Search Results (155)

Ammonia serves as a critical medium for hydrogen storage and energy transportation, making the development of efficient ammonia cracking technologies essential for advancing hydrogen energy applications. Plasma-assisted ammonia cracking has emerged as a promising approach for clean energy conversion, leveraging non-thermal plasma to effectively decompose ammonia into hydrogen and nitrogen. Compared to conventional thermal catalytic cracking, this method offers several advantages, including rapid startup and response, operational flexibility, and the ability to operate under low-temperature and atmospheric pressure conditions. This study presents a novel high-pressure plasma reactor designed to overcome the high-energy barriers associated with conventional methods. Through systematic optimization of discharge parameters, reactor configuration, and catalyst integration, significant improvements in both ammonia conversion efficiency and energy utilization have been achieved. Experimental results demonstrate that increased discharge power and reduced ammonia flow rate enhance cracking performance. In the absence of a catalyst, conversion efficiency initially increases with pressure but subsequently decreases at higher pressures. However, the incorporation of a catalyst markedly improves overall performance across all tested conditions. These advancements support the practical implementation of ammonia-based systems for distributed hydrogen supply and clean propulsion technologies. Full article

Background/Objectives: Bone defects remain widespread. Type I collagen–hydroxyapatite composites suit bone engineering by mimicking matrix structure, making them pertinent materials for bone tissue engineering across a range of defect types. Their application is well aligned with non-load-bearing conditions, while use in load-bearing sites requires mechanical properties that meet the demands of those environments. Marine collagen offers a low-cost source from processing by-products. This work aimed to develop perch collagen–hydroxyapatite scaffolds for bone tissue engineering. Methods: Composites with COLL:HAp ratios of 100:0, 50:50, 40:60, and 30:70 were prepared. After crosslinking and freeze-drying, porosity and water absorption were examined. SEM and X-EDS assessed morphology and elemental distribution. FT-IR confirmed the chemical composition. Compression tests evaluated mechanical behavior. Cell viability and colonization assessed the biological performance. Biodegradability, thermal stability, and antimicrobial activity were also determined. Results: FT-IR confirmed the characteristic absorption bands of both components. SEM and swelling behavior showed porous, interconnected structures with uniform hydroxyapatite dispersion. X-EDS indicated Ca/P ratios consistent with hydroxyapatite. Thermal analysis demonstrated scaffold stability. Compression tests showed mechanical resistance for all the scaffolds, with stiffness increasing with the inorganic content. Perch collagen enhanced biological functionality, supporting osteoblast viability and colonization. Biodegradation gradually proceeded. Antibacterial activity against the tested pathogens was detectable, though moderate. Conclusions: The developed scaffolds combined structural stability, controlled degradation, and favorable cell response, constituting a viable and promising candidate for applications in bone tissue engineering. Full article

Distributed fiber-optic sensing (DFOS) with intentionally spaced mechanical fixity points was experimentally evaluated for the structural health monitoring (SHM) of reinforced concrete (RC) members. A full-scale four-point bending test was conducted on a 12 m RC beam (400 × 400 mm) instrumented with a single-mode DFOS cable incorporating internal anchors at 2 m intervals and bonded externally with structural epoxy. Brillouin time-domain analysis (BOTDA) provided distributed strain measurements at approximately 0.5 m spatial resolution, with all cables calibrated to ±15,000 µε. Under stepwise monotonic loading, the system captured smooth, repeatable strain baselines and clearly resolved localized tensile peaks associated with crack initiation and propagation. Long-gauge averages exhibited a near-linear load–strain response (R² ≈ 0.99) consistent with discrete foil and vibrating-wire strain gauges. Even after cracking, the DFOS signal remained continuous, while some discrete sensors showed saturation or scatter. Temperature compensation via a parallel fiber ensured thermally stable interpretation during load holds. The fixed-point configuration mitigated local debonding effects and yielded unbiased long-gauge strain data suitable for assessing serviceability and differential settlement. Overall, the results confirm the suitability of fixed-point DFOS as a durable, SHM-ready sensing approach for RC foundation elements and as a dense data source for emerging digital-twin frameworks. Full article

