Search Results (144)

Internal diameter atmospheric plasma spraying (ID-APS) commonly employs an inherently inclined nozzle configuration to overcome geometric interference in confined cylindrical components. This non-orthogonal deposition condition breaks the symmetry of the plasma jet and produces asymmetric thickness distributions, making uniform coating formation difficult to control using conventional models developed for planar or external spraying. In this study, a kinematic-based mathematical model was developed from experimentally measured single-path deposition data obtained under representative internal spraying conditions. A skew-normal formulation was introduced to describe the asymmetric cross-sectional profile, and a superposition framework was established to relate kinematics and geometric constraints to coating quality metrics, including mean thickness, profile uniformity, flatness, and lateral distance. The effects of kinematic parameters and workpiece geometric characteristics were systematically analyzed, and the resulting model was implemented on an internal cylindrical surface to predict spatial thickness evolution. Experimental validation was conducted at both macroscopic and microscopic scales through surface reconstruction and cross-sectional microscopy, confirming that the proposed approach can capture the main features of coating buildup and provide reliable estimates of thickness uniformity. The developed framework offers a practical tool for process design and quality control in ID-APS, reducing dependence on empirical parameter tuning and enabling more consistent thickness control on internal surfaces. Full article

Water-sensitive papers (WSPs) are widely used for spray deposition assessment because they are inexpensive, simple to use, and suitable for field conditions. Combined with image analysis, they provide quantitative information on spray coverage and indirect insight into deposition structure. However, their interpretation is often oversimplified, particularly when percent coverage is treated as the sole indicator of spray quality. This paper presents a critical methodological review of image-based evaluation of spray deposition using WSPs, with emphasis on coverage-related metrics, spatial descriptors, droplet size estimation, and the main sources of uncertainty affecting their interpretation. The review also positions WSPs relative to other spray characterization techniques and discusses their practical role as proxy-based tools rather than direct measurement instruments. Representative WSP samples from previous field experiments are used exclusively to illustrate typical processing steps and methodological pitfalls, not to report new experimental results. In addition, the paper summarizes major segmentation approaches, discusses the interpretative value of selected deposition descriptors, and formulates practical recommendations for image acquisition, binarization, metric selection, sample exclusion, and reporting practice. It is concluded that WSP-based image analysis is most valuable for comparative and diagnostic assessment of spray deposition, provided that its methodological constraints are explicitly recognized and consistently reported. Full article

The growing penetration of renewable energy sources has intensified the demand for safe, sustainable, and cost-effective energy-storage technologies. Aqueous lithium-ion batteries are promising candidates because of their intrinsic safety and high ionic conductivity, though their deployment is limited by narrow electrochemical stability window of water. Lithium titanate oxide (LTO) has emerged as an ideal anode material for aqueous systems because of its exceptional structural stability, negligible volume change during lithiation/delithiation, and relatively high operating potential that suppresses hydrogen evolution. This review examines the peer-reviewed literature (2010–2026) on LTO-based aqueous lithium-ion batteries, focusing on the interdependence between material synthesis, electrode fabrication, electrolyte engineering, and electrochemical performance. Scalable fabrication techniques, such as spray deposition and tape casting, are discussed alongside their pact on electrode quality. Attention is given to water-in-salt, gel-polymer, and localized high-concentration electrolytes that expand the stability window and improve interfacial behavior. Overall, the review highlights how electrolyte design, electrode architecture, and processing methods can be jointly tailored to support stable and scalable LTO-based aqueous lithium-ion batteries systems. Full article

