Search Results (1,914)

The ligand-driven self-assembly of metal clusters offers a powerful strategy for constructing discrete molecular architectures with tunable magnetic and structural properties. By judiciously selecting appropriate multidentate ligands, researchers can direct the formation of polynuclear metal assemblies with diverse nuclearities, geometries, and topologies. Coordination-driven processes commonly stabilize such assemblies where multidentate ligands operate as templates and linkers. These will also determine how the metal centers are arranged in space and how they connect to each other. These clusters can take on shapes that range from basic bridging dimers to more complicated icosahedral and cubane-type motifs. They often have excellent symmetry and strong frameworks. Magnetically, these clusters are a great place to study exchange interactions, spin frustration, and the behavior of single-molecule magnets (SMMs). The magnetic characteristics depend on things like the type of metal ions, the bridging ligands, the overall shape, and the local coordination environment. Interestingly, a large number of ligand-assembled clusters exhibit high spin ground states and slow magnetization relaxation, which makes them attractive options for quantum information storage and molecular spintronic devices. This review connects coordination chemistry, supramolecular design, and molecular magnetism of pyridine–amine–carboxylate frameworks, offering insights into fundamental magnetic phenomena and guiding the development of next-generation functional materials. Continued exploration of ligand frameworks and metal combinations holds the potential to yield novel clusters with enhanced or unprecedented magnetic characteristics. Full article

Performance diagnostics in alpine skiing increasingly rely on integrated biomechanical and kinematic assessments to support technique optimization under real training conditions; however, many existing approaches address trajectory geometry or biomechanical variables separately, limiting their explanatory power. This study evaluates an integrated analysis framework combining OptiPath, an AI-assisted video-based trajectory analysis tool, with XSensDOT wearable inertial sensors to identify technical inefficiencies during giant slalom skiing. Thirty competitive youth athletes (n = 30; 14–16 years) performed controlled runs with predefined lateral offsets from the gates, enabling systematic examination of the relationship between spatial trajectory deviations, biomechanical execution, and performance outcomes. Skier trajectories were extracted using computer vision-based methods, while lower-limb kinematics, trunk motion, and tri-axial acceleration were recorded using inertial measurement units. Deviations from mathematically defined ideal trajectories were quantified through regression-based calibration and arc-based modeling. The results show that although OptiPath reliably detected trajectory variations, shorter skiing paths did not consistently produce faster run times. Instead, superior performance was associated with more efficient biomechanical execution, reflected by coordinated trunk–lower limb motion, controlled vertical loading, reduced lateral corrections, and higher forward acceleration, even when longer trajectories were followed. These findings indicate that trajectory geometry alone is insufficient to explain performance outcomes and support the integration of wearable biomechanics with trajectory modeling as a practical, low-cost, and field-deployable tool for alpine skiing performance diagnostics. Full article

Droplet-based microfluidics has emerged as a powerful platform for precise fluid manipulation in biomedical, chemical, and material science applications. Among various geometries, T-junction microchannels are widely utilized for droplet generation and splitting due to their simplicity and reliability. This review provides a comprehensive overview of droplet splitting mechanisms in T-junction microfluidic systems, with particular emphasis on the role of actuation methods in enhancing control and functionality. We first discuss the fundamental physics governing droplet behavior, including the influence of capillary and viscous forces, flow regimes, and geometric parameters. Passive strategies based on flow rate tuning and channel design are outlined, followed by an in-depth examination of active actuation techniques: thermal, electrical, magnetic, acoustic, and pneumatic and their effects on droplet dynamics. In addition, the review highlights computational modeling approaches and experimental tools used to characterize and predict splitting behavior. Finally, we explore the current challenges and future directions in integrating multifunctional actuation systems for real-time, programmable droplet control in lab-on-a-chip platforms. This article serves as a foundational resource for researchers aiming to advance microfluidic droplet manipulation through actuator-enabled strategies. Full article

Floating-base robotic systems rely critically on their inertial geometry to maintain stability and regulate interaction forces in the absence of fixed ground constraints. Their control authority additionally depends on the placement and orientation of actuators relative to the center of mass, which determines the moment arms through which thrust or force inputs generate stabilizing actions. This paper develops a general theoretical framework showing that internal mass shifting provides a powerful, domain-independent mechanism for reshaping global system dynamics. Through geometric principles governing center-of-mass placement, moment-arm modification, and inertia redistribution, mass shifting enhances passive stability, reduces the torque induced by external disturbances, and improves the controllability of interaction-intensive tasks. The theory is first examined in a buoyancy-driven simulation of a two-mass floating body subjected to multi-sine wave excitation, which isolates the hydrostatic effects of center-of-mass displacement. To validate the generality of these principles, we further demonstrate their applicability in a radically different domain through real-world experiments on the AeroBull aerial robot, a multirotor platform equipped with an internal mass-shifting mechanism for aerial manipulation. Across both aquatic and aerial settings, mass shifting consistently improves stability, reduces control effort, and increases achievable interaction forces. These results establish internal mass redistribution as a platform-agnostic strategy for enhancing the stability and resilience of floating-base robots operating in uncertain and physically demanding environments. Full article

