Applied Surface Science Advances最新文献

In this experimental investigation, a Physical Vapor Deposition (PVD) process was employed to deposit TiAlN coating onto a Si substrate. The nitrogen flow rate, bias voltage, and substrate-to-target distance were selected as input parameters, each with three different levels. The design of these input parameters was structured according to Taguchi's L9 Orthogonal Array (OA). Following deposition, the mechanical, microstructural, structural, and electrochemical properties of the TiAlN coating were meticulously characterized and analyzed to discern the influence of the selected parameters on its various properties. Microstructural analysis revealed a homogeneous structure throughout the film. Additionally, the mechanical properties of the film exhibited notable performance under the specified parameters. However, it was observed that no consistent trend could be identified across different properties concerning the applied parameters. To elucidate the complex relationships among these variables, the Least Squares Method (LSM) regression analysis technique was employed. This analytical approach facilitated the establishment of correlations among the diverse parameters, enhancing the understanding of their collective impact on the TiAlN coating properties. The understanding of analytical results will be useful for predicting the values between the two extremities to measure the performance parameters where the experimental results are not available.

Improving the adhesion of nanosized copper films to a glass substrate is vital for their application in electronics and medicine, as it enhances their overall reliability. For this purpose, we employed Ar plasma etching (240 s) and magnetron sputtering to create copper layers on a glass substrate. Furthermore, we investigated the effect of subsequent solid state dewetting (at 300 °C) of Cu nanolayers on the interface stability. Increasing the sputtering time resulted in elevated copper concentration, UV-Vis absorption, conductivity, and surface roughness. The as-deposited and dewetted samples exhibited very good wettability with water contact angles below 60°. Importantly, plasma treatment improved the adhesion of the Cu layers to the glass. Subsequent dewetting accelerated surface diffusion and the oxidation of Cu atoms, causing structural and morphological changes. The presence of CuO after dewetting caused loss of the surface plasmon resonance (SPR) band in the UV-Vis spectrum and a decrease in sample conductivity due to the transformation of the copper layer from a metal to an oxide. Biological testing revealed a more pronounced bactericidal effect for the as-deposited Cu layer against E. coli and S. epidermidis on contrary to dewetted samples. The similar cytotoxic trend was observed for human dermal fibroblasts and hepatocytes. Nonetheless, biological testing confirmed better cell adhesion on dewetted Cu layers compared to the as-deposited ones. Therefore, our copper nanostructured samples could find application as antibacterial coatings of biomedical devices.

Selected area electrochemical etching (EE) and electrochemical deposition (ED) are widely used for fabricating microstructures and devices, but they require complex processes and expensive equipment and generate significant electrolyte waste. This study demonstrates a novel single-droplet electrochemical system using a triboelectric nanogenerator (TENG) for self-powered selected area EE and ED reactions. Utilizing TENG's pulsed power, Al nanostructures were created by EE, while nano-Ag and Cu₂O nanocubes were synthesized by ED. The generated nanomaterials were applied to detect trace chemicals through the Surface-Enhanced Raman Scattering effect. The electrochemical reaction area can be controlled by droplet size, and patterns can be created using a needle movement platform. The size and density of nanostructures can be adjusted by the TENG's current, collision frequency, and electrolyte concentration. The deposition gradient from the center to the edge of the droplet is controlled by the distance between the needle and the substrate. COMSOL Multiphysics calculations show that a smaller D creates a larger electric field gradient. However, the varied deposition gradients were attributed to competition of electric field, diffusion effects, and capillary flow. This proposed green technology offers low cost, simplicity, no waste electrolyte, and self-powering capabilities, pioneering new research directions in EE and ED.

Orthopedic disorders are increasing in our society due to population aging. Numerous biomaterials have been developed to support bone regeneration, however showing a strong discrepancy between in vitro and in vivo results. This has been attributed to a lack of knowledge about protein adsorption, an early step occurring after biomaterial implantation. Bioactive glass S53P4 is clinically accepted for orthopedic applications pertaining to its osteoconductive and osteogenic behavior. However, its interactions with proteins are still unclear. To better understand the impact of surface chemistry on the glass-protein interactions, bare and silanized S53P4 were placed in contact with fibronectin (fn), in static and dynamic conditions. The surfaces were characterized by zeta potential, confocal microscopy and FTIR-ATR spectroscopy.The impact of fn on the cell response was assessed by live-dead, proliferation and morphology tests, using human adipose stem cells (hASCs). Both S53P4 and silanized-S53P4 showed good cell viability. Fn was found to affect cell alignment on both bare and silanized substrates. The impact of the surface treatments on osteogenesis was evaluated studying the expression of relevant osteogenic markers (hDLX5, hRUNX2A, hOSTERIX), which was particularly promoted by the concomitant action of silanization and fn coating.