With the growing security requirements of sensor nodes in Internet of Things (IoT) systems, conventional silicon-circuit-based physical unclonable functions (PUFs) still face limitations in circuit overhead, design complexity, and system integration. To address these challenges, this paper proposes a lightweight gas sensor PUF (GS-PUF) design based on a Ni-doped SnO₂ nanoscale gas sensor array. The proposed method exploits both the unavoidable process randomness introduced during sensor fabrication and the device-to-device electrical response variations induced by gas–material interactions as entropy sources, thereby enabling high-quality PUF response generation. At the device level, Ni-SnO₂ nanomaterials are prepared by electrostatic spray deposition (ESD), and an indirectly heated gas sensor array is constructed to enhance the sensitivity and stability of the sensing response. At the algorithmic level, a random resistance balancing algorithm based on multi-sensor combinational comparison is proposed. By randomly comparing the summed resistances of multiple sensor clusters, a 128-bit multi-bit PUF response is generated, while the uniformity and independence of the output bits are effectively improved. Experimental results demonstrate that the proposed GS-PUF exhibits excellent randomness, uniqueness, and reliability: the information entropy of the PUF responses is greater than 0.99, approaching the ideal value; the probabilities of output bits “1” and “0” are 0.4988 and 0.5012, respectively, indicating a well-balanced distribution; the inter-device uniqueness reaches 49.8%, close to the ideal value of 50%; all items in the NIST randomness test suite are passed, with all p-values exceeding 0.01 and the minimum p-value being 0.0368, confirming a high level of statistical randomness confidence. In addition, long-term measurements under fixed laboratory conditions show that the PUF response reliability remains above 96%. Compared with other sensor-based PUFs, the proposed method provides a lightweight sensing-security integration approach for IoT sensor nodes by reusing intrinsic gas-sensor response variations and avoiding an additional dedicated silicon PUF circuit. Full article

Hydrogen storage remains a key challenge in the transition toward sustainable energy systems, particularly for applications requiring high energy density and safe operation. Among various solid-state storage materials, magnesium hydride (MgH₂) is considered highly promising due to its high hydrogen capacity, low cost, and good reversibility; however, its practical application is hindered by slow kinetics and high thermodynamic stability. In this study, Mg and Ni coatings were deposited on Al₂O₃ based substrates using a direct current plasma spraying technique to develop a composite system for enhanced hydrogen storage performance. The influence of plasma torch parameters on coating characteristics was investigated, and the hydrogenation behavior was analyzed under controlled conditions (350 °C & 200 °C, 5 atm H₂). The structural, morphological, and compositional evolution of the coatings before and after hydrogenation was examined using SEM, EDS, XRD, and FTIR techniques. Results demonstrate that plasma-sprayed Mg coatings undergo significant morphological transformation after hydrogenation, including surface cracking, increased porosity, and phase conversion to MgH₂, confirming effective hydrogen uptake. In contrast, Ni coatings exhibit limited hydride formation but play a catalytic role by facilitating hydrogen dissociation and improving surface reactions. The influence of plasma power on coating quality and hydrogenation efficiency was also identified, with higher power leading to improved coating uniformity and enhanced MgH₂ formation. Additionally, a reaction–diffusion model was developed to evaluate the effect of temperature and hydrogen pressure on hydride layer growth. The model predicts an optimal temperature range (~300–330 °C) for MgH₂ formation, beyond which thermodynamic instability limits hydride stability. Overall, the study demonstrates that plasma-sprayed Mg/Ni coatings on granular substrates represent a promising approach for developing efficient hydrogen storage systems, combining improved kinetics, structural stability, and scalable processing. Full article

The quality of Surface-Enhanced Raman Chemical Imaging (SER-CI) rely on several parameters, among which the uniform deposition of metallic nanoparticles impacts greatly the result. Optimizing deposition protocols for biological samples is challenging due to inherent spatial heterogeneity, preventing the distinction between deposition artefacts and true analyte distribution. However, to optimize the deposition parameters, it is necessary to have a controlled experimental model. This study presents the development of a repeatable dried gelatine–agarose hydrogel as a controlled analytical substrate with the uniform spatial homogeneity of diphenhydramine hydrochloride as the experimental model for further nanoparticle deposition optimization. With its skin-mimicking Raman fingerprint, the proposed hydrogel enables the systematic evaluation of deposition techniques without biological variability. Confocal Raman imaging performances are as follows: the normalization-based ratio (I₁₀₀₃/I₁₄₆₉) achieved an intra-day RSD of 3.6–8.2%, inter-day RSD of 6.5%, and intra-day pixel-wise RSD (%) of 8.3–12.3%. The Distribution Homogeneity Index (DHI) confirmed the analyte’s uniform distribution. Drying kinetics modelling revealed a diffusion-based dehydration process, with repeatable batch production. Application of dried hydrogels for SERS chemical imaging confirmed diphenhydramine hydrochloride detectability inside the polymeric matrix, with the proportionality of intensity based on the diphenhydramine hydrochloride concentration. A preliminary performance comparison of nanoparticle deposition by drop-casting and spray-coating demonstrates the applicability of the developed model. This standardized matrix provides a reference platform for evaluating deposition homogeneity, distinguishing method performance from sample artefacts and accelerating chemical imaging method development and performance through optimization. Full article

