Search Results (1,390)

Organoid and organ-on-chip technologies are rapidly evolving platforms for viral research that integrate stem cell biology, tissue engineering, and microfluidics to recapitulate key structural, mechanical, biochemical, and cellular features of human and animal physiology. By incorporating multicellular organoids into perfused microfluidic systems, these models can provide complex, dynamic, and physiologically relevant micro-environments for investigating virus–host interactions that are difficult to capture in conventional two-dimensional cultures and static organoids. Controlled flow, shear stress, extracellular matrix organization, tissue–tissue interfaces, and multicellular signaling enable mechanistic investigation of viral infectivity, dissemination, tissue injury and immune activation. Integration of real-time imaging and biosensors further permits longitudinal monitoring of viral replication, host responses, and tissue integrity, expanding the potential of these platforms for antiviral drug discovery. Recent organoid-on-chip studies using brain, skin, vaginal, respiratory, and intestinal models have demonstrated how tissue architecture, mechanical forces, glycocalyx dynamics, and immune–stromal interactions influence viral tropism and pathogenesis. In this review, we provide a mechanistic and translational overview of organoid and organ-on-chip technologies for studying viral infections, with particular emphasis on models of herpes simplex virus (HSV)-mediated disease. We further examine advances in immune integration, multi-organ systems, biosensing, and computational approaches that are expanding the complexity and predictive potential of these models. Importantly, patient-derived organoids and organ-on-chip platforms can capture interindividual differences in viral susceptibility, host responses, and therapeutic efficacy, providing pharmaceutical research with more precise, patient-relevant data to support drug prioritization and precision antiviral medicine. Finally, we discuss key barriers to broader adoption, including organoid maturation, biological and technical variability, reproducibility, scalability, biosafety, cost, standardization, and regulatory validation. Collectively, these advances position organoid and organ-on-chip technologies as powerful human-relevant models that bridge reductionist in vitro systems and human disease, while continued optimization, standardization, and validation will be essential to realize their full potential for mechanistically informed antiviral discovery, therapeutic development, and precision medicine. Full article

To address severe PCD tool wear during the milling of high-volume-fraction SiC_p/Al composites, a synergistic milling process coupling pulsed laser pretreatment with ultrasonic vibration was proposed. Five-factor, four-level orthogonal experiments were conducted on 70 vol.% SiC_p/Al composites to investigate the effects of milling speed, feed per tooth, cutting depth, laser power, and ultrasonic amplitude on milling forces and tool wear, and a tool wear prediction model was established. The results showed that the factors influencing tool wear, in descending order, were feed per tooth, cutting depth, milling speed, laser power, and ultrasonic amplitude. Appropriate laser power and ultrasonic amplitude reduced cutting loads and suppressed tool wear. The model achieved a coefficient of determination of 0.7887 and was statistically significant overall. The optimal parameter combination was 50 m/min, 0.02 mm/z, 0.1 mm, 60 W, and 3.5 μm, under which the tool wear loss was 1.0 mg, representing a reduction of 61.54% compared with the maximum-wear condition. The main wear modes of the PCD tool included rake-face grooving and fatigue spalling, flank-face abrasive wear, and cutting-edge micro-chipping. These findings provide a useful reference for the precision milling of SiC_p/Al composites. Full article

Micro curved-surface parts are widely used in the aerospace, defense, biomedical, and automotive industries, and their growing adoption imposes increasingly stringent performance requirements. Five-axis micro-milling can achieve precision machining of parts with complex shapes. In the micro-milling process, the cutting force is a critical parameter, as it is the main factor causing machining deformation, vibration, and tool wear. Therefore, this study develops a prediction model for five-axis micro-milling forces in the machining of micro complex curved-surface parts. First, four coordinate systems were established for the five-axis milling process, and the transformation relationships among them were derived. A cutter–workpiece engagement (CWE) extraction method based on solid modeling was also introduced. Then, an instantaneous undeformed chip thickness (IUCT) model was established, taking into account tool runout, elastic recovery of the machined surface, minimum chip thickness, and the local radius of the micro ball-end mill. On this basis, a five-axis micro-milling force prediction model was developed. Finally, five-axis micro-milling experiments were conducted on a micro-impeller and a micro-spherical part, and the cutting forces at different cutter location (CL) points were measured. For the micro-impeller blade, the average percentage errors in the X, Y, and Z directions at all selected CL points were below 11.2%; for the micro-spherical part, the corresponding errors were below 14.4%. These results show good agreement between the predicted and measured values, verifying the effectiveness of the proposed model. Full article

