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The machinability of High-Density Polyethylene (HDPE) reinforced with zirconia (ZrO 2 )-coated MWCNTs via Abresive Waterjet Machining (AWJM) was investigated in this study. The composite was fabricated with 3 wt% ZrO 2 -MWCNTs to enhance mechanical and thermal properties. A Taguchi L 9 orthogonal array was employed to evaluate the effects of waterjet pressure, traverse speed, and stand-off distance on surface roughness (Ra), kerf taper (KT), and material removal rate (MRR). Grey Relational Analysis (GRA) was applied for multi-objective optimization, integrating the individual responses into a single Grey Relational Grade (GRG). Results indicated that lower traverse speed (100 mm/min) minimized R a (4.286 μm), while higher pressure (200 MPa) and intermediate speed (150 mm/min) reduced kerf taper (0.04727 radians). The optimal parameter combination yielded the highest GRG (0.7361), balancing superior surface finish, dimensional accuracy, and machining efficiency. This study provides critical insights into precision AWJM of polymer nanocomposites for high-performance engineering applications.

To prevent water scarcity, wastewater must be discharged to the surface or groundwater after being treated. Another method is to reuse wastewater in some areas after treatment and evaluate it as much as possible. In this study, it is aimed to recover and reuse the caustic (sodium hydroxide, NaOH) used in the recycling of plastic bottles from polyethylene terephthalate (PET) washing wastewater. Chemical substances used in the industry will be significantly reduced with chemical recovery from wastewater. Ultrafiltration (UP150) and nanofiltration (NP010 and NP030) membranes were used for this purpose in our study. Before using nanofiltration membranes, pre-treatment was performed with coagulation-flocculation process to reduce the pollutant accumulation on the membranes. Different coagulants and flocculants were used to find suitable coagulants and flocculants in pre-treatment. The pre-treated wastewater using aluminum oxide, which supplied the highest chemical oxygen demand (COD) removal (76.0%), was used in a dead-end filtration system to be filtered through NP010 and NP030 membranes at different pressures (10–30 bar). In the same filtration system, raw wastewater was filtered through a UP150 membrane. Among these treatment scenarios, the best method that could remove pollutants and provide NaOH recovery was selected. After each treatment, pH, conductivity, COD, and NaOH analyses were performed. The maximum NaOH recovery (98.6%) was obtained with the UP150 membrane at 5 bar.

This study presents a comprehensive investigation into the design and optimization of capacitive pressure sensors (CPSs) for their integration into capacitive touch buttons in electronic applications. Using the Finite Element Method (FEM), various geometries of dielectric layers were meticulously modeled and analyzed for their capacitive and sensitivity parameters. The flexible elastomer polydimethylsiloxane (PDMS) is used as a diaphragm, and polyvinylidene fluoride (PVDF) is a flexible material that acts as a dielectric medium. The Design of Experiment (DoE) techniques, aided by statistical analysis, were employed to identify the optimal geometric shapes of the CPS model. From the prediction using the DoE approach, it is observed that the cylindrical-shaped dielectric medium has better sensitivity. Using this optimal configuration, the CPS was further examined across a range of dielectric layer thicknesses to determine the capacitance, stored electrical energy, displacement, and stress levels at uniform pressures ranging from 0 to 200 kPa. Employing a 0.1 mm dielectric layer thickness yields heightened sensitivity and capacitance values, which is consistent with theoretical efforts. At a pressure of 200 kPa, the sensor achieves a maximum capacitance of 33.3 pF, with a total stored electric energy of 15.9 × 10−12 J and 0.468 pF/Pa of sensitivity for 0.1 dielectric thickness. These findings underscore the efficacy of the proposed CPS model for integration into capacitive touch buttons in electronic devices and e-skin applications, thereby offering promising advancements in sensor technology.

