocal live-cell imaging workflow that tracks rAAV2 from 4 to 12 h post-transduction, paired with an automated 3D analysis pipeline that quantifies spatiotemporal vector distribution, cytoplasmic trafficking, nuclear accumulation, and transgene expression at single-cell resolution. We use this platform to evaluate the effects of vector dose, cell cycle progression, and the behavior of empty particles. We identify previously undescribed trafficking features associated with high transgene expression. Higher rAAV2 doses enhanced cytoplasmic trafficking and nuclear delivery, while cell cycle progression facilitated both trafficking efficiency and transgene expression. We also characterize empty rAAV2 particles, revealing distinct trafficking patterns and markedly reduced nuclear accumulation compared to genome-containing vectors. By uncovering new bottlenecks in rAAV transduction, this platform provides mechanistic insights and potential strategies to improve AAV-based gene therapy. Its generalizable design further supports broad applicability to other non-enveloped viruses. Full article

Cu₂ZnSnSe₄ is a promising light-absorbing material for cost-effective and eco-friendly thin-film solar cells; however, its synthesis often leads to secondary phases that limit device efficiency. To overcome these challenges, we devised a straightforward and efficient method to obtain single-phase Cu₂ZnSnSe₄ nanocrystalline powders directly from the elements Cu, Zn, Sn, and Se via mechanochemical synthesis followed by vacuum annealing at 450 °C. Phase evolution monitored by X-ray diffraction (XRD) and Raman spectroscopy at two-hour milling intervals confirmed the formation of phase-pure kesterite Cu₂ZnSnSe₄ and enabled tracking of transient secondary phases. Raman spectra revealed the characteristic A₁ vibrational modes of the kesterite structure, while XRD peaks and Rietveld refinement (χ² ~ 1) validated single-phase formation with crystallite sizes of 10–15 nm and dislocation densities of 3.00–3.20 10¹⁵ lines/m². Optical analysis showed a direct bandgap of ~1.1 eV, and estimated linear and nonlinear optical constants validate its potential for photovoltaic applications. Scanning electron microscopy (SEM) analysis showed uniformly distributed particles 50–60 nm, and energy dispersive X-ray (EDS) analysis confirmed a near-stoichiometric Cu:Zn:Sn:Se ratio of 2:1:1:4. X-ray photoelectron spectroscopy (XPS) identified the expected oxidation states (Cu⁺, Zn²⁺, Sn⁴⁺, and Se²⁻). Electrical characterization revealed p-type conductivity with a mobility (μ) of 2.09 cm²/Vs, sheet resistance (ρ) of 4.87 Ω cm, and carrier concentrations of 1.23 × 10¹⁹ cm⁻³. Galvanostatic charge–discharge testing (GCD) demonstrated an energy density of 2.872 Wh/kg⁻¹ and a power density of 1083 W kg⁻¹, highlighting the material’s additional potential for energy storage applications. Full article

To address the triple challenges of data sparsity, highly nonlinear dynamics, and maneuver uncertainty in tracking non-cooperative targets in cislunar space, we propose a collaborative framework combining Particle Filter (PF) and Unscented Kalman Filter (UKF). This framework optimizes search efficiency through a two-phase strategy: in the search phase, PF constructs the target reachable domain and leverages undetected information to dynamically shrink the search scope; upon target detection, the framework switches to UKF for high-precision and low-overhead tracking. To overcome the computational bottleneck in high-dimensional reachable domain integration, we integrate a non-product-type Designed Quadrature (DQ) method—one that generates minimal quadrature point sets to replace traditional Monte Carlo sampling by matching the moment conditions of mixed distributions via Gauss–Newton optimization. Distinct from existing single-filter or reachability modeling approaches, the key novelties of this work lie in a two-phase PF-UKF switching framework tailored to the unique cislunar environment resolving the trade-off between search capability and computational efficiency and integration of the non-product DQ method to break the dimensionality curse in high-dimensional reachable domain computation ensuring both moment-matching accuracy and real-time performance. This work holds potential to support space domain awareness and cislunar mission safety: reliable tracking of non-cooperative targets is a key prerequisite for avoiding collisions, safeguarding space assets, and enabling effective space defense, and the proposed framework provides a feasible technical path for this goal through simulation validation. Simulations demonstrate that on a three-dimensional Distant Retrograde Orbit (DRO) observation platform, successful recapture of cislunar transfer orbit targets can be achieved. Under fifth-order accuracy conditions, the system exhibits a position error of $3.745\times {10}^{-1}\mathrm{km}$ and a velocity tracking error of $9.703\times {10}^{-3}\mathrm{m}/\mathrm{s}$ for target search-and-tracking tasks, with a system response time of 1.8343 h. Compared with the traditional PF + numerical integration method, our proposed PF-UKF framework achieves an 86.7% reduction in time cost and a 24.1% reduction in position error. Full article