Carbon fiber reinforced plastic (CFRP) composites have been widely adopted in the aerospace industry due to their excellent mechanical and physical properties. However, their anisotropy and weak interlaminar bonding make them prone to defects such as delamination and fiber pull-out during conventional drilling (CD). Longitudinal-torsional coupled ultrasonic vibration-assisted drilling (LTC-UAD) integrates axial and circumferential vibrations to suppress hole defects and is considered a promising machining method for improving the quality of holes drilled in CFRP. Based on kinematic analysis, a model for the working rake angle of the main cutting edge is established to obtain the variation law of the maximum working rake angle along the cutting edge. Compared with CD and longitudinal ultrasonic vibration-assisted drilling (L-UAD), LTC-UAD significantly increases and homogenizes the maximum working rake angle of the main cutting edge, which helps optimize its cutting performance. A three-dimensional finite element model of CFRP is constructed to analyze the dynamic fiber removal process under typical fiber orientations. Finally, drilling experiments are performed to observe the hole wall micro-morphology at various fiber angles. The simulation results indicate that ultrasonic vibration causes periodic changes in the fiber cutting angle, subjecting the fibers to a directional shear state and making them more prone to shear fracture. Two-dimensional ultrasonic vibration cutting enhances the directional shear effect, promotes fiber fracture, accelerates chip removal, and improves the quality of the machined surface. Experimental observations confirm LTC-UAD alleviates fiber crushing, bare fibers, and surface cavities with uniform resin coverage. Furthermore, ultrasonic vibration suppresses thrust force. L-UAD and LTC-UAD yield 10.6% and 17.1% reductions via periodic cutting depth variation and facilitated carbon fiber shear fracture. Full article

Hard and brittle material components with complex curved surfaces are widely used in critical foundational parts within aerospace, optoelectronics, and other fields. Their machining quality directly determines the performance and reliability of high-end equipment. However, the inherent properties of hard and brittle materials make them prone to surface/subsurface damage during traditional polishing processes, and maintaining the form accuracy of complex curved surfaces is challenging. Although abrasive waterjet polishing enables non-contact flexible processing, its energy efficiency is low. Additionally, although ultrasonic-assisted polishing can improve material removal, its spatial localization is insufficient, limiting energy utilization efficiency. To address these issues, this paper proposes a novel method of focused, ultrasonic, vibration-assisted abrasive waterjet polishing. The influence of the radiation force and cavitation force of the focused ultrasonic field on abrasive particle motion is analyzed, and analytical equations for abrasive particle velocity are established. Subsequently, single-factor and response surface methodologies are employed to systematically evaluate the influence of process parameters on machining quality and efficiency. The material removal process during FUAP involves both plastic shearing/chip formation and localized brittle fracture. Focused ultrasonic assistance promotes micro-cutting and plastic shearing, while localized crushing pits indicate that brittle fracture remains non-negligible. The focused ultrasound superimposes alternating stress onto the impact action, mitigating microscale crushing pit defects during the brittle removal process of monocrystalline silicon. Furthermore, appropriately increasing ultrasonic power, enlarging abrasive particle size, and raising abrasive concentration all contribute to enhanced material removal from monocrystalline silicon. Adjusting the nozzle height to the effective region of the focused ultrasonic energy field promotes material removal via chip formation while avoiding pit defects caused by excessive fracture. These results suggest that focused ultrasonic energy can be effectively integrated into abrasive waterjet polishing to enhance material removal while suppressing brittle surface defects, thereby offering a promising strategy for the ultra-precision finishing of hard and brittle components with complex curved surfaces. Full article

