Search Results (1,162)

Internal wall loss in hot-fluid pipelines is difficult to monitor during operation because weak thermal perturbations are masked by global heating, axial cooling, and external heat dissipation. This study develops an external thermometry framework combining a flexible-base fiber Bragg grating (FBG) array with residual-peak morphology analysis. A closed-loop hot-water rig with five artificial wall-loss regions was tested under exposed-air and buried-soil boundaries, and a validated conjugate heat-transfer model generated 269 controlled scenarios. Experiments showed residual anomalies above measurement uncertainty, with a maximum repeatability standard deviation of approximately 0.12 °C and the clearest signals at 90–120 s after hot-water injection. Boundary conditions strongly affected observability at 115 mm and 70 °C, and the residual peak increased from about 0–1 °C in exposed air to 8–9 °C under the buried boundary. Simulations showed that defect width expanded the disturbed region from approximately 100 to 210 mm, while peak amplitude remained coupled to width and depth. Six morphology descriptors jointly estimated position, width, and depth, with mean absolute errors (MAEs) of 0.547, 1.647, and 0.245 mm, respectively. The method provides a recalibratable early-screening route for locating suspicious wall-loss regions before confirmatory inspection. Full article

Structural health monitoring (SHM) based on ultrasonic-guided waves has been widely investigated for aerospace, transportation, marine engineering, petrochemical equipment and large-scale civil infrastructure. As the core component for guided-wave excitation and signal reception, the transducer directly affects electromechanical conversion efficiency, modal selectivity and service stability. Interdigital transducers (IDTs) are characterized by a lightweight structure, designable wavelength, tunable operating frequency and good array compatibility, and therefore show considerable potential for curved structures, composite components and large-area online monitoring. The periodically repeated interdigital electrode pattern represents a basic form of structural symmetry in IDTs, providing the geometric basis for wavelength matching and frequency-selective response, while substrate properties govern the electromechanical conversion efficiency and stability of the device. This review focuses on the research progress of substrate materials for flexible IDTs. The material characteristics and application status of inorganic piezoelectric materials, piezoelectric polymers, piezoelectric composites and heterogeneous integrated substrates are summarized. The differences among typical substrate systems are compared in terms of electromechanical coupling, flexible conformability, thermal stability, acoustic loss and integration process. Recent applications of IDTs in guided-wave damage detection, flexible sensing, high-temperature monitoring and on-chip acoustic devices are also discussed. The main challenges and future directions of IDT substrate materials are analyzed to provide guidance for material selection, device design and SHM applications. Full article

With the rapid advancement of terahertz technology, electromagnetic interference has become a critical issue that compromises device performance, creating an urgent demand for high-performance terahertz absorbers. Three-dimensional (3D) printing, characterized by flexible structural design, monolithic fabrication, and mold-free processing, has emerged as a promising technique for producing terahertz absorbers. In this work, inspired by the structural and functional characteristics of deep-sea sponges, we propose a bioinspired absorber design that integrates a porous topology with 3D printing. By optimizing the rotation angle and the hollowed array, the absorber establishes multiple internal reflection paths, which, combined with the structural matrix and the graphene conductive coating, enable highly efficient dissipation of electromagnetic energy. Experimental results show that the fabricated sample achieves an absorptivity exceeding 99% over the 0.5–2.0 THz frequency range, while also exhibiting wide-angle absorption and polarization-insensitive performance. The influence of pore size and graphene concentration on the absorption properties is systematically revealed. This work further enhances the performance of 3D-printed terahertz absorbers and provides a novel technical pathway for the design and fabrication of high-performance terahertz absorbers. Full article

