Search Results (114)

In order to analyze and solve the problem of excessive commutation spark of DC motor in ship electric propulsion system, which leads to a decrease in output power and low torque, this paper first establishes a mathematical model of the ship DC motor, builds its simulation model based on the mathematical model, and conducts simulation verification. Secondly, the Cassie arc model is introduced to model the commutation spark, and the Cassie arc model is connected in series in the armature winding of the DC motor to achieve virtual injection of excessive spark fault of the DC motor. Finally, the Fourier transform and wavelet analysis are used to process the data of the armature winding current and excitation current of the DC motor. The simulation results show that when an arc fault occurs in the DC motor, the ripple coefficient of the armature current and excitation current will increase, and the high-frequency component will increase. DB8 is an adopted wavelet function that decomposes the armature current and excitation current six times, and calculates the energy changes before and after the fault of each decomposed signal layer. It is found that without considering the approximate components, the D4 layer wavelet energy of the armature current and excitation current has the largest proportion in the detail components. The D1, D2, and D3 layers’ wavelet decomposition signals of the armature current and excitation current have significant energy changes; that is, the energy increase in the middle and high frequency parts exceeds 20%, and the D3 layer wavelet decomposition signal has the largest energy change, exceeding 40%. This can be used as a fault characteristic quantity to determine whether the DC motor has a large spark fault. This study can provide reference and guidance for online detection technology of excessive sparks in ship DC motors. Full article

The vulnerability of copper to corrosion in humid and saline environments remains a critical challenge for its long-term use. In this work, we present a streamlined and scalable approach for fabricating superhydrophobic, corrosion-resistant copper surfaces by integrating a simple wet chemical oxidation process with atmospheric pressure chemical vapor deposition (APCVD) of a perfluorinated silane. The hierarchical CuO nanostructures formed via alkaline oxidation serve as a robust layer, while subsequent silane functionalization imparts low surface energy, resulting in surfaces with water contact angles exceeding 170° and minimal contact angle hysteresis. Comprehensive surface characterization by SEM and roughness analysis confirmed the preservation of hierarchical morphology after coating. Wettability studies reveal a transition from hydrophilic to superhydrophobic behavior, with the Cassie–Baxter regime achieved on nanostructured and silane-functionalized samples, leading to enhanced droplet mobility and self-cleaning effect. Salt spray tests demonstrate that the superhydrophobic surfaces exhibit a corrosion rate reduction of 85.7% (from 2.51 mm/year for bare copper to 0.36 mm/year for the treated surface), indicating a seven-fold improvement in corrosion resistance compared to bare copper. This methodology offers a practical, reproducible route to multifunctional copper surfaces, advancing their potential for use in anti-fouling, self-cleaning, and long-term protective applications. Full article

The array of regular square pyramid microstructures with zero-spacing features is an ideal structural topology for building superhydrophobic functional surfaces due to its excellent anti-wetting performance and low surface adhesion properties. In the framework of existing studies, this microstructured array is usually considered to exist only in two typical wetting states, the stable Cassie state and the Wenzel state. In this study, a third type of wetting state, the incomplete Wenzel state, was discovered for the first time using experimental characterization, and the evolution mechanism of this new wetting state was revealed based on critical contact angle theory and numerical simulation. It is revealed that the faces and edges of the square pyramid microstructures exhibit different tilting angles, and this unique geometrical design endows them with dual critical contact angles. When the intrinsic contact angle of the microstructure is between the critical contact angles for the edges and faces, the wetting behavior of the droplet contact line in the directions parallel to the edges and faces will generate spontaneous and non-spontaneous competition effects, which lead to the formation of the incomplete Wenzel state. The dual-critical-angle theoretical model constructed in this study provides a new perspective for improving the theoretical system of wetting dynamics on pyramid arrays. Full article

Growing demand for chemically resistant, thermally stable, and anti-icing coatings has intensified interest in boron nitride (BN)-based materials and surface coatings. In this study, BN coatings were developed on mild steel (MS) via chemical vapour deposition (CVD) at 1200 °C for 15, 30, and 60 min, and their structural, surface, and water-repellent characteristics were evaluated. X-ray diffraction (XRD) and Fourier-transform infrared (FTIR) spectroscopy confirmed the successful formation of BN, while water contact angle measurements indicated high hydrophobicity, demonstrating excellent barrier properties. Scanning electron microscopy (SEM) revealed morphological evolution from flower- and needle-like BN structures in the sample placed in the CVD furnace for 15 min to dense, coral-like, and tubular networks in the samples placed for 30 and 60 min. These findings highlight that BN coatings, particularly the one obtained after 30 min of deposition, have a high hydrophobic character following the Cassie–Baxter model and can be used for corrosion resistance and anti-icing on MS, making them ideal for industrial applications requiring long-lasting protection. Full article