Additive manufacturing [AM; three-dimensional (3D) printing] is reshaping heterogeneous photocatalysis by enabling architected materials and devices that span from centimeter-scale monoliths to micro- and lab-on-chip reactors. Heterogeneous photocatalysts have traditionally been deployed as powders or simple coatings, which offer high activity but suffer from mass-transfer limitations, poor light utilization and difficult recovery. Three-dimensional (3D) printing now allows precise control over macroscopic geometry, internal channel networks and micro-/nano-scale surface texturing, creating structured photocatalysts and microreactors that can be tailored for specific photon and flow fields. In contrast to recent reviews that primarily survey materials development or additive-manufacturing routes, this work focuses on photocatalytic microreactors and lab-on-chip devices as multi-scale reactors in which catalyst composition, architected geometry, photon management and hydrodynamics are co-designed across length scales. We summarize three-dimensional 3D-printed photocatalytic systems based on polymer–oxide composites, ceramic scaffolds such as zinc oxide (ZnO)/titanium dioxide (TiO₂) clay monoliths, and laser-written titanium dioxide (TiO₂) nano-architectures, with particular emphasis on microfluidic and lab-on-chip implementations fabricated by fused deposition modeling (FDM), direct ink writing (DIW), stereolithography (SLA), digital light processing (DLP) and laser direct writing (LDW). Based on the literature data and representative case studies, we examine how architected lattices, sponges and microreactor chips affect key performance metrics—apparent rate constants, apparent quantum yield (AQY) and space–time yield (STY)—for the degradation of dyes, antibiotics, detergents and other emerging contaminants in realistic matrices, and we compile reported values to illustrate emerging performance trends and limitations. Representative case studies highlight 3D-printed manganese-doped zinc oxide (Mn:ZnO)-decorated sponges used as modular cartridges for greywater and detergent treatment, as well as laser-written titanium dioxide (TiO₂) nano-photocatalysts integrated into microchannels to couple structured light fields with controlled residence times. Finally, we outline materials and process challenges—including ultraviolet (UV) aging of polymer supports, the energy intensity of ceramic sintering and the lack of standardized testing protocols—and identify future research directions formulti-scalee modeling and techno-economic evaluation of three-dimensional (3D)-printed photocatalytic microreactors and devices. Full article

Microfluidic cell culture systems provide controlled microscale environments for biomedical research; however, cell viability within closed microchambers depends on adequate oxygen availability during the adhesion phase and on the mechanical stresses generated after perfusion begins. Experimental characterization of oxygen depletion and local shear stress remains challenging due to the small dimensions involved and the complexity of transport phenomena. In this study, a computational framework was developed to assess oxygen transport and hydrodynamic shear stress in an SU-8-based microfluidic culture chamber. Oxygen diffusion and cellular consumption were first modeled under static conditions to determine cell survival time prior to perfusion. The influence of chamber height on oxygen availability was investigated, and an empirical correlation was derived to predict oxygen concentration as a function of chamber geometry. Subsequently, medium perfusion was introduced, and the resulting wall shear stresses acting on adhered cells were evaluated under different flow conditions. The simulations demonstrated that chamber height significantly affects oxygen depletion time, while both chamber geometry and flow rate influence the magnitude of wall shear stress. The proposed framework provides practical design guidelines for optimizing microfluidic culture systems, enabling adequate oxygen supply and physiologically compatible mechanical conditions while reducing reliance on extensive experimental testing. Full article