To address the inherent limitations of the standard Sine Cosine Algorithm (SCA) in multi-threshold image segmentation, this paper proposes an enhanced algorithm named the Reinforcement Learning–Thermal Conduction–Sine Cosine Algorithm (RLTC-SCA), with symmetry as a core guiding principle. Symmetry, a fundamental property in nature and image processing, refers to the invariance or regularity of grayscale distributions, texture patterns, and structural features across image regions; this characteristic is widely exploited to improve segmentation accuracy by leveraging consistent spatial or intensity relationships. In multi-threshold segmentation, symmetry manifests in the balanced distribution of optimal thresholds within the grayscale space, as well as the symmetric response of segmentation metrics (e.g., PSNR, SSIM) to threshold adjustments. To evaluate the optimization performance of RLTC-SCA, comprehensive numerical experiments were conducted on the CEC2020 and CEC2022 benchmark test suites. The proposed algorithm was compared with several mainstream metaheuristic algorithms. An ablation study was designed to analyze the individual contribution and synergistic effects of the three enhancement strategies. The convergence behavior was characterized using indicators such as average fitness value, search trajectory, and convergence curve. Moreover, statistical stability was assessed using the Wilcoxon rank-sum test (at a significance level of $p = 0.05$) and the Friedman test. Experimental results demonstrate that RLTC-SCA outperforms all comparison algorithms in terms of average fitness, convergence speed, and stability, ranking first on both benchmark test suites. Furthermore, RLTC-SCA was applied to multi-threshold image segmentation tasks, where the Otsu method was adopted as the objective function. Segmentation performance was evaluated on multiple benchmark images under four threshold levels (2, 4, 6, and 8) using PSNR, FSIM, and SSIM as evaluation metrics. The results indicate that RLTC-SCA can efficiently obtain optimal segmentation thresholds, with PSNR, FSIM, and SSIM values consistently higher than those of competing algorithms—demonstrating superior segmentation accuracy and robustness. This study provides a reliable solution for improving the efficiency and precision of multi-threshold image segmentation and enriches the application of intelligent optimization algorithms in the field of image processing. Full article

Background: Shape memory alloy spring actuators offer significant potential for advanced actuation systems in exoskeletons, medical devices, and robotics, but adoption has been limited by slow activation speeds and insufficient design guidelines for achieving rapid response times while maintaining structural integrity. Objective: This study aimed to establish comprehensive design parameters for nickel–titanium spring actuators capable of achieving sub-second activation times through systematic experimental characterization and performance optimization. Methods: Nine different nickel–titanium spring configurations with wire diameters ranging from 0.5 to 0.8 mm and spring indices from 6 to 8 were systematically evaluated using differential scanning calorimetry for thermal characterization, mechanical testing for material properties, high-current electrical activation studies spanning 5–11 A, infrared thermal distribution analysis, and laser displacement sensing for dynamic response measurement. Results: Dynamic testing achieved activation times below 1 s for currents exceeding 5 A, with maximum displacement recoveries reaching 600–800% strain recovery, while springs with intermediate spring index values of 6.5–7.5 provided optimal balance between force output and displacement range, and optimal activation involved moderate current levels of 5–7 A for thin wires and 8–11 A for thick wires. Conclusions: Systematic geometric optimization combined with controlled high-current density activation protocols enables rapid actuation response while maintaining structural integrity, providing essential design parameters for engineering applications requiring fast, reliable actuation cycles. Full article

Bone defect repair presents a significant clinical challenge, especially for critical-sized defects, due to the limitation of conventional 3D-printed scaffolds to provide simultaneous mechanical support and bioactivity. Herein, this study developed a bioactive composite scaffold through a low-temperature rapid prototyping (LT-RP) 3D printing technology. The scaffold comprises a polyurethane (PU) matrix enhanced with bioactive manganese dioxide (MnO₂) nanoparticles, combining structural integrity with versatile bioactivity for bone repair. By incorporating 2, 6-pyridinedimethanol (PDM) into the PU molecular network, a coordination system is formed, enabling homogeneous distribution and structural integration of MnO₂ nanoparticles. As designed, the bioactive scaffolds are fabricated through LT-RP 3D printing technology with a regular porous architecture for improving cell growth. With 10 wt% MnO₂, the scaffolds (PPM10) have optimal comprehensive properties, with a modulus of ~14.1 MPa, improved thermal stability, good cytocompatibility, and enhanced osteogenic differentiation. Furthermore, in vitro degradation tests revealed the responsive release of Mn²⁺ from the PPM10 scaffolds in a glutathione-rich microenvironment. This functionality indicates the potential of the scaffolds to modify the tumor microenvironment for ultimate bone regeneration after bone tumor surgery. Full article