The X and Gamma Imager and Spectrometer (XGIS) on board THESEUS is a finely pixelized and modular instrument designed for broadband high-energy transient detection. XGIS consists of two cameras, each composed of 10 supermodules, with each supermodule further divided into 10 modules and each module made with 64 independent readout pixels based on Silicon Drift Detectors coupled with 5 × 5 × 30 mm³ CsI scintillator bars. An algorithm to quickly read out the signals from the 64 pixels and send them in chronological order through the module and supermodule logic up to the camera logic is under development. Furthermore, a challenge for space-based high-energy instruments is distinguishing X-/gamma-ray photons while effectively rejecting background photons and particles, including electrons, protons, and heavier cosmic rays. Unlike traditional systems that rely on anticoincidence systems, XGIS aims to achieve background rejection through an innovative readout logic that analyzes the spatial and temporal properties of energy deposits in the detector. By leveraging the finely pixelized structure, the readout system can differentiate single-photon events from charged-particle tracks based on energy deposition patterns and event topology. Full article

This study systematically benchmarks the performance of four single soft magnetic powders—water-atomized Fe–Si–Cr (FeSiCr), silica-coated reduced iron powder (RIP), silica-coated carbonyl iron powder (CIP), and phosphate-coated CIP (CIP-P)—to establish quantitative relationships between powder attributes, deformation substructure, and high-frequency loss for molded power inductors (100 kHz–1 MHz). We prepared toroidal compacts at 200 MPa and characterized them by initial permeability (μ_i), core-loss (P_cv(f)), partitioning (P_cv(f) = K_hf + K_ef², K_h, $K_{\mathrm{e}}$: hysteresis and eddy-current loss coefficients), and EBSD (electron backscatter diffraction)-derived microstrain metrics (Kernel Average Misorientation, KAM; low-/high-angle grain-boundary fractions). Corrosion robustness was assessed using a 5 wt% NaCl, 35 °C, 24 h salt-spray protocol. Our findings reveal that FeSiCr achieves the highest μ_i across the frequency band, despite its lowest compaction density. This is attributed to its coarse particle size (D₅₀ ≈ 18 µm) and the resulting lower intragranular pinning. The loss spectra are dominated by hysteresis over this frequency range, with FeSiCr exhibiting the largest K_h, while the fine, silica-insulated Fe powders (RIP/CIP) most effectively suppress K_e. EBSD analysis shows that the high coercivity and hysteresis loss in CIP (and, to a lesser extent, RIP) are correlated with dense, deformation-induced subgrain networks, as evidenced by higher mean KAM and a lower low-angle grain boundary fraction. In contrast, FeSiCr exhibits the lowest KAM, with strain confined primarily to particle contact regions. Corrosion testing ranked durability as FeSiCr ≳ CIP ≈ RIP ≫ CIP-P, which is consistent with the Cr-rich passivation of FeSiCr and the superior barrier properties of the SiO₂ shells compared to low-dose phosphate. At 15 A, inductance retention ranks CIP (67.9%) > RIP (55.7%) > CIP-P (48.8%) > FeSiCr (33.2%), tracking a rise in effective anisotropy and—for FeSiCr—lower M_s that precipitate earlier roll-off. Collectively, these results provide a microstructure-informed selection map for single-powder formulations. We demonstrate that particle size and shell chemistry are the primary factors governing eddy currents (K_e), while the KAM-indexed substructure dictates hysteresis loss (K_h) and DC-bias superposition characteristics. This framework enables rational trade-offs between magnetic permeability, core loss, and environmental durability. Full article