In the field of long-distance partial discharge (PD) detection for submarine cables, there is an urgent need for a remote demodulation distributed detection technology deployable at multiple critical locations to overcome the limitations of single-point measurement. Factory joints of high-voltage submarine cables are high-risk components for PD, and long-distance fiber optic acoustic sensing technology holds the greatest potential for online monitoring. However, due to the viscoelasticity of the joint’s polymer insulation structure, sound propagation distance is severely limited, and non-multi-point measurement cannot achieve effective coverage of the measurement area. This paper proposes a quasi-distributed remote demodulation sensing system, in which both the fiber optic interferometer and the distributed feedback fiber laser (DFB-FL) serve as sensors, enabling highly sensitive quasi-distributed PD detection. The DFB-FL itself is highly sensitive to strain, and the multiple fiber coils formed by the interferometer arms are also highly sensitive to strain. Both can be modulated by the micro-strain induced by the acoustic field generated from PD in the polymer solid, producing phase shifts of the optical waves, which are then intrinsically demodulated by the interferometer system to extract the vibration signals caused by the discharge. Theoretical analysis shows that the sensitivity increases with the length of the unbalanced arm, with the upper limit constrained by laser coherence and optical attenuation; for the PD frequency band, the optimal unbalanced length is below 200 m—this design rule is applicable to on-chip interferometric sensors. The interferometer coils employ bend-insensitive fibers to suppress optical loss and improve fringe visibility. Meanwhile, three fiber coil configuration schemes are constructed to enhance the detection sensitivity to acoustic signals. Simulation results generate frequency response contour maps based on Young’s modulus, indicating that the solid-wound coil achieves the highest amplitude and the broadest bandwidth, with an optimal response frequency of approximately 50 kHz. Experimental results demonstrate that among the three structure types, the solid-wound coil also achieves the largest response ratio. Finally, in tests performed on a 220 kV submarine cable intermediate joint (with the system installed inside the metallic sheath), the minimum detectable discharge level in the DFB-FL region reached 6.75 pC, while that for the fiber coil reached 12.66 pC; when installed outside the metallic sheath, the minimum detectable discharge levels were 53.2 pC for the grating region and 89.6 pC for the fiber coil. Full article

The increasing computational demands of artificial intelligence (AI) inference at the edge require hardware accelerators capable of overcoming the von Neumann bottleneck while operating under power constraints. Conventional digital architectures based on multiply–accumulate (MAC) units are limited in energy efficiency and scalability for resource-constrained applications. This work presents a proof-of-concept AI accelerator based on analog frequency–domain computation implemented within a semi-systolic array architecture. The proposed approach exploits frequency mixing to perform multiplication and accumulation operations in hardware, enabling the execution of matrix–matrix operations, which constitute the General Matrix Multiplication (GEMM) methods that dominate the computational workload of convolutional and fully connected neural networks. The proposed system consists of a custom printed circuit board controlled by an ATmega328P microcontroller(Microchip Technology Inc., Chandler, AZ, USA) and a software stack designed to interface with standard machine learning frameworks such as PyTorch. The software layer enables neural network operations, including convolutional and fully connected layers, to be mapped onto hardware-executed matrix–matrix computations through an abstraction analogous to the General Matrix Multiplication (GEMM) functionality provided by Level-3 Basic Linear Algebra Subprograms (BLAS). Matrix multiplication and accumulation are partly performed directly by the hardware processing elements, while the software control unit coordinates data movement and computation scheduling. Although bias operations are not implemented in the current prototype, their comparatively low computational cost makes them less critical to the overall acceleration strategy. A quantization-aware mapping methodology constrained by analog-to-digital and digital-to-analog converter specifications is introduced to translate neural network operations into frequency–domain computations. The paper further describes the hardware architecture, communication protocols, software stack organization, and interactions between system components. In addition, the effects of analog nonidealities and error sources associated with frequency–domain multiplication are investigated, and simulations of the proposed processing elements are presented to evaluate the computational approach. Experimental and simulation results demonstrate the feasibility of performing dense linear algebra operations through analog frequency–domain processing and validate the operation of the processing elements. The study further explores converter resolution, frequency interference, and analog component nonidealities and provides a comparison with conventional digital and other low-power accelerator approaches. The results indicate that exploiting the inherent parallelism of analog computation offers a promising pathway toward ultra-low-power AI inference, making the proposed architecture a potential alternative for energy-constrained edge applications. Full article