The equilibrium shape of a crystal is a fundamental problem in materials science and condensed matter physics. The Wulff construction, a cornerstone of crystal morphology prediction, is traditionally presented and utilized as a powerful geometric algorithm to derive equilibrium shapes from anisotropic surface energy $\gamma \left(\mathbf{n}\right)$. While its application across materials science is vast, the profound mathematical physics underpinning it, specifically its intrinsic identity as a manifestation of the Legendre transform, is often relegated to a passing remark. This work recenters the focus on this fundamental duality. We present a comprehensive, step-by-step derivation of the Wulff shape from the variational principle of surface energy minimization under a constant volume, employing the language of support functions and differential geometry. We then rigorously demonstrate that the equilibrium shape, defined by the support function $h\left(\mathbf{n}\right)$, and the surface energy density $\gamma \left(\mathbf{n}\right)$ are conjugate variables linked by a Legendre transformation; the Wulff shape $\mathcal{W}$ is precisely the zero-sublevel set of the dual function ${\gamma }^{*}\left(\mathbf{x}\right)={sup}_{\mathbf{n}}[\mathbf{x}·\mathbf{n}-\gamma \left(\mathbf{n}\right)]$. This perspective elevates the Wulff construction from a mere graphical tool to a canonical example of convex duality in thermodynamic systems, connecting it to deeper principles in convex analysis and statistical mechanics. To bridge theory and computation, we provide a robust computational algorithm implemented in pseudocode capable of generating Wulff shapes for two-dimensional (2D) crystals with arbitrary N-fold symmetry. Finally, we discuss the relevance and extensions of the classical theory in contemporary research, including non-equilibrium growth, nanoscale effects, and machine learning approaches. Full article

In vitro models of the human nasal cavity are crucial for understanding the deposition dynamics of nasally administered drugs. Three-dimensional (3D) printing offers a powerful method for creating patient-specific, anatomically precise models for such experimental purposes. Background/Objectives: This study details the complete workflow for the development, design, and fabrication of a sectioned nasal cavity model intended for droplet deposition analysis of nasal sprays. Methods: A digital nasal cavity model was derived from medical imaging data and optimized for computer-aided design (CAD) operations. It was segmented into five therapeutically relevant regions: nasal vestibule, olfactory area, middle and upper turbinates, lower turbinate, and nasopharynx. Sections were 3D-printed in polypropylene for chemical compatibility, and a carbon fiber-reinforced fixation frame ensured precise alignment and airtight assembly. Results: Functional validation confirmed the model’s functional relevance through comparative deposition studies using automated actuation and high-performance liquid chromatography (HPLC) based regional quantification. Two devices with distinct spray characteristics (characterized separately by laser diffraction, plume geometry, and spray pattern imaging) were tested under varied administration conditions. The study demonstrated the model’s ability to discriminate between products, establishing a solid foundation for future investigations incorporating additional variables. Conclusions: Overall, the developed methodology provides a cost-effective and replicable platform for producing anatomically accurate, sectioned nasal cavity models. The newly developed in vitro system is well suited for detailed, region-specific analysis of nasal spray deposition, offering a valuable tool for pharmaceutical research and development. Full article

This research presents the design, numerical analysis, and experimental validation of a compact radial turbine intended for mini-turbocharger applications in UAV power systems. To meet the stringent requirements of UAV propulsion—such as lightweight construction, high efficiency at small scales, and stable performance across varying operating altitudes—a test rig was constructed to experimentally estimate turbine torque and shaft power across selected operating conditions. Complementary CFD simulations were performed to evaluate aerodynamic behavior, including flow distribution, torque generation, and power output at multiple rotational speeds matched to experimental mass-flow rates. Additional high-speed CFD simulations were conducted to predict turbine performance in operational regimes typical of UAV engines, where experimental testing is challenging. The combined CFD–experimental methodology provides accurate performance prediction for micro-scale radial turbines across different volute geometries and operating conditions. The results contribute essential insights for the development of next-generation miniaturized turbochargers aimed at enhancing UAV engine efficiency, high-altitude capability, and overall flight endurance. Full article