The present study demonstrates the synthesis of compact ZnO layers using CdS sensitized on ZnO as a photoanode with copper sulfide (Cu 2 S) and carbon as a counter electrode (CE). In this study, a compact ZnO layer was fabricated using the simple and low-cost successive ionic layer adsorption and reaction (SILAR) method, and Cu 2 S CE films were synthesized using the chemical bath deposition method. Various characterizations, such as X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), confirmed the formation of ZnO and CdS sensitizations on the ZnO . UV-visible spectroscopy revealed that the bandgaps of the ZnO and Cu 2 S films were 3.2 and 1.3 eV, respectively. Furthermore, the morphology of the ZnO films was optimized by varying the number of SILAR cycles. Scanning electron microscopy revealed the formation of a nanorod compact layer (CL) and the porous nature of the ZnO photoanode films. However, the porosity increased with the number of SILAR cycles. Various parameters, such as the current density, voltage, fill factor, and efficiency, were measured using the J-V characteristics. The highest 0.85% efficiency was achieved by using the ZnO compact film with 30 SILAR cycles for the Cu 2 S CE. Furthermore, the study revealed that the Cu 2 S counter electrode had a higher electrocatalytic response than the carbon CE.

Glass fiber-reinforced polymers (GFRPs) are widely used in domestic applications such as doors, windows, and furniture, where drilling is a common machining process. The surface roughness of drilled hole walls is a critical factor, particularly in fastening applications, where smooth finishes are essential. This study explores the influence of drilling parameters on the surface roughness of GFRPs reinforced with different types of eggshell fillers viz.,un-carbonized, carbonized, and hybrid along with an unfilled variant. The research employs a Central Composite Design within the Response Surface Methodology (RSM) framework to investigate the effects of material type, spindle speed, feed rate, and point angle on surface roughness. Material type and point angle were treated as categorical variables, while spindle speed and feed rate were continuous, each with four levels, resulting in 16 experimental runs. The results showed that surface roughness values varied from 3.04 to 4.99μm, depending on the specific combination of drilling parameters. Statistical analysis using Analysis of Variance (ANOVA) confirmed that spindle speed and feed rate significantly impact surface roughness, with roughness increasing at higher speeds and feeds. Notably, the carbonized eggshell-filled GFRP variant achieved the lowest surface roughness. The study also developed a highly accurate regression model, validated through experimental data. The novel use of different variants of eggshell fillers in GFRPs provides a sustainable and effective way to enhance material properties. Further, conducting the drilling studies to observe the outcomes, offers potential industrial applications in the production of high-quality, durable composite materials.

Corrosion is one of the major issues faced by marine industries. Two of the major metals used in these industries include copper and AISI 5140 steel. This iterates the importance of understanding the microstructure and its influence on the corrosion behavior of these metals in 3.5 wt% NaCl that is studied here. Annealing treatment was performed for both the metals, and the microstructure before and after the annealing treatment was performed using an optical microscope and SEM. X-ray diffraction (XRD) of the metals before and after heat treatment was performed, and it was found that the annealing treatment caused an increase in the crystallite size irrespective of the metal. The samples were subjected to Vicker's microhardness testing, and a decrease in the hardness was achieved post-annealing. The electrochemical studies further proved that there is an improvement in corrosion resistance post-annealing. The kinetic and thermodynamic parameters are described using Arrhenius and transition state theories.

The prospective utilization of electrospun nanofibers across diverse fields has elicited substantial scientific attention. Nevertheless, managing their diameter remains problematic due to the intricate interactions among electrospinning variables. This research explores the application of Long Short-Term Memory (LSTM) networks and multiple regression models to forecast the diameters of Titanium Dioxide (TiO₂) and Polyvinyl pyrrolidone (PVP) nanofibers, facilitating improved process regulation and enhancement. TiO₂ + PVP nanofibers were fabricated under diverse conditions, including changes in applied voltage, solution concentration, and distance between tip to collector. The acquired data underwent analysis using LSTM and regression models to assess their predictive capabilities. The outcomes revealed that both approaches effectively estimated nanofiber diameters; however, the regression model surpassed LSTM with a lower error rate of 0.077% compared to 0.305%. This indicates that while LSTM captures nonlinear relationships, conventional regression models yield more precise predictions in this scenario. These findings underscore the potential of machine learning in advancing electrospinning technology by minimizing trial-and-error experiments and boosting nanofiber production efficiency. The incorporation of artificial intelligence-driven modeling into the electrospinning process sets the stage for more accurate control over fiber morphology, resulting in enhanced material properties and expanded applications in biomedical, environmental, and energy sectors.