The rapid expansion of RNA therapeutics requires analytical methods that resolve molecular integrity, heterogeneity, and process-related impurities. Capillary electrophoresis (CE) provides direct RNA separation with low sample consumption and can be coupled with laser-induced fluorescence (LIF) or mass spectrometry (MS). This review assesses CE across RNA therapeutic development, release testing, and process analytics. We examine CE separation methods and platform formats for mRNA integrity and poly(A) tail analysis, size- and structure-related impurity profiling, circular RNA purity evaluation, RNA modification analysis by CE-MS, and aptamer discovery. We also discuss multi-capillary systems, microchip electrophoresis (MCE), automation, artificial intelligence (AI)-assisted data analysis, and bioprocess integration. CE is already highly useful for selected quality control tasks, especially mRNA integrity, poly(A) tail profiling, and circular RNA purity, but broader routine adoption requires improved sensitivity, standardization, method transfer, and regulatory acceptance. Full article

High-frequency (HF) RFID systems operating at 13.56 MHz are widely used in applications such as near-field communication, contactless identification, and smart sensing. Their performance strongly depends on the inductive coupling between the reader and tag antennas, which is influenced by antenna geometry, relative position, orientation, and environmental conditions. This work investigates the antenna component of passive HF RFID tags, represented by planar spiral inductors, without integrating an RFID microchip, allowing the electromagnetic coupling to be analyzed independently of chip-specific effects. A low-cost automated measurement platform was developed to experimentally evaluate the influence of antenna geometry, distance, orientation, and temperature on inductively coupled HF RFID antennas. The platform combined an automated positioning system with a mobile application for remote operation, minimizing the influence of the operator during measurements. A second experimental setup was designed to investigate the effect of temperature on antenna performance. Experimental results show that rectangular spiral antennas generally provided stronger inductive coupling than the other geometries investigated. Furthermore, varying the receiving antenna orientation improved the coupling between rectangular and octagonal antennas under specific configurations. Temperature variations within the investigated range had only a minor influence on antenna performance. The proposed platform provides a low-cost, portable, and reproducible solution for the experimental characterization of HF RFID antennas operating at 13.56 MHz. Full article

To investigate the influence of substrate curvature and process deviations on the bonding quality of microchips mounted on curved surfaces, a thermo–mechanical coupled finite element model was developed in ANSYS 19.2 using a 0402 microchip as a representative component. The model was employed to evaluate the effects of substrate curvature radius, chip placement position, chip angular misalignment, and thermocompression-head angular deviation on the stress distribution and bonding behavior during conformal assembly. The simulation results were further validated through thermocompression bonding experiments. The results show that decreasing the substrate curvature radius significantly increases the stress concentration in the chip-pad region and leads to a more non-uniform stress distribution. In addition, placement errors and loading-direction deviations adversely affect bonding quality by altering the contact stress distribution and increasing the tendency for chip displacement and sliding. Quantitative analysis reveals the relative sensitivity of bonding performance to different geometric and process parameters, providing insight into the dominant thermo–mechanical mechanisms governing curved-surface assembly. Experimental results further demonstrate that excessive thermocompression tilt angles can significantly reduce bonding strength and increase chip sliding, suggesting that the tilt angle should be controlled within an appropriate range to ensure assembly reliability. The proposed thermo–mechanical modeling approach provides a quantitative tool for evaluating the influence of process variations on curved-surface microchip assembly and offers practical guidance for process parameter selection, tolerance control, and reliability-oriented design of conformal electronic packaging. Full article