Unmanned aerial vehicles (UAVs) are an alternative to traditional pesticide applications in orchards. Particularly, drones are an example of UAVs that have increased in popularity in recent years; however, relatively few studies have evaluated how spraying operation modes interact with other drone parameters within a single experimental framework. This study evaluated the effects of operation mode, application volume, flight height, and droplet size on spray coverage, droplet density, droplet spectra, and droplet size uniformity using the spraying drone DJI Agras T40 under a simulated canopy structure. A four-factorial experimental design was used; treatments included three operation modes (i.e., standard mode, fruit-tree mode, and spinning mode), two application volumes (i.e., 37.4 L/ha and 74.8 L/ha), two flight heights (i.e., 3 m and 5 m), and two droplet sizes (i.e., 150 μm and 300 μm). Operation mode was among the most influential factors affecting spray deposition quality. The spinning mode achieved the highest overall spray coverage (20.81%) and droplet density (172.44 drops/cm²), while the standard mode provided the most uniform spatial distribution. Results from the interaction analyses indicated that the parameter combination that produced the highest spray coverage within the tested ranges was an application volume of 74.8 L/ha, a flight height of 3 m, and a droplet size of 150 μm in the standard mode. For the fruit-tree mode, the highest spray coverage was observed at an application volume of 74.8 L/ha, a flight height of 5 m, and a droplet size of 300 μm. For the spinning mode, the combination associated with the highest spray coverage was 74.8 L/ha, 3 m, and 300 μm. In conclusion, the results provide data-driven guidance on how drone operational parameters influence spray deposition and can support future validation under commercial orchard conditions. Full article

This study seeks to elucidate the precise modulation of laser-assisted cold spray (LACS) particle deposition and to provide guidance for optimizing process parameters in LACS. While LACS has been shown to improve coating quality, the underlying roles of laser-induced thermal softening in particle deformation, impact energy redistribution, and interfacial bonding of 6061 Al alloy remain unclear. Here, multiscale finite element simulations and experiments were combined to investigate single-particle impact and coating build-up under different laser powers. The results indicate that laser assistance enhances thermal softening, leading to stronger radial spreading, more pronounced jetting, and a larger bonding interface. The simulations show that laser heating expands the thermal softening zone and shifts impact energy dissipation from the particle to the substrate, thereby reducing elastic rebound and promoting stable deposition. TEM analysis confirms dynamic recrystallization at the particle interface under all conditions, while higher laser power broadens the recrystallized region from approximately 0.7 μm to about 1.5 μm and promotes grain growth without causing additional oxidation. Moreover, coating porosity decreases from 3.1% to 1.0% with increasing laser power, whereas nanohardness decreases from 1.43 GPa to 1.24 GPa due to the increased contribution of thermal softening. Overall, the study demonstrates that the beneficial effect of laser assistance originates from thermally activated interfacial localization and energy redistribution, offering a mechanistic framework for optimizing the deposition of difficult-to-deposit aluminum alloys. Full article

Thermal barrier coatings (TBCs) are an effective means of providing thermal insulation and protecting the hot-section components of gas turbine engines. Their quality and performance characteristics largely depend on the microstructural features and the bond strength between the bonding layer and the substrate. The present study aims to determine the optimal plasma spraying parameters that ensure the formation of NiCrAlY coatings with superior microstructural integrity and adhesion strength. The objective of the study is a thermally sprayed nickel–chromium–aluminum–yttrium (NiCrAlY) bond coat deposited onto an Inconel 718 nickel-based superalloy, which is widely used in aircraft gas turbine engines due to its high strength and excellent oxidation resistance at elevated temperatures. It was found that the coating produced under the optimized conditions exhibited a significantly higher adhesion strength compared with the samples obtained under other spraying regimes. The results confirm that a precise adjustment of the atmospheric plasma spraying (APS) process parameters, taking into account the equipment configuration, allows for a substantial improvement in coating quality and performance. Full article