Accurate conjugate heat transfer (CHT) analysis is critical to the thermal management of hypersonic vehicles operating in rarefied environments, where non-equilibrium gas dynamics dominate. While numerous sophisticated CHT solvers exist for continuum flows, they are physically invalidated by rarefaction effects. This paper presents a novel partitioned coupling framework that bridges this methodological gap by utilizing the preCICE library to non-intrusively integrate the Direct Simulation Monte Carlo (DSMC) method, implemented in SPARTA, with the finite element method (FEM) via FEniCS for high-fidelity simulations of rarefied hypersonic CHT. The robustness and accuracy of this approach are validated through three test cases: a quasi-1D flat plate benchmark confirms the fundamental coupling mechanism against a reference finite difference solution; a 2D flat-nosed cylinder demonstrates the capability of the framework to handle highly non-uniform heat flux distributions and resolve the ensuing transient thermal response within the solid; finally, a standard cylinder case confirms the compatibility with curved geometries and its stability and accuracy in long-duration simulations. This work establishes a validated and accessible pathway for high-fidelity aerothermal analysis in rarefied gas dynamics, effectively decoupling the complexities of multi-physics implementation from the focus on fundamental physics. Full article

Wildfire-induced disturbances to soil and vegetation can significantly impact streamflows for years, depending upon the degree of burn severity. Accurately predicting the effects of wildfire on streamflow at the watershed scale is essential for effective water budget management. This study presents a novel approach to generating a burn severity map on a small scale by integrating unmanned aerial vehicle (UAV)-based thermal imagery with Landsat-derived Differenced Normalized Burn Ratio (dNBR) and upscaling burned severity to the entire burned area. The approach was applied to the Thompson Ridge Fire perimeter, and the upscaled UAV-Landsat-based burn severity map achieved an overall accuracy of ~73% and a kappa coefficient of ~0.62 when compared with the Burned Area Emergency Response’s (BAER) fire product as a reference map, indicating moderate accuracy. We then tested the transferability of burn severity information to a Beaver River watershed by applying Random Forest models. Predictors included topography, spectral bands, vegetation indices, fuel, land cover, fire information, and soil properties. We calibrated and validated the Distributed Hydrology Soil Vegetation Model (DHSVM) against observed streamflow and Snow Water Equivalent (SWE) data within the Beaver River watershed and measured model performance using Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and Percent Bias (PBIAS) metrics. We adjusted soil (maximum infiltration rate) and vegetation (fractional vegetation cover, snow interception efficiency, and leaf area index) parameters for the post-fire model setup and simulated streamflow for the post-fire years without vegetation regrowth. Streamflow simulations using the upscaled and transferred UAV-Landsat burn severity map and the Burned Area Emergency Response’s (BAER) fire product produced similar post-fire hydrologic responses, with annual average flows increasing under both approaches and the UAV-Landsat-based simulation yielding slightly lower values, by less than 6% compared to the BAER-based simulation. Our results demonstrate that the UAV-satellite integration method offers a cost- and time-effective method for generating a burn severity map, and when combined with the transferability method and hydrologic modeling, it provides a practical framework for predicting post-fire streamflow in both burned and unburned watersheds. Full article

In response to escalating environmental concerns, the construction industry is under growing pressure to adopt sustainable practices. As a major consumer of natural resources and a significant emitter of greenhouse gases, it paradoxically holds the potential to become a leader in green transformation. This study investigates the development of innovative, fire-resistant, and alkali-activated hybrid binder foams incorporating recycled materials: fly ash, coal slag, and ground brick waste, as sustainable alternatives to traditional building materials. The fire resistance performance at a technical scale and the thermal behavior of fiber-reinforced, alkali-activated hybrid binder foams synthesized from recycled aluminosilicate precursors were determined. The properties of unreinforced composite were compared with the composites reinforced with merino wool, basalt fibers, polypropylene fibers, and coconut fiber. Small-scale fire-resistance tests revealed that merino wool-reinforced composites exhibited the best thermal insulation performance, maintaining structural integrity, that is, retaining shape and continuity without delamination or collapse for 83 min under fire exposure. Analyses combining chemical characterization (X-ray fluorescence) with microstructural methods (computed tomography and colorimetry) confirmed that fire performance is strongly influenced not only by fiber type but also by pore distribution, phase composition, and oxide migration under thermal loading. These findings demonstrate the potential of fiber-reinforced foamed, alkali-activated hybrid binder as eco-efficient, printable materials for fire-safe and thermally demanding construction applications. Full article