Tool micro-defect quantification is often degraded by optical sampling limits, boundary aliasing, and non-Gaussian industrial noise. To improve segmentation fidelity and physical measurement reliability, this study proposes a physically guided reconstruction and metrology framework for milling tool defects. The framework first uses an improved ResNet18-BiFPN segmentation network supervised by a distance transform-based boundary-aware loss, which encourages mask boundaries to fit high-curvature crack and chipping regions. A tensor-guided elliptical adaptive structuring element is then introduced to restore local topology while preserving tangential connectivity and normal boundary fidelity. Finally, a stable edge region search and curvature-adaptive gray/Zernike moment fusion strategy are used for noise-robust sub-pixel localization and physical area quantification. Image-based evaluation and controlled simulation-based stress tests show that the proposed method achieves an mIoU of 92.5%, an F1-score of 97.1%, and an HD95 of 2.15 pixels. Under strong synthetic noise, the average distance error remains below 0.28 pixels. For micro-defect area measurement, the mean relative error is reduced to approximately 1.7%. These results indicate that the proposed framework can support more reliable defect metrology for tool condition monitoring under complex industrial imaging conditions. Full article

Microscopic magnetic stimulation (MSTI) induces electric fields without direct charge injection and can shape localized field gradients with asymmetric micro-coils. Most demonstrations still rely on external drivers, off-chip hardware, or separated coil validation, so the CMOS integration boundary remains poorly characterized. This work presents a fabricated $2\times 1 {\mathrm{mm}}^2$ $0.18 \mathsf{\mu }\mathrm{m}$ CMOS magnetic-stimulation SoC that co-integrates ASK-compatible command decoding, FSM and register-based parameter control, a programmable current–voltage–current triangular driver, and a bent top-metal micro-coil, and it characterizes the on-chip driver-to-coil path together with a substrate-aware field model. Sensing-load reconstruction confirms command-to-waveform programmability, including duration-window decoding, burst-count control, and polarity reversal, with measured slew targets that give a peak current of $I_{\mathrm{pk}}=3.72$–$21.6 \mathrm{mA}$. A quantitative comparison contrasts the current-mode triangular driver with conventional electrode stimulators, a coil-impedance measurement shows the coil stays resistive across 1 to 10 MHz, and the measured total SoC power is about 41 mW. Substrate-aware simulation at a $15 \mathsf{\mu }\mathrm{m}$ target plane shows that the grounded p-substrate retains $35.1$–$40.5\%$ of the no-substrate peak x-directed field-gradient metric. The prototype establishes this electrical programmability and the substrate-aware gradient-transfer loss as a compact design-margin metric for CMOS-integrated magnetic stimulation. Direct biological activation is not claimed and is left to future in vitro validation. Full article

Orbital remote sensing platforms increasingly rely on CNN-based object detection for real-time situational awareness. However, deploying these models on spaceborne edge devices is challenging because of stringent Size, Weight, and Power (SWaP) constraints. In addition, the branch-and-merge topology of conventional single-stage detectors increases on-chip memory usage and introduces pipeline stalls, limiting efficient FPGA implementation. To address these challenges, we proposed RS-YOLO, an object detection algorithm developed through a hardware–software co-design approach. Structural re-parameterization converts heterogeneous branches into a sequential stream of padding-free convolutions, producing a deterministic dataflow and reducing per-state combinational control complexity and data-path multiplexing overhead. To mitigate the high-entropy concentration at the center of the re-parameterized kernels, we further introduce a spatial heterogeneous quantization (SHQ) engine. The SHQ engine assigns 16-bit precision to the central coefficients while preserving vectorized 8-bit computation for peripheral elements, reducing quantization errors for small targets with minimal hardware overhead. Experimental results on the Xilinx Zynq-7020 platform show that the proposed system consumes only 2.24 W while achieving a mean Average Precision (mAP) of 0.887 on the NWPU VHR-10 dataset, representing a 1.4% decrease compared with the FP32 baseline. The system also achieves an energy efficiency of 15.19 GOPS/W, demonstrating an effective balance between hardware efficiency and detection performance for resource-constrained edge platforms such as micro-satellite payloads. Full article