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In the field of electronics thermal management (TM), there has already been a lot of work done to create cooling options that guarantee steady-state performance. However, electronic devices (EDs) are progressively utilized in applications that involve time-varying workloads. Therefore, the TM systems could dissipate the heat generated by EDs; however, there seemed to be a necessity for a design that would contain temperature rise within an acceptable range for limiting hot spots and managing thermal transients induced by higher-frequency operating cycles. Heat dissipation issues become more significant when miniaturization in electronics increases. More effective TM often results in enhanced reliability as well as a longer life expectancy for devices. Hence, this paper explicates the TM of EDs, the comparison of cooling methods, the comparison of convections for TM on EDs, the heat source (HS) mounted on the substrate board, and optimization techniques to optimize the size and position of HSs mounted on the substrate board. This paper also analyzes the TM technologies on different EDs from 2014 to 2023 and the comparison of the thermal conductance of EDs with two types of phase change materials (PCMs) and pin-fin heat pipes (HPs).

Wire electric discharge machining (WEDM) is a recent technique that is useful in machining Ti-6Al-4 V alloy, which is a material that is preferred in many industries due to its exceptional hardness. This paper aims to evaluate the effects of WEDM process parameters on the machining characteristics of Ti-6Al-4 V alloy. The 4-axis CNC WEDM machine that was used in this study had brass wire as the electrode and de-ionized water as the dielectric fluid. The parameters under investigation were the peak current (Ip), pulse on time (TON), pulse off time (TOFF), and servo voltage (SV) set at 3 levels