The adhesion of deleterious bacteria on titanium substrates not only causes economic losses but also endangers human life and health. The study is expected to address the challenging issues of using ZnO as an antibacterial material, including low bactericidal efficiency without lighting, susceptibility to ZnO cluster formation, and easy adhesion of bacteria to its surface. It is proposed that the prepared ZnO nanorod arrays with a hexagonal wurtzite structure on the surface of titanium-based materials can address the issue of ZnO cluster formation. Remarkably, a mere 3.49 g cm⁻² of decorated Ag/AgCl achieves over 99% sterilization efficiency without lighting. The incorporation of FAS (1H,1H,2H,2H-perfluorodecyltrimethoxysilane) molecules with low surface energy enables the prepared Ti@ZnO@Ag/AgCl@FAS to attain a Cassie–Baxter wetting state, thereby imparting exceptional bacterial anti-adhesion properties exceeding 99.50%. Furthermore, antibacterial and anti-adhesion models have been proposed to elucidate the underlying mechanisms. This innovative approach is anticipated to be adaptable for application across various material substrates, which opens up a new avenue for the application of the antibacterial and bacterial anti-adhesion properties on the surface of ZnO materials. Full article

As inspired by nature, wettability of bio-based material surfaces can be controlled by combining appropriate surface chemistries and topographies mimicking the structure of plant leaves or animals. The need for bio-based nanocellulose coatings with enhanced hydrophobic properties becomes technically relevant for extending their applications in the technological domain with better protection and lifetime of the coatings. In this work, the water repellence of spray-coated nanocellulose coatings with hydrophobically modified cellulose microfiber (mCMF coatings), or hydrophobically modified cellulose nanofiber (mCNF coatings) was enhanced after femtosecond laser patterning. In particular, the influences of different island-like pattern geometries and pattern sizes were systematically studied. The island-like patterns were experimentally created with single posts that have variable sizes of the valleys (B = 30 to 15 µm) and top surface area (T = 120 to 15 µm), resulting in good resolution of the patterns down to the size of the laser beam diameter (15 µm). Depending on the intrinsic homogeneity and porosity of sprayed mCMF and mCNF coatings, the quality and resolution of the island-like patterns is better for the mCNF coatings with thinner and more homogeneous sizes of the cellulose nanofibrils. The increase in apparent water contact angle on patterned nanocellulose coatings can be estimated from the theoretical Cassie–Baxter state of wetting and shows maximum values up to θ_s = 128° (mCMF coatings), or θ_s = 140° (mCNF coatings), for the smallest pattern sizes in parallel with minimum contact angle hysteresis of Δθ = 14° (mCMF coatings), or Δθ < 9° (mCNF coatings). The study demonstrated that femtosecond laser patterning technology provides high flexibility and adaptivity to create surface patterns in appropriate dimensions with enhanced hydrophobicity of nanocellulose coatings. Full article

Superhydrophobicity is closely linked to the chemical composition and geometric characteristics of surface roughness. Building on our structural studies on water and air–water interfaces, this work aims to elucidate the mechanism underlying the wetting transition from the Wenzel to the Cassie state on a hydrophobic surface. In the Wenzel state, the grooves are filled with water, meaning that the surface roughness becomes embedded in the liquid. To evaluate the effects of surface roughness on water structure, a wetting parameter (W_Roughness) is proposed, which is closely related to the geometric characteristics of roughness, such as pillar size, width, and height. During the wetting transition from Wenzel to Cassie states, the critical wetting parameter (W_Roughness,c) may be expected, which corresponds to the critical pillar size (a_c), width (w_c), and height (h_c). The Cassie state is expected when the W_Roughness is less than W_Roughness,c (<W_Roughness,c), which can be achieved by altering the geometric characteristics of the roughness, such as increasing pillar size (>a_c), decreasing width (<w_c), or increasing height (>h_c). Additionally, molecular dynamic (MD) simulations are conducted to demonstrate the effects of surface roughness on superhydrophobicity. Full article