Flexible pressure sensors serve as core sensing components for wearable health monitoring systems, electronic skins for soft robots, and flexible human–machine interaction devices. Benefiting from the interfacial electric double-layer polarization effect, iontronic sensing delivers far higher pressure response sensitivity than conventional parallel-plate capacitive sensors, endowing it with distinctive advantages in the detection of weak physiological signals. Nevertheless, current dense ionic thin-film dielectric layers suffer from limited deformation space under compression and poor low-pressure sensing capability. Mainstream high-precision micropillar arrays are fabricated via photolithography, 3D printing, and metal etching molds, which require costly equipment and complicated fabrication procedures, making large-area mass production unfeasible. Random frosted concave-convex microstructures feature disordered dimensions, leading to severe device hysteresis and narrow linear ranges, which fail to achieve ultrahigh sensitivity alongside a wide pressure detection range simultaneously. To address the aforementioned multiple bottlenecks, this paper proposes a low-cost resin template replication process to fabricate TPU-based ionic thin-film dielectric layers with ordered micropillar array microstructures. Combined with inkjet-printed silver conductive PI flexible electrodes, an iontronic flexible pressure sensor with a sandwich layered structure is constructed. Multi-dimensional investigations including microscopic morphology characterization, electromechanical sensing performance calibration, and human wearable application tests are systematically implemented to thoroughly elucidate the synergistic enhancement mechanism of the arrayed micropillars. Test results demonstrate that the effective pressure detection range of the sensor spans 0–1038 kPa, accommodating ultra-low pressures such as pulse signals as well as medium-to-high-pressure loads including joint bending. The sensitivity reaches 23.27 kPa⁻¹ within the low-pressure range of 0–200 kPa and remains stable at 3.52 kPa⁻¹ in the high-pressure range of 200–1038 kPa, with piecewise linear fitting correlation coefficients of 0.93 and 0.96 respectively. Both the response time and recovery time of the device are 40 ms, and the hysteresis error throughout the loading-unloading cycle is merely 2.62%. After 20,000 consecutive cyclic loading-unloading tests, the peak capacitance output only decays by 5.1%, verifying outstanding mechanical fatigue resistance and electrical stability. Validations in multi-scenario applications prove that the sensor can accurately capture human physiological and motion signals including radial artery pulses, laryngeal deformation induced by multi-syllable vocalization, and multi-angle bending of fingers and elbow joints, suitable for home-based health monitoring, quantitative rehabilitation training, flexible tactile interaction and other scenarios. The entire fabrication process eliminates high-precision micro-nano processing equipment such as photolithography systems, plasma etchers and 3D printers; only general chemical raw materials and conventional laboratory instruments are adopted. The reusable templates enable low manufacturing costs and large-area coating forming, offering a novel low-cost technical solution for the engineering implementation and industrialization of high-performance iontronic flexible pressure sensors. Full article

Scientific visualization is changing from passive observation to active, AI-assisted collaboration. While Extended Reality (XR) has proven valuable for comprehending dense 3D arrays, traditional VR applications are typically deployed in rigid, single-purpose, and monolithic architectures. In this paper, we present the evolution of ASCRIBE-XR: a virtual reality platform backed by remote computation that has been re-engineered into a dynamic, service-oriented ecosystem. We introduce three core innovations that make immersive data analysis easier, faster, and more flexible when using multimodal scientific imaging. First, a lightweight Python REST interface decouples XR logic from the rendering engine, enabling real-time, programmable scene customization and on-demand data generation. Second, we present a Specimen Catalog architecture that lets the platform pivot between radically different disciplines, ranging from archaeological heterogeneous concrete and fuel-cell membranes to the root system of a bioenergy grass, by describing each dataset through portable metadata rather than hard-coded application logic. Finally, we introduce a prompt-driven layer powered by the Claude Agent SDK, allowing researchers to generate, segment, and manipulate volumetric and mesh data through natural language dialogue within the virtual space. For example, applying foundation models such as the Segment Anything Model (SAM) to perform zero-shot segmentation on demand. By bridging human intent with remote computation, ASCRIBE-XR relaxes the constraints of conventional visualization tools, offering a highly adaptable, conversational platform for scientific discovery with human auditing. Full article

Flexible pressure sensors featuring high sensitivity, a broad detection range, and excellent stability are pivotal components for high-precision electronic skins and human health monitoring. To circumvent the limitations of existing sensors in maintaining high responsiveness across extensive pressure ranges, herein, a novel flexible capacitive pressure sensor is developed based on a 3D porous ionogel foam composite (IL/EG/PVA@MF) coupled with a planar electrode array. This device leverages the synergistic structural engineering of the 3D hyperelastic melamine foam (MF) skeleton and the pressure-regulated fringe-field distribution and iontronic interfacial polarization of the porous ionogel. Experimental evaluations demonstrate that the sensor achieves a high normalized sensitivity of 62.45 kPa⁻¹ (2–10 kPa) and maintains reliable piecewise linear sensing performance across a broad working range of 0–50 kPa, accompanied by a rapid response time of within 8 ms. Furthermore, the sensor exhibits outstanding performance consistency after 6000 compression-release cycles at 50 kPa, verifying its good mechanical durability. In practical applications, the device can monitor diverse physiological signals with high fidelity, ranging from subtle radial artery pulses to large-scale joint movements and specific coughing patterns, underscoring its broad potential for integrated wearable systems and intelligent healthcare. Full article

A flexible integrated composite electrode is fabricated using NiV-layered double hydroxide (NiV-LDH) nanosheets grown perpendicularly onto a Ti₃C₂T_x MXene-incorporated carbon nanofiber scaffold (MXene/CNFs). This hybrid structure, prepared by electrospinning and a hydrothermal treatment, is referred to as NiV-LDH@MXene/CNFs. Constructed from a conductive MXene/CNF scaffold and vertically aligned NiV-LDH nanosheets, the integrated flexible electrode offers uninterrupted electron transport, good flexibility, abundant active sites, and strong interfacial cohesion, thereby obviating the use of polymeric binders and conductive additives. The hydrophilic nature of MXene and the three-dimensionally interconnected porous structure favor rapid electrolyte uptake and ion diffusion. As a result of these synergistic effects, the composite exhibits a specific capacitance of 614 F g⁻¹ at 1 A g⁻¹ and retains 60% of its initial capacitance after 10,000 cycles at 5 A g⁻¹ in a three-electrode cell. An asymmetric supercapacitor made of this material and activated carbon achieves 68.75% capacitance retention under the same cycling protocol at 5 A g⁻¹ and shows a stable open-circuit voltage of 1.37 V. Two cells in series are capable of lighting a 3 V LED strip. Overall, this work validates an effective strategy to prepare high-capacity, robust, and binder-free flexible electrodes for advanced energy-storage applications. Full article

Smart chemical sensors have emerged as promising tools for real-time monitoring of food spoilage in both fermented and non-fermented products. By detecting key spoilage indicators—including biogenic amines, ammonia, hydrogen sulfide, methane, pH variations, and microbial volatile organic compounds (MVOCs)—these systems enable rapid, on-site assessment of food quality, offering a viable alternative to conventional, time-consuming laboratory analyses. Recent advances encompass diverse sensing mechanisms, including chemiresistive platforms based on conducting polymers and MEMS (Microelectromechanical Systems); optical/colorimetric systems using dyes, metal–organic frameworks, and porphyrins; and electrochemical and biosensing approaches employing enzymes, antibodies, aptamers, and whole-cell recognition elements. These sensors demonstrate high sensitivity (ppb–ppm range), enabling early detection of spoilage before sensory perception or microbiological threshold exceedance. Their applicability has been validated across a wide range of food matrices, including meat, fish, dairy products, vegetables, beverages, and fermented foods. Despite significant progress, key challenges persist, including signal drift, limited specificity, susceptibility to environmental factors such as humidity and temperature, and interference from complex food matrices. Furthermore, integration into intelligent packaging requires the development of flexible, food-safe, and regulatory-compliant materials. Emerging approaches that combine sensor arrays with machine learning and MVOC pattern recognition are enhancing predictive accuracy and enabling food classification across commodity types. Overall, smart chemical sensing technologies are rapidly transitioning from laboratory prototypes to practical applications in intelligent packaging and wireless monitoring systems, with ongoing research focused on improving robustness, standardization, and scalability for commercial deployment. This article provides an overview of the topic, drawing on the available bibliography from the last five years and the most-cited scientific databases. Full article

Deep learning (DL) models have achieved remarkable progress in natural language processing, computer vision, content generation, and edge intelligence; however, their rapidly increasing computational complexity, memory demand, and deployment diversity pose significant challenges for practical implementation. Field-programmable gate arrays (FPGAs) provide customized low-precision computation, spatial dataflow, on-chip data reuse, reconfigurability, and rich I/O capabilities, making them an important platform for DL inference. This paper presents a systematic review of FPGA-based DL deployment from a cross-layer perspective spanning model, compiler, architecture, runtime, and electronic design automation (EDA). Following a PRISMA-guided evidence synthesis protocol, this review analyzes DL workload characteristics, FPGA architectural optimizations, deployment toolflows, and physical implementation challenges. A unified taxonomy is proposed along the specialization–programmability continuum, including model-fixed accelerators, generator-based accelerators, template-configurable accelerators, and ISA-programmable overlays. These approaches are compared according to hardware regeneration requirements, model adaptability, operator coverage, compilation cost, and deployment flexibility. Furthermore, emerging workloads, including vision Transformers, graph neural networks, large language models, and multimodal models, are analyzed from the perspectives of computation, memory behavior, and runtime coordination. The review shows that FPGA deployment efficiency increasingly depends on memory capacity, mutable state management, operator support, and end-to-end compilation capability rather than peak multiply–accumulate throughput alone. Based on the analysis of 70 primary FPGA implementation studies, this paper highlights that reliable cross-study comparison requires careful consideration of model configuration, precision, execution phase, batch size, memory residency, FPGA platform, and evidence maturity. For multimodal generative models, the current evidence remains limited, with no identified end-to-end FPGA-based vision–language model implementation in the reviewed corpus. This review provides a systematic perspective for future FPGA-based DL deployment research, emphasizing cross-layer optimization, physically aware compilation, extensible accelerator architectures, and practical deployment efficiency. Full article

Kelp is a high-moisture, flexible, sheet-like marine biomass whose drying behavior is strongly affected by the coupled effects of radiative heating, convective vapor removal, internal moisture migration, tissue shrinkage, curling, and material overlap. Traditional sun drying and hot-air drying remain widely used but are limited by long processing cycles, environmental dependence, high energy consumption, and inconsistent product quality. With the development of infrared heating, heat-pump dehumidification, Internet of Things (IoT)-enabled sensing, fifth-generation (5G) mobile communication, multiphysics simulation, and digital control, kelp drying is progressively shifting toward monitored, model-assisted, and intelligent processing. This review critically summarizes recent advances in kelp and related seaweed drying, with particular emphasis on infrared-assisted heat and mass transfer, drying kinetics, coupled computational fluid dynamics–finite element method (CFD–FEM) simulation, quality evaluation, and intelligent control. Representative published studies demonstrate the engineering potential of these approaches. In a suspended infrared-array kelp drying system, an infrared power density of 1.2 kW m−2 combined with an air velocity of 3 m s−1 maintained the drying temperature at approximately 55–62 °C, while relative humidity decreased from about 80% to 20–30%. Under these conditions, the Page model achieved R² = 0.987 and RMSE = 0.019, the rehydration ratio exceeded 94%, and the total color difference remained below ΔE = 6.5. A recent CFD–FEM–MATLAB workflow further reported a composite operating-condition index of J = 0.4535, with mapped mean and maximum kelp surface temperatures of 62.23 and 63.57 °C, respectively. These quantitative results indicate that the key challenge in infrared kelp drying is not simply to increase heat input, but to coordinate radiation distribution, airflow organization, internal moisture transport, structural response, and quality preservation. Future research should therefore focus on experimentally validated heat–mass-transfer models, adaptive sensing and control, multi-objective optimization, and pilot-scale verification under realistic production conditions. Full article

Autonomous robotic systems designed for extraterrestrial in situ resource utilization (ISRU) will play a central role in enabling a sustained human presence on the Moon and Mars. These robots are expected to perform tasks such as regolith excavation, water extraction, oxygen production, and propellant generation under extremely harsh environmental conditions, including large temperature variations, abrasive dust, high radiation levels, and significant communication delays with Earth. Consequently, their onboard electrical systems must operate with high reliability, autonomy, and fault tolerance. A critical enabling technology for these systems is the isolated power conversion architecture, which distributes energy from primary power sources to multiple robotic subsystems, including mobility actuators, drilling systems, sensors, computing units, and thermal management modules. Future lunar and Martian missions are expected to rely on a combination of alternative energy sources, including solar photovoltaic arrays with energy storage, fuel cells, radioisotope power systems, and nuclear surface power reactors, which can provide continuous and high-density energy independent of sunlight availability. These diverse power sources require flexible and highly efficient isolated DC–DC power conversion architectures capable of managing wide input voltage ranges while ensuring electrical isolation, safety, and system stability across distributed robotic platforms. This literature review surveys recent developments in autonomous isolated power conversion architectures suitable for lunar and Martian resource extraction robots. The review examines advanced converter topologies such as resonant converters, phase-shifted full-bridge converters, dual-active bridge converters, and modular multiport power converters designed for high efficiency, high power density, and scalable power distribution. Emphasis is placed on converter architectures capable of interfacing with nuclear-powered systems and other high-energy-density sources while supporting distributed loads in robotic mining and processing systems. In addition, the paper reviews emerging autonomous control strategies, including adaptive digital control, intelligent power management, fault detection and self-recovery mechanisms, and distributed power architectures capable of maintaining stable operation under dynamic load conditions. The role of wide-bandgap semiconductor technologies, including silicon carbide (SiC) and gallium nitride (GaN), is also examined, highlighting their potential to enable higher switching frequencies, improved efficiency, reduced system mass, and enhanced thermal performance in vacuum environments. Finally, system-level considerations for integrating isolated power conversion within robotic ISRU platforms are discussed, including redundancy strategies, power bus architectures, electromagnetic compatibility, thermal management, and long-duration reliability requirements. By consolidating advances across power electronics, autonomous control, and space power systems, this review identifies key research gaps and outlines design directions for next-generation autonomous power conversion systems capable of supporting scalable lunar and Martian resource extraction infrastructures powered by both renewable and nuclear energy sources. Full article

Sparse antenna arrays are attractive for compact microwave and millimeter-wave front ends because they can achieve prescribed radiation performance with fewer radiating elements, thereby reducing the number of feeding channels, hardware cost, weight, and power consumption. However, the joint optimization of element positions and complex excitations remains challenging, since the element positions enter the array factor nonlinearly and the element-count objective is inherently combinatorial. This paper presents an iterative reweighted ℓ₁ synthesis framework for sparse antenna arrays with continuous element positions. At each iteration, position perturbations are introduced and the array factor is linearized using a first-order Taylor expansion within a trust region. The resulting non-convex sparse synthesis problem is then approximated by convex programing through an iteratively reweighted ℓ₁ relaxation, allowing the excitation amplitudes, phases, and element positions to be updated simultaneously. Additional aperture, minimum-spacing, and minimum-directivity requirements are formulated as convex constraints and incorporated when required, enabling joint control of sparsity, sidelobe level, physical layout, and radiation performance. The proposed method is validated through four representative examples, including a shaped-beam linear array, a tri-pattern reconfigurable linear array, a planar pencil-beam array, and a directivity-constrained planar array. Compared with fixed-grid reweighted ℓ₁ methods under the same specifications, the proposed approach produces sparser layouts while avoiding the grid-resolution limitation. In the directivity-constrained benchmark, it also achieves competitive element reduction while enforcing a prescribed minimum element spacing. These results indicate that the proposed framework provides a flexible and practical synthesis tool for compact and integrated sparse antenna-array design. Full article

The von Neumann architecture, due to the physical separation between memory and processor, has limited the development of data-intensive applications. Neuromorphic computing technologies inspired by the brain’s parallel and event-driven operation mechanisms have enabled low-power in-memory computing. Memristors with tunable conductance can emulate biological synapses, while flexible memristors further offer mechanical flexibility, making them suitable for wearable electronics and intelligent sensing systems. This review systematically summarizes the switching mechanisms of flexible neuromorphic memristors, including conductive filaments, interface effects, ferroelectricity, phase change, and multiple synergistic mechanisms. It categorically discusses natural and bio-derived materials, synthetic organic/polymer materials, and inorganic functional materials, and introduces strategies for enhancing flexibility. The article also covers device architectures such as sandwich structures, crossbar arrays, and fiber-based textile structures, along with low-temperature fabrication techniques. Finally, it reviews recent advances in neuromorphic computing, in-memory computing, biomimetic sensing, and biomedical wearable systems. Challenges related to mechanical stability and device uniformity are analyzed, and future directions toward self-healing materials and integrated sensing-storage-computing systems are outlined. This comprehensive review bridges the gap between material innovation and system-level integration in flexible neuromorphic memristors, providing a valuable roadmap for accelerating the development of next-generation wearable artificial intelligence, edge computing, and bio-integrated electronic technologies. Full article

Achieving precise microstructure control in composite dielectric layers remains a key challenge for enhancing the sensitivity and reducing the power consumption of flexible capacitive sensors. In this work, an ultrasonic needle-induced acoustic streaming strategy is proposed to regulate the spatial distribution of Fe particles within a silicone matrix, enabling controllable particle migration and aggregation in liquid silicone. Multiphysics simulations reveal that, at an excitation frequency of 75.49 kHz, Fe particles are effectively driven toward the ultrasonic focal region, forming a tunable microstructure. Experimental results confirm that this method enables precise morphological control of the composite dielectric layer. The composite with 25 wt% Fe exhibits the highest measured relative permittivity of about 3.45, enabling a capacitive sensor sensitivity of 0.423 kPa⁻¹ in the 0–1 kPa range. After acoustic-streaming optimization and integration into a four-unit capacitive array, the device achieved 0.509 kPa⁻¹ sensitivity, retained 92.04% of its response after 5000 cycles at 3 kPa, and maintained 97.8% of its initial capacitance after 24 h. The proposed approach provides an effective route to improving sensor performance through microstructure engineering while maintaining low electrical loss. This work not only advances the design of high-performance functional composites but also expands the application of acoustic streaming techniques in low-power flexible electronics. Full article

Tactile sensing can enhance robotic grasping by providing contact information unavailable from vision or control signals alone. However, implementing tactile sensing in robotic hands is often constrained by external wiring, data-acquisition hardware, power requirements, and limited fingertip space. This study presents a self-contained capacitive tactile fingertip module for adding wireless multi-point contact sensing to robotic grippers. The module integrates a 3 × 1 array of thin flexible capacitive sensors, a capacitance-to-digital converter, a Bluetooth-enabled microcontroller, and an onboard battery within a compact fingertip-shaped housing. It can be mounted in place of an existing fingertip and operates independently of the robotic hand controller. Individual sensors characterized before module integration responded near-linearly to normal compression up to 2.5 N, with an approximately 8% relative capacitance change at 2.5 N and R² = 0.99, and were evaluated over 100 repeated compression cycles. In proof-of-concept grasping tests with an empty PET bottle, a water-filled PET bottle, and a water-filled aluminum tumbler, the assembled module produced distinguishable capacitance changes at the mid- and proximal-position sensors, providing relative information on contact location and local loading rather than calibrated force. These results demonstrate the feasibility of wireless tactile sensing in robotic grasping using a compact standalone fingertip module. Full article