This study presents a multi-modal investigation into the wear behavior of bio-based epoxy composites reinforced with multi-walled carbon nanotubes (MWCNTs) at 0–0.75 wt%. A Taguchi L16 orthogonal array was employed to systematically assess the influence of MWCNT content, load (20–50 N), and sliding speed (1–2.5 m/s) on wear rate (WR), coefficient of friction (COF), and surface roughness (Ra). Statistical analysis revealed that MWCNT content contributed up to 85.35% to wear reduction, with 0.5 wt% identified as the optimal reinforcement level, achieving the lowest WR (3.1 mm3/N·m) and Ra (0.7 µm). Complementary morphological characterization via SEM and AFM confirmed microstructural improvements at optimal loading and identified degradation features (ploughing, agglomeration) at 0 wt% and 0.75 wt%. Regression models (R2 > 0.95) effectively captured the nonlinear wear response, while a Random Forest model trained on GLCM-derived image features (e.g., correlation, entropy) yielded WR prediction accuracy of R2 ≈ 0.93. Key image-based predictors were found to correlate strongly with measured tribological metrics, validating the integration of surface texture analysis into predictive modeling. This integrated framework combining experimental design, mathematical modeling, and image-based machine learning offers a robust pathway for designing high-performance, sustainable nanocomposites with data-driven diagnostics for wear prediction.

This study presents an integrated framework combining supervised classification and composition-driven regression modeling for automated phase identification and quantification in steel microstructures. SEM micrographs of three commercially used steels EN3, EN353, and 20MnCr5 were acquired at magnifications of 5000×, 10,000×, and 20,000×. Images were segmented using the SLIC algorithm into 64 × 64 patches, from which six Gray Level Co-occurrence Matrix (GLCM) features were extracted: contrast, correlation, energy, homogeneity, dissimilarity, and angular second moment (ASM). The proposed framework provides a preliminary demonstration of interpretable classification and composition-linked regression modeling for phase prediction in steels, with future work required to validate its generalizability across broader steel systems. Using these features, a Random Forest classifier achieved 70% classification accuracy and a macro F1-score of 0.61 in identifying four phases: ferrite, pearlite, distorted pearlite, and bainite. Patch-wise predictions (972 in total) were aggregated to evaluate steel-specific phase trends. Distorted pearlite was predominant in EN3 and EN353, while bainite appeared mainly in 20MnCr5. A regression model was developed to predict global phase percentages from alloying elements (C, Mn, Cr, Ni) and magnification level, achieving strong agreement with machine learning predictions (R² = 0.88 for pearlite and 0.83 for distorted pearlite), moderate agreement for bainite (R² = 0.69), and weak agreement for ferrite (R² = 0.07). This hybrid framework exhibits potential for microstructural classification of texture-based classification and composition-informed modeling in capturing microstructural complexity. The approach lays groundwork for scalable microstructure analysis for steel evaluation and supports data-driven microstructure design and analysis.

This study evaluates the morphological effects of TiO2 nanoparticles, nanofibers, and a bilayer configuration on electronic devices, such as Dye-Sensitized Solar Cells (DSSCs) and UV sensors. Cost-efficient natural dyes—curcumin, coffee beans, and banana peel—were used as sensitizers for nanomaterial films. TiO2 nanoparticles were synthesized using the sol–gel technique, while nanofibers were produced via electrospinning. Characterization techniques, including Scanning Electron Microscopy (SEM), Energy Dispersive x-ray Spectroscopy (EDS), and x-ray Diffraction (XRD), confirmed the formation and dimensions of the TiO2 nanostructures. UV–visible spectroscopy was used to determine the optical properties of the samples. TiO2 nanofibers and nanoparticles exhibited high surface-area-to-volume ratios, with nanofibers having a diameter of 20 nm and particles measuring 50 nm. A binder-free, low-temperature paste was prepared using TiO2 nanoparticles and nanofibers to develop thin films. The turmeric dye showed peak absorption at 470 nm with a band gap energy of 2.06 eV when loaded on a TiO2 bilayer film. This study aims to develop electronic devices that reduce costs and enhance performance by using low-cost, efficient, and economically viable dyes. TiO2 nanofiber and nanoparticle films show promise for cost-effective and high-performance electronic devices.