The delicate fruits of highbush blueberry are exposed to factors causing mechanical damage and yield losses during cultivation, harvesting, and postharvest handling. Foliar stimulation with silicon-based formulations may improve fruit firmness and postharvest quality, thereby increasing the market value of the produce. This study evaluated the effect of foliar silicon fertilisation on highbush blueberry fruit quality in terms of changes in mechanical properties, taking into account the applied spraying technique. The experiments were conducted using standard flat-fan and air-induction nozzles at different spraying speeds and varying spray liquid pressures. Treatment quality was assessed based on the degree of spray deposition, determined through analysis of silicon content in leaves and fruits. Instrumental compression and fruit detachment tests were performed to determine safe ranges of pressures and forces from the perspective of harvest quality. The results showed that the loads exerted by the picker’s hand during manual harvesting of the cultivar ‘Patriot’ remain within safe limits but are close to the critical threshold of fruit mechanical resistance (2 N). The greatest increases in destructive force and fruit firmness were obtained with the use of standard XR nozzles, reaching 3.19–3.34 N (up to 19%) and 2.03–2.21 N (up to 10%), respectively, compared with the control treatment. These findings provide practical guidance for optimising foliar silicon applications and spray parameters in highbush blueberry cultivation to improve fruit mechanical resistance and reduce the risk of harvest and postharvest damage. Full article

Unmanned aerial vehicles (UAVs) have revolutionized plant protection spraying due to their high efficiency and adaptability. However, the lack of rapid, portable tools for assessing droplet deposition remains a bottleneck for optimizing spray quality and improving pesticide utilization. The main purpose of this study is to develop a portable, image-based detection device with improved algorithms for real-time analysis (<3 s per card) of droplet deposition on spray cards during UAV plant protection spraying, addressing the limitations of existing methods in portability, real-time capability, and field robustness. This study presents a portable detection device integrated with advanced image processing algorithms for real-time analysis of droplet deposition on copperplate paper cards during UAV operations. The device employs a Raspberry Pi 5 as the core processor, coupled with a high-resolution camera and a standard chessboard calibration board for field-portable image acquisition. Key innovations include an adaptive background subtraction and local contrast enhancement method to address variable field lighting conditions, and an improved adhesion droplet segmentation algorithm combining iterative morphological opening operations with refined distance transform-based concave point matching. Validation on 21 field-collected cards using ImageJ as reference demonstrated a droplet extraction accuracy of 89.4%, with coverage rate improvements of 25.4% and 15.2% compared to OTSU and block thresholding methods, respectively. The adhesion segmentation relative error averaged 6.3%. This low-cost, lightweight device provides farmers and researchers with an effective tool for on-site spray quality evaluation, contributing to precision agriculture and reduced pesticide waste. Full article

Weed management during the seedling stage is a critical component of soybean production. Efficient weed control can significantly improve crop yield and crop quality. However, conventional spraying techniques exhibit low pesticide utilization and contribute to environmental pollution. To address these challenges, this study proposes a deep learning-based precision target spraying method. A lightweight YOLOv5-MobileNetv3-SE model was developed by replacing the backbone feature extraction network and incorporating an attention mechanism. Field images of weeds were collected to construct a dedicated dataset, and the detection performance of the model was evaluated. Furthermore, a grid-based matching spraying algorithm was developed to synchronize target detection with spray actuation. The system time delay, including image processing delay, communication and control delay, and spray deposition delay, was analyzed and measured, and a time-delay compensation strategy was implemented to ensure accurate spraying. Experimental results demonstrated that the improved model achieved an mAP@0.5 of 86.9%, a model size of 7.5 MB, and a frame rate of 38.17 frames per second. The weed detection accuracy exceeded 92.94%, and spraying accuracy exceeded 85.88% at forward speeds of 1–4 km·h⁻¹. Compared with conventional continuous spraying, the proposed method achieved pesticide reduction rates of 79.0%, 72.5%, 55.8%, and 48.6% at weed coverage rates of 5%, 10%, 15%, and 20%, respectively. The proposed method provides a practical approach for precise herbicide application, effectively reducing chemical usage and minimizing environmental impact. Full article