This paper presents a comprehensive experimental and numerical investigation of the aerodynamics of a vertical-axis Darrieus wind turbine equipped with newly developed modified asymmetric blades intended to enhance performance at low and variable wind speeds. Using URANS modeling (SST k–ω) combined with full-scale testing, a detailed comparison was carried out against the classical NACA 0021 airfoil. The results show that the asymmetric profile increases starting torque by 30–40%, reduces negative torque by 20–25%, and decreases load pulsations by 15–20%, owing to the delayed onset of dynamic stall and the stabilization of the vortex wake structure. Within the optimal operating range of TSR = 2.5–4, an 18–22% increase in pressure differential is observed, resulting in a higher power coefficient; the maximum Cp reaches 0.15, exceeding that of the symmetric configuration by 20–25%. The agreement between CFD predictions and experimental measurements exceeds 95%, confirming the robustness of the numerical model employed. The findings clearly demonstrate the substantial effectiveness of the proposed blade geometry and its strong potential for next-generation VAWTs optimized for regions with low wind resources. Full article

Lumbar shear forces are increasingly recognized as critical contributors to lower-back injury risk, yet most ergonomic assessment tools—most notably the Revised NIOSH Lifting Equation (RNLE)—do not directly estimate shear loading. This study develops and evaluates a family of linear mixed-effects regression models that statistically predict L4/L5 lumbar shear force exposure using traditional NIOSH lifting parameters combined with posture descriptors extracted from digital human models. A harmonized dataset of 106 peak-shear lifting postures was compiled from five controlled laboratory studies, with lumbar shear forces obtained from validated biomechanical simulations implemented in the Siemens JACK (Siemens software, Plano, TX, USA) platform. Twelve model formulations were examined, varying in fixed-effect structure and hierarchical random effects, to quantify how load magnitude, hand location, sex, and joint posture relate to simulated task-level anterior–posterior shear exposure at the lumbar spine. Across all models, load magnitude and horizontal reach emerged as the strongest and most stable predictors of shear exposure, reflecting their direct mechanical influence on anterior spinal loading. Hip and knee flexion provided substantial additional explanatory power, highlighting the role of whole-body posture strategy in modulating shear demand. Upper-limb posture and coupling quality exhibited minimal or inconsistent effects once load geometry and lower-body posture were accounted for. Random-effects analyses demonstrated that meaningful variability arises from individual movement strategies and task conditions, underscoring the necessity of mixed-effects modeling for representing hierarchical structure in lifting data. Parsimonious models incorporating subject-level random intercepts produced the most stable and interpretable coefficients while maintaining strong goodness-of-fit. Overall, the findings extend the NIOSH framework by identifying posture-dependent determinants of lumbar shear exposure and by demonstrating that simulated shear loading can be reliably predicted using ergonomically accessible task descriptors. The proposed models are intended as statistical predictors of task-level shear exposure that complement—rather than replace—comprehensive biomechanical simulations. This work provides a quantitative foundation for integrating shear-aware metrics into ergonomic risk assessment practices, supporting posture-informed screening of manual material-handling tasks in field and sensor-based applications. Full article

Underwater localization using airborne visible light beams offers a promising alternative to acoustic and radio-frequency methods, yet accurate modeling of light propagation through a dynamic air–water interface remains a major challenge. This paper introduces a physics-informed machine learning framework that combines geometric optics with neural network inference to localize submerged optical nodes under both flat and wavy surface conditions. The approach integrates ray-based light transmission modeling with a third-order Stokes wave formulation, enabling a realistic representation of nonlinear surface slopes and their effect on refraction. A multilayer perceptron (MLP) is trained on synthetic intensity–position datasets generated from this model, learning the complex mapping between received optical power (light intensity) and coordinates of the submerged receiver. The proposed method demonstrates high precision, stability, and adaptability across varying geometries and surface dynamics, offering a computationally efficient solution for optical localization in dynamic underwater environments. Full article

To address the severe surface imperfections induced during ultrafast pulsed laser fabrication of fused silica microfluidic chips, a high-precision CO₂ laser polishing strategy based on shallow-layer melting and reflow was employed. This method enables localized melting within an extremely thin surface layer, effectively smoothing the topography without altering the original microstructure geometry. An L9(3³) orthogonal experimental design was conducted to systematically investigate the influence of key parameters on polishing quality, identifying defocus distance as the dominant factor affecting surface roughness, followed by scanning speed and laser power. The optimal parameter combination was determined to be a laser power of 8 W, a defocus distance of 6 mm, and a scanning speed of 5 mm/s. Furthermore, an overlap rate between 38% and 63% was found to ensure sufficient fusion without excessive remelting, with the minimum surface roughness of 0.157 µm achieved at a 50% overlap rate. Based on the optimized parameters, adaptive scanning paths were designed for different functional units of a fused silica microfluidic chip. Surface characterization demonstrated that the surface roughness was remarkably reduced from 303 nm to 0.33 nm, meeting optical-grade surface quality requirements. Full article

