Applied Surface Science Advances最新文献

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.

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.

Incremental sheet forming (ISF) is a controlled and die-less forming process that can produce customized products from the sheets layer by layer using the same setup, i.e., forming tools and machine. This novel and emerging forming technique can potentially be useful as a green aspect of manufacturing by reducing the required power and by saving the resources for producing the components of lightweight materials because material is deformed locally during ISF. The surface quality of the formed parts from sheet material affects the aesthetics aspects, stress concentration, fatigue life and their applicability. This review article aims to provide a state-of-the-art review for various aspects and parameters of ISF process that affect the surface roughness significantly by conducting literature survey quantitatively. Furthermore, the techniques of ISF and roles of various parameters responsible for affecting the surface quality of parts have also been explored for providing the critics for existing literature and setting the further guidelines for the researchers. Various instruments used for measuring the surface roughness of parts formed by ISF has also been discussed. Comparative analysis of exiting literature available in the context of various process parameters, ISF hardware, and surface roughness makes this study comprehensive and exhaustive for the researchers to find the gaps and challenges in this field. The relationship of wall angle with other process parameters can be explored in the future to obtain the desired surface quality of a particular material. Significant parameters responsible for the surface quality have also been discussed critically. Further, the new tool paths can be developed for high surface quality and low forming time. A study on the selection of feed rate for various applications and relation with other parameter is also recommended for the future work. The lubricants with additives can further be tested for high surface quality of ISF. Further, significant study on surface roughness is not conducted on hybrid sheet forming and other advanced variants of ISF (like hot forming, friction stir assisted incremental forming, waterjet forming). It is required to develop the comparisons between the different techniques of ISF. Results also showed that majority of researchers used SPIF technique followed by DSIF and hybrid ISF.

Silicones such Sylgard 184 are widely employed in biological applications due to their versatile properties. However, their inherently adhesive surfaces can restrict their application, especially in direct contact with damaged biological tissues, potentially compromising patient comfort. To enhance the surface properties of Sylgard 184 while maintaining its transparency in the visible spectrum, a novel low-temperature method (70 °C) has been developed. This method involves immersing PDMS in a solution of titanium (IV) ethoxide in THF, thus inducing swelling of the silicone's polymer network, followed by the diffusion and condensation of titanium (IV) ethoxide within the polymer matrix. The resulting hybrid material, incorporating amorphous titanium oxide within the silicone network, exhibits significantly increased surface hardness compared to unmodified Sylgard 184, while retaining transparency and improving biological behaviour. The elaborated method holds promising potential for enhancing the performance of silicone-based materials in diverse biomedical applications.

Flexible piezoelectric nanogenerator (PENG) and triboelectric nanogenerators (TENG) have gained prodigious attention due to the increasing demand of nano and micro energy for driving of miniaturized electronic devices, sensors, and various internet of things. The key challenges that are currently in focus are material selection and simple fabrication techniques for improved electrical performance along with good mechanical properties and flexibility. Herein, a ferroelectric polymer, poly(vinylidenefluoride-co-trifluoroethyne) (PVDF-TrFE), is chosen as a flexible material due to its promising prospect for energy harvesting. To improve the performance, a ceramic material, 0.65Pb(Mg_1/3Nb_2/3)O₃–0.35PbTiO₃ (PMN-PT), with very high piezoelectric properties has been selected as the reinforcement. Further, reduced graphene oxide has been added as a conducting filler to promote charge conduction. A remarkable enhancement in output voltage of nearly 3 fold is achieved in PVDF-TrFE/PMN-PT (PP) polymer composite as compared to the base polymer PVDF-TrFE (P) TENG device. Furthermore, the PVDF-TrFE/rGO/PMN-PT (PPR) as a PENG illustrates a great improvement in output current of the order of 2 as compared to the pristine polymer. The maximum output voltage as shown by the TENG is 200 V and the maximum current that is shown by the PENG is 30 µA. Therefore, the fabricated PMN-PT based PVDF-TrFE nanogenerators have an immense prospect for applications in self-powered systems.

Several anticorrosive treatments have been proposed over time to create protective layers to hinder the corrosion phenomenon. In recent years, organic molecules from plant extracts and expired drugs have been tested due to their potential corrosion inhibition properties. However, direct corrosion inhibition efficiency (IE%) evaluation requires costly reactants and a specific experimental setup. Quantitative-structure activity relationship (QSAR) proposes modeling IE% in terms of variables measured in previous experiments or determined by theoretical approaches. Computed descriptors, such as ionization energy (I), electronic affinity (A), or global hardness, were added to a database of physicochemical properties. This work compares several methodologies to obtain precise yet portable mathematical models for predicting corrosion inhibition efficiency. As an original approach from this research group, nonlinear autoregressive moving average with exogenous inputs (NARMAX), using forward regression with orthogonal least squares (FROLS), models were implemented as a robust method to get nonlinear portable models and to determine the most important variables impacting IE%. Contrastingly, ordinary least squares (OLS) methodology was employed with the novelty of applying power series expansions from the promoted FROLS variables for linear and polynomial regression with only one independent variable, which resulted in clearer graph visualization of trends and the ease of proposing thumb rules based on raw information. Finally, IBM Watson was also compared as a robust yet non-portable and highly parametrized alternative to conventional mathematical approaches, based on extra trees regressor (ETR). The models were compared using mean absolute percentage error (MAPE), mean-squared error (MSE), and root-mean-squared error (RMSE). Overall, models with fewer variables and up to second-order terms show improved performance. The main tendencies of IE%, drawn by inferences for 630 substances by second-order NARX, are analyzed. Also, the determinant role of the highest occupied molecular orbital energy was reported. Experimentalists can take advantage of a “cost-free” general approach that can obtain estimations for IE% values with errors of about 6 %, in particular the second-order NARX model.