The quality of the feedstock powder plays a key role in determining the properties of coatings produced by cold spray (CS). However, most commercially available powders are not specifically designed for CS, which makes it difficult to tailor powder characteristics for optimal performance. In this study, we examined the cold sprayability of five copper (Cu) powders manufactured using electrolysis, gas atomization, and mechanical grinding. The powders were characterized in terms of their microstructure, particle shape, and size distribution to evaluate how the production method influences powder properties. Powder flowability was measured using a shear cell test, while mechanical properties and deformability relevant to CS were assessed through nano-indentation. The results showed that gas-atomized powders with equiaxed grain structures offered the best combination of flowability and deformability, making them the most suitable for CS. Their spherical particle shape resulted in a lower surface area compared to the irregular electrolytic powder, which reduced inter-particle surface forces and allowed for smoother powder flow. Nano-indentation measurements indicated that the mechanically ground powder with ultra-fine grains and the gas-atomized powder containing fine dendrites had the highest nano-hardness values (HIT = 2.1 ± 0.15 GPa and 1.6 ± 0.1 GPa, respectively). In contrast, the porous electrolytic Cu powder showed the lowest hardness (HIT = 0.7 ± 0.2 GPa). These trends were confirmed by microstructural analysis of the deposited coatings. Coatings produced from the irregular electrolytic powder exhibited limited particle deformation, weak inter-particle bonding, and the highest porosity. Conversely, spherical gas-atomized powders produced much denser coatings. In particular, the powder with the most uniform spherical shape and no microsatellite particles resulted in the lowest coating porosity due to its superior deformation behavior upon impact. Full article

It has become common to apply multiple drones to conduct plant-protection in large-scale farms, where dual-UAV synchronisation is representative. However, current studies are mainly dedicated to the spray quality of a single UAV, and it remains unclear whether synchronous operation affects spray effectiveness. This paper focuses on the spray efficacy and coupling effects of dual-UAV collaboration. Five-factor orthogonal four-level tests were conducted using the developed UAV collaboration system, and the results were compared with those of asynchronous and ideal linear superposition. It is indicated that (1) spray uniformity was impacted by the relative height between the UAVs and the flight speed of the UAVs (all the p-values < 0.02), whilst the deposition amount was affected by the relative horizontal spacing between the UAVs and the height of the left UAV relative to the forward flight direction (all the p-values < 0.04); (2) the proportion of high-quality spray in the coupling areas had a negative relation with the relative horizontal distance of the two UAVs, and the threshold of the effective coupling distance was 5 m; and (3) synchronous coupling should be avoided. If it is not, the left-side UAV (referring to the forward direction of flight) should be at a higher altitude (5 m or 6.5 m), be 0.5 m higher than the right and fly with a low or medium flight speed (3.5 m/s–4.5 m/s). The research can give a reference to the real spray operation by multiple UAVs. Full article

Measuring the spray parameters and providing feedback on the quality of the spraying is critical to ensuring that the spraying material reaches to the appropriate region. A novel software entitled DropSense was developed to determine spray parameters quickly and accurately compared to DepositScan, ImageJ 1.54d and Image-Pro 10 software. Water-sensitive papers (WSP) were used to determine spray parameters such as deposit coverage, total deposits counted, D_V10, D_V50, D_V90, density, deposit area and relative span values. Upon execution of the developed software, these parameters were displayed on the computer screen and then saved in an Excel spreadsheet file at the end of the image analysis. A conveyor belt system with three different belt speeds (4, 5 and 6 km h⁻¹) and four nozzle types (AI11002, TXR8002, XR11002, TTJ6011002) were used for carrying out the spray experiments. The novel software was developed in the LabVIEW programming language. Compared WSP image results related to the mentioned spray parameters were statistically evaluated. The results showed that the DropSense software had superior speed and ease of use in comparison to the other software for the image analysis of WSPs. The novel software showed mostly similar or more reliable performance compared to the existing software. The core technical innovation of DropSense lay in its integration of advanced morphological operations, which enable the accurate separation and quantification of overlapping droplet stains on WSPs. In addition, it performed fully automated processing of WSP images and significantly reduced analysis time compared to commonly used WSP image analysis software. Full article