Low-voltage AC power lines are prone to arc faults, and an arc current presents as a random and complicated signal. The amplitude of the line current remains relatively unchanged during the occurrence of series arcs, hence complicating the detection of series arc faults. In this work, we developed a low-voltage series arc fault test platform to analyze the digital features of low-voltage series arc currents and the morphology of arc combustion, as the current model fails to capture the high-frequency and randomness of arc currents. An analysis of the physical causes and influencing factors of the random distribution of AC arc zero-crossing times was conducted. A time-domain simulation model for arc fault currents was developed by enhancing the time constant of the Cassie arc model, while the high-frequency features of arc currents were simulated using a segmented noise model. The measured arc current data were utilized to validate the model through the analysis of the zero-crossing time distribution of arc current, the correlation coefficient of the arc current frequency-domain signal, and the similarity of the time-domain waveforms. When comparing the similarity of the simulated waveforms of the arc model presented in this research and those of other traditional arc models, it was found that the suggested model effectively characterizes the time-/frequency-domain features of low-voltage AC series arc fault currents. The suggested model enhances the features of randomness in low-voltage AC series arc faults and is important in extracting essential aspects and reliably recognizing low-voltage series arc faults. Full article

Superhydrophobic coatings are beneficial for applications like self-cleaning, anti-corrosion, and drag reduction. In this study, we investigated the impact of surface geometry on the static, dynamic, and sliding contact angles in the Cassie–Baxter state. We used fluoro-silane-treated silicon micro-post patterns fabricated via lithography as model surfaces. By varying the solid fraction (ϕ_s), edge-to-edge spacing (L), and the shape and arrangement of the micro-posts, we examined how these geometric factors influence wetting behavior. Our results show that the solid fraction is the key factor affecting both dynamic and sliding angles, while changes in shape and arrangement had minimal impact. The Cassie–Baxter model accurately predicted receding angles but struggled to predict advancing angles. These insights can guide the development of coatings with enhanced superhydrophobic properties, tailored to achieve higher contact angles and customized for different environmental conditions. Full article

Hyperspectral image (HSI) reconstruction is a critical and indispensable step in spectral compressive imaging (CASSI) systems and directly affects our ability to capture high-quality images in dynamic environments. Recent research has increasingly focused on deep unfolding frameworks for HSI reconstruction, showing notable progress. However, these approaches have to break the optimization task into two sub-problems, solving them iteratively over multiple stages, which leads to large models and high computational overheads. This study presents a simple yet effective method that passes the degradation information (sensing mask) through a deep learning network to disentangle the degradation and the latent target’s representations. Specifically, we design a lightweight MLP block to capture non-local similarities and long-range dependencies across both spatial and spectral domains, and investigate an attention-based mask modelling module to achieve the spatial–spectral-adaptive degradation representationthat is fed to the MLP-based network. To enhance the information flow between MLP blocks, we introduce a multi-level fusion module and apply reconstruction heads to different MLP features for deeper supervision. Additionally, we combine the projection loss from compressive measurements with reconstruction loss to create a dual-domain loss, ensuring consistent optical detection during HS reconstruction. Experiments on benchmark HS datasets show that our method outperforms state-of-the-art approaches in terms of both reconstruction accuracy and efficiency, reducing computational and memory costs. Full article

This paper presents the findings from an autonomous shuttle pilot program conducted at the University of North Carolina at Charlotte between June and December 2023 as part of the North Carolina Department of Transportation’s Connected Autonomous Shuttle Supporting Innovation (CASSI) initiative. The shuttle completed 825 trips, transporting 565 passengers along a 2.2-mile mixed-traffic campus route. The study evaluates the shuttle’s operational performance, reliability, and challenges using data from onboard sensors, system logs, and operator reports. Key analyses are divided into four areas: service reliability, which assesses autonomy disengagements caused by signal loss, technical issues, and environmental factors; service robustness, focusing on the shuttle’s ability to maintain operations under adverse conditions; performance metrics, including average speed, autonomy percentage, and battery usage; and service usage, which examines the number of trips and passengers to gauge efficiency. Signal loss and battery-related issues were the primary causes of service interruptions, while environmental factors like weather and vegetation also affected shuttle performance. Recommendations include enhancing vehicle-to-infrastructure communication and optimizing battery management. Full article