Pattern transfer by plasma etching is a traditional standard technology in microelectronics and other micron technologies. These technologies require vacuum conditions, which limit throughput, size, and low-cost fabrication. Recent developments in low cost atmospheric plasma technologies may be suitable to realize pattern transfer without vacuum conditions. Reactive atmospheric plasma jet etching has been used to transfer aluminum mask patterns to fused silica. Aluminum line patterns of 2.5 to 50 µm width on fused silica wafer are exposed to a static as well as a scanning CF₄/O₂ reactive atmospheric plasma jet with a footprint diameter of 0.85 mm (full width at half maximum), resulting in etching only the SiO₂ and causing a nearly isotropic etch with an etch rate of about 200 nm/s. As a result, line narrowing, trapezoidal line cross-sections, and under-etching were observed. The successfully transferred line patterns with the demonstrated widths and depths are of technological interest in various fields of application. Therefore, this approach enables low-cost patterning of fused silica through the use of reactive atmospheric plasma jet etching for micron-scale pattern transfer. This advancement addresses the limitations of both traditional vacuum-based and wet etching methods.

Wettability is a crucial surface feature of polymers due to their numerous interaction-destined applications. This study focuses on the application of sand blasting process for investigating the wettability of polymeric materials to produce hydrophobic behavior. Four different polymeric materials, Acrylonitrile Butadiene Styrene (ABS), Poly(methyl methacrylate) (PMMA), Polypropylene (PP), and Polycarbonate (PC) underwent sand blasting with varying process parameters, following a comprehensive plan for the design of experiments. Subsequent analyses included surface roughness measurement and wettability tests, supplemented by scanning electron and confocal microscopy observations to gain deeper insights into the blasted surfaces. A predictive model based on a machine learning algorithm was developed using the backpropagation technique to correlate the surface treatment parameters to surface roughness and wettability indexes. From the experimental results sand blasting proved to be efficient in creating hydrophobic surfaces on all the tested materials. The developed neural network demonstrated high fitting degrees between the predicted and measured values. ABS exhibited the most hydrophobic behavior and emerged as a strong candidate for further investigations.

Plasma electrolytic oxidation (PEO) produces an oxide coating containing pores and cracks lowering corrosion protection. The defects can be sealed by in-situ or post-treatment methods. This work compares the sealing effect of SiO₂ particles and post-alkali treatment on the corrosion resistance of PEO coatings formed on AZ31 magnesium (Mg) alloy. PEO was conducted in a phosphate-based electrolyte containing 2 g/l nanoparticle SiO₂ at a constant current density of 300 A/m² for 10 min. The post-alkali treatment was performed in 0.5 M NaOH solution at 80 °C for 30 min. The corrosion resistance was evaluated using polarization, electrochemical impedance spectroscopy, and weight loss tests. The SiO₂ particles were successfully embedded uniformly in the Mg₃(PO₄)₂ coating, enhancing the coating compactness and stability. The reinforced coating exhibited ten times higher impedance modulus and lower corrosion current density. The post-alkali treatment improved corrosion resistance but not as high as the SiO₂ reinforcement. The impedance modulus of the alkali-treated specimen increased five times, and the corrosion current density decreased three times of the base coating. The weight loss test consistently showed that the SiO₂-reinforced coating generated lower mass loss during 14 days of immersion in simulated body fluid.

In the pipe industry, pressure pipes have long made use of High-Density Polyethylene (HDPE), which is used extensively. Currently, HDPE pipes are installed in higher numbers in comparison with other plastic pipes. The purpose of this study is to evaluate and compare the predictive capabilities of two methods, including the finite element method (FEM) and artificial neural network (ANN) techniques, for predicting the tensile strength of HDPE pipes used in water distribution systems. Attempts have been made to improve prediction models to better predict the mechanical behavior of these pipes by improving our understanding of the structure and surface characteristics as well as the interactions between the interface and the operating environment. The results show that experimental trial results are in perfect agreement with machine learning techniques. The findings of this study highlight the benefits of using ANN to predict the behavior of HDPE pipes, which may have significant ramifications for the plastics and water distribution industries.