Carbon fiber reinforced polymers (CFRPs) are widely used in aerospace and wind power applications, but the complex failure mechanisms of their connection structures pose challenges for connection design. This study aims to investigate the influence of bonding interface inclination angle and connection method on the failure mechanisms of CFRP joints under bending loads. The study investigated two design parameters: the joint geometry of the bonding interface (single-slope, transition-slope, and single-step) and the connection methods (bonding, bolting, and hybrid bonding–bolting). Finite element simulations analyzed the mechanical performance and failure modes under different design parameters. Bending tests validated the mechanical properties of the joint interface, validating the effectiveness of the numerical simulation. The study found that under bonded connections, the bending load increased with the slope of the connection interface, with improvements of 21.87% and 39.75%, respectively. The main reason is stress concentration caused by sharp geometric discontinuities. The hybrid connection had the highest peak load, with improvements of 38.38% and 43.91% compared to the other connection methods. Hybrid bonding–bolting connections further optimized structural performance and damage tolerance. This study reveals the damage mechanisms of the bonding interface and provides a reliable prediction method for aerospace and wind turbine blade applications. Full article

Prolonged tooth loss causes alveolar ridge atrophy, complicating implantation, especially in patients with impaired mineral metabolism. This study aimed to develop a personalized titanium mesh for guided bone regeneration and qualitatively evaluate its local tissue response in a vitamin D3-deficient rabbit model. A titanium mesh design has been developed in the form of a plate-shaped profile frame of a truncated pyramid with a solid upper base and perforated side faces. For testing in a rabbit model with vitamin D3 deficiency, a bone defect was created and repaired in the mandible using hydroxyapatite, an individual titanium mesh and a collagen membrane. Histological analysis was performed in the Laboratory of Digital Microscopic Analysis. The optimized geometry and parameters of the mesh openings contributed to effective vascularization and osteogenesis. In the postoperative period (3, 5 and 7 days), moderate edema and hyperemia were noted with their complete leveling by the 7th day (p < 0.05). According to the histological examination, 3 months after the installation of the titanium mesh, the formation of dense connective tissue with signs of active osteogenesis was observed in the defect area, including zones of mineralized bone trabeculae, osteocytes and osteon elements. The findings of this study indicate acceptable biocompatibility of the developed titanium structure and suggest osteoconductive potential, which, however, needs to be confirmed in controlled, quantitatively powered studies. Full article

Deep learning has emerged as a transformative methodology in modern cosmology, providing powerful tools to extract meaningful physical information from complex astronomical data. This paper implements a novel Bayesian graph deep learning framework for estimating key cosmological parameters in a primordial magnetic field (PMF) cosmology from simulated Cosmic Microwave Background (CMB) maps. Our methodology utilizes DeepSphere, a spherical convolutional neural network architecture specifically designed to respect the spherical geometry of CMB data through HEALPix pixelization. To advance beyond deterministic point estimates and enable robust uncertainty quantification, we integrate Bayesian Neural Networks (BNNs) into the framework, capturing aleatoric and epistemic uncertainties that reflect the model confidence in its predictions. The proposed approach demonstrates exceptional performance, achieving $R^2$ scores exceeding 89% for the magnetic parameter estimation. We further obtain well-calibrated uncertainty estimates through post hoc training techniques including Variance Scaling and GPNormal. This integrated DeepSphere-BNNs framework delivers accurate parameter estimation from CMB maps with PMF contributions while providing reliable uncertainty quantification, enabling robust cosmological inference in the era of precision cosmology. Full article

The integration of complementary metal–oxide–semiconductor (CMOS) and micro-electromechanical systems (MEMSs) technologies for miniaturized biosensor fabrication enables unprecedented spatiotemporal resolution in monitoring the bioelectrical activity of the nervous system. Wafer-level CMOS technology incurs high costs, but multi-project wafer (MPW) runs mitigate this by allowing multiple users to share a single wafer. Still, monolithic CMOS biosensors require specialized surface materials or device geometries incompatible with standard CMOS processes. Performing MEMS post-processing on the few square millimeters available in MPW dies remains a significant challenge. In this paper, we present a MEMS post-processing workflow tailored for CMOS dies that supports both surface material modification and layout shaping for intracortical biosensing applications. To address lithographic limitations on small substrates, we optimized spray-coating photolithography methods that suppress edge effects and enable reliable patterning and lift-off of diverse materials. We fabricated a needle-like, 512-channel simultaneous neural recording active pixel sensor (SiNAPS) technology based neural probe designed for integration with optical fibers for optogenetic studies. To mitigate photoelectric effects induced by light stimulation, we incorporated a photoelectric shield through simple modifications to the photolithography mask. Optical bench testing demonstrated >96% light-shielding effectiveness at 3 mW of light power applied directly to the probe electrodes. In vivo experiments confirmed the probe’s capability for high-resolution electrophysiological measurements. Full article