In this study, vinyl triethoxysilane (VTES), KH-560 and trimethylchlorosilane (TMCS) were used to modify the surface groups of commercially available nano-silica (SiO₂, 50 nm), and ethylene vinyl acetate copolymer (EVA) was used as a film-forming agent. EVA/SiO₂, EVA/V-SiO₂, EVA/K-SiO₂ and EVA/T-SiO₂ coatings were prepared, respectively. The coatings were characterized by SEM, FTIR, TG and contact angle. It was found that when the mass percentage of SiO₂ was 66 wt%, the hydrophobicity performance of the coating could be significantly improved by silica modification. Compared to the EVA/SiO₂, the water contact angle (WCA) of the EVA/V-SiO₂, EVA/K-SiO₂ and EVA/T-SiO₂ were increased by 24.0%, 14.4% and 24.6%, respectively. The FTIR results indicated that VTES, KH-560 and TMCS could effectively replace the -OH groups on the surface of the SiO₂ after hydrolysis, resulting in the presence of water transport groups on the SiO₂ surface. The TG results certified that TMCS had the highest substitution rate (24.6%) for the -OH groups on the SiO₂ surface after the hydrolysis. Additionally, the SEM results indicated that T-SiO₂ was more easily dispersed in the EVA film-forming agent, leading to a uniform micro–nano surface rough structure, which aligned with the Cassie–Wenzel model. The durability test had demonstrated that the EVA/T-SiO₂ maintained its hydrophobic properties even after enduring 40,000 drops of water and the impact of 200 g of sand. Furthermore, it exhibited excellent resistance to acid corrosion, along with superior self-cleaning properties and an anti-fog performance. It also provided outstanding protection against high temperatures and UV radiation for outdoor applications. Full article

Recent studies have shown correlations between the microbiota’s composition and various health conditions. Machine learning (ML) techniques are essential for analyzing complex biological data, particularly in microbiome research. ML methods help analyze large datasets to uncover microbiota patterns and understand how these patterns affect human health. This study introduces a novel approach combining statistical physics with the Monte Carlo (MC) methods to characterize bacterial species in the human microbiota. We assess the significance of bacterial species in different age groups by using notions of statistical distances to evaluate species prevalence and abundance across age groups and employing MC simulations based on statistical mechanics principles. Our findings show that the microbiota composition experiences a significant transition from early childhood to adulthood. Species such as Bifidobacterium breve and Veillonella parvula decrease with age, while others like Agathobaculum butyriciproducens and Eubacterium rectale increase. Additionally, low-prevalence species may hold significant importance in characterizing age groups. Finally, we propose an overall species ranking by integrating the methods proposed here in a multicriteria classification strategy. Our research provides a comprehensive tool for microbiota analysis using statistical notions, ML techniques, and MC simulations. Full article

Lygus lineolaris (Palisot de Beauvois) (Hemiptera: Miridae), a true omnivorous insect, poses a significant threat to agriculture in the Neartic region. Understanding the feeding behavior of L. lineolaris is crucial for developing integrated pest management strategies. This study aimed to evaluate the effects of different diets on the fitness of L. lineolaris, with a focus on the diet source, feeding regime (phytophagy, zoophagy, and phytozoophagy), and number of diet items. The experimental design in the laboratory investigated the impacts of strawberry, canola and buckwheat flowers, as well as spider mites and aphids to explore relationships found in a conventional strawberry field. Results reveal that diet source, feeding regime, and the number of diet items influence L. lineolaris performance (i.e., survivorship rate, developmental time, and adult weight and length). Improvements in fitness are indicated by higher nymphal survival, shorter developmental time, and larger adults. Immature stages of L. lineolaris show improved fitness when provided with diets rich in canola compared to strawberry flowers and spider mites. Furthermore, the inclusion of multiple diet items in phytozoophagous regimes enhances insect performance. The findings emphasize the significance of understanding L. lineolaris’ nutritional requirements and the biodiversity of target ecosystems for modeling energy flows and designing effective IPM strategies against this pest. This research contributes to the knowledge base for biological control programs targeting L. lineolaris in agricultural systems. Full article

The effect of extreme water repellency, called the lotus effect, is caused by the formation of a Cassie–Baxter state in which only a small portion of the wetting liquid droplet is in contact with the surface. The rest of the bottom of the droplet is in contact with air pockets. Instrumental methods are often used to determine the textural features that cause this effect—scanning electron and atomic force microscopies, profilometry, etc. However, this result provides only an accurate texture model, not the actual information about the part of the surface that is wetted by the liquid. Here, we show a practical method for estimating the surface fraction of texture that has contact with liquid in a Cassie–Baxter wetting state. The method is performed using a set of ethanol–water mixtures to determine the contact angle of the textured and chemically equivalent flat surfaces of AlSI 304 steel, 7500 aluminum, and siloxane elastomer. We showed that the system of Cassie–Baxter equations can be solved graphically by the wetting diagrams introduced in this paper, returning a value for the texture surface fraction in contact with a liquid. We anticipate that the demonstrated method will be useful for a direct evaluation of the ability of textures to repel liquids, particularly superhydrophobic and superoleophobic materials, slippery liquid-infused porous surfaces, etc. Full article