Titanium dioxide (TiO₂) is a widely used photocatalyst, yet its activity is limited to ultraviolet light due to its large band gap. To extend absorption into the visible spectrum, this study developed a dual-sensitizer strategy by coupling perylene tetracarboxylic acid (PTCA) and copper phthalocyanine tetracarboxylic acid (CuPcTC) onto TiO₂. Both dyes were selected for their strong visible light absorption, photostability, and efficient charge transfer properties. Hybrid photocatalysts were prepared via an ultrasonication–coprecipitation method and incorporated into coatings. Optical, morpho-structural, thermal, and electrochemical methods were used to characterize the hybrid photocatalysts, while photocatalytic performances were evaluated by UV–Vis spectroscopy, hydroxyl radical generation, and Methylene Blue degradation under simulated solar light. The dual-sensitized TiO₂ composites exhibited broadened absorption across 400–750 nm, effective charge separation, and stable radical generation. Among the tested samples, the PTCA–CuPcTC hybrid (P3) demonstrated the highest activity, achieving efficient degradation of Methylene Blue with sustained performance over repeated cycles. Characterization confirmed uniform distribution of sensitizers, high crystallinity, and adequate thermal stability. These findings indicate that combining PTCA and CuPcTC provides synergistic benefits in light harvesting, charge transfer, and durability. The dual-sensitizer approach offers a promising route for visible-light-responsive photocatalysts in environmental remediation. Full article

Underground high-voltage transmission cables, especially high-pressure fluid-filled (HPFF) pipe-type cable systems, are critical components of urban power networks. These systems consist of insulated conductor cables housed within steel pipes filled with pressurized fluids that provide essential insulation and cooling. Despite their reliability, HPFF cables experience faults caused by insulation degradation, thermal expansion, and environmental stressors, which, due to their subtle and gradual nature, complicate incipient fault detection and subsequent fault localization. This study presents a novel, proactive, and retrofit-friendly predictive condition monitoring method. It leverages distributed accelerometer sensors non-intrusively mounted on the HPFF steel pipe within existing manholes to continuously monitor vibration signals in real time. A physics-enhanced convolutional neural network–long short-term memory (CNN–LSTM) deep learning architecture analyzes these signals to detect incipient faults before they evolve into critical failures. The CNN–LSTM model captures temporal dependencies in acoustic data streams, applying time-series analysis techniques tailored for the predictive condition monitoring of HPFF cables. Experimental validation uses vibration data from a scaled-down HPFF laboratory test setup, comparing normal operation to incipient fault events. The model reliably identifies subtle changes in sequential acoustic patterns indicative of incipient faults. Laboratory experimental results demonstrate a high accuracy of the physics-enhanced CNN–LSTM architecture for incipient fault detection with effective data feature extraction. This approach aims to support enhanced operational resilience and faster response times without intrusive infrastructure modifications, facilitating early intervention to mitigate service disruptions. Full article

Enhanced lubrication is critical for improving gear wear resistance. Current research on surface textures has overlooked the fundamental role of structural connectivity. Inspired by biological scales, a biomimetic hexagonal texture (BHT) was innovatively designed for tooth flanks, featuring dual-orientation grooves (perpendicular and inclined to the rolling-sliding direction) with bidirectional interconnectivity. This design synergistically combines hydrodynamic effects and directional lubrication to achieve tribological breakthroughs. A lubrication model for line contact conditions was established. Subsequently, the texture parameters were then optimized using response surface methodology and numerical simulations. FZG gear tests demonstrated the superior performance of the optimized BHT, which achieved a substantial 82.83% reduction in the average wear area ratio and a 25.35% decrease in tooth profile deviation variation. This indicated that the biomimetic texture can effectively mitigate tooth surface wear, thereby extending the service life of gears. Furthermore, it significantly improves thermal management by enhancing convective heat transfer and lubricant distribution, as evidenced by a 7–11 °C rise in bulk lubricant temperature. This work elucidates the dual-mechanism coupling effect of bio-inspired textures in tribological enhancement, thus establishing a new paradigm for gear surface engineering. Full article

Assessing the variation in the thermal conductivity of heterogeneous rock materials can be critical when upscaling models to simulate geothermal system operation, especially for petrothermal systems, where conduction dominates over convection. This study’s objective was to evaluate heterogeneity effects when assessing the thermal conductivity of geological materials, in this case, metamorphic rocks from Kuujjuaq (Canada), where the installation of a ground-coupled heat pump system is expected. Four core samples of gneissic rocks were analyzed in detail and compared to results obtained from a thermal response test. Thermal conductivity measurements in dry conditions were performed on the cylindrical surface of the samples with an optical thermal conductivity scanner. The 2D thermal conductivity spatial distribution was obtained by ordinary kriging interpolation method and used for numerical modeling to simulate steady-state conductive heat transfer along the sample vertical direction. Then, the effective thermal conductivity was computed according to Fourier’s law, using the simulated temperature to investigate the effect of scale variation with the heterogeneity. Results indicate the importance of distinguishing between the sample section’s effective thermal conductivity and local average thermal conductivity. Significant scale effects were identified with a variation ratio comprised between −10% and +16% when varying the length of the sample section. The representative elementary volume for the effective thermal conductivity was determined equivalent to half of the sample length. This volume gave a thermal conductivity that is equal to the harmonic mean of the laboratory-assessed values with a relative error <5%. A comparison between the in situ and laboratory-assessed thermal conductivity indicates that the thermal conductivity inferred from the thermal response test is adequate for sizing a geothermal system, assuming a range of variability equivalent to 1.5 times its standard deviation. Full article

To replicate delaminations at the coupon and substructural scales, simulated defects are often introduced into test specimens; therefore, understanding their behaviour within the laminate is essential. Full-field imaging is employed to investigate the effects of artificial defects in Carbon Fibre-Reinforced Polymer (CFRP) composites. Centre Crack Ply (CCP) specimens are used to evaluate the Mode II fracture toughness of laminated composites from a simple tensile test. Two batches of specimens are manufactured using IM7/8552. Artificial defects are introduced using a steel film insert of 5 µm thickness. For the first type of samples, the inserts were coated with Frekote release agent, while for the second type, the steel inserts were incorporated into the laminate without coating. Additionally, a third batch of specimens with a [0₄, 90]s layup is manufactured. Thermoelastic Stress Analysis (TSA) and Digital Image Correlation (DIC) are employed to obtain full-field temperature and displacement data from the tested samples. The inclusion of 90-degree plies enhances thermal contrast exploiting, their anisotropic mechanical and thermal properties. First, the specimens are tested under monotonic loading to failure, with DIC used to capture strain distributions at damage initiation and failure. In addition, acoustic emission is employed to evaluate damage initiation. Load drops provide an indirect evaluation of fracture toughness. Results show that full-field imaging is capable of establishing how the release agent and the layup configuration influence damage initiation and propagation. The non-adiabatic thermoelastic response is shown to be effective in observing subsurface damage. Finally, a novel approach to evaluate fracture toughness from the temperature increase at the failure event is proposed. Full article

Integration of thermographic imaging with in situ scanning electron microscopy (SEM) analysis may aid in quantifying thermal–mechanical behavior during tensile testing of 3D-printed polymers, which gives information about fracture mechanics, including the associated thermal phenomena. Upon fracture, samples exhibit changes in the thermal field, which is interesting because temperature fluctuations can affect material integrity. The paper introduces printing parameters to demonstrate a thermal measurement system’s sensitivity in detecting variations in mechanical response due to controlled changes in the process. Employing scientific methods, one can extrapolate results to a wider class of materials such as thermoplastics. Analysis of variance (ANOVA) is key in the design of experiments (DOE) if one wants to analyze the effect of factors and interactions. It has been used with the purpose of reducing the risk of type I errors (i.e., false positives). The finite element method (FEM) highlights temperature distribution in the area of interest and confirms recorded data. The particularly developed research experiments are carried out in a laboratory environment. Different samples are subjected to tensile tests under the evaluation of changes in the thermal field. SEM analysis is also widely used in fracture analysis to understand failure modes (ductile vs. brittle, crazing, delamination, and others). Thus, the paper aims to present a custom setup comprised a thermal camera pointed at samples during tensile testing that would serve as a reliable assessment system that accounts for the substitution of a sensor-based environment but is still fully capable of validating the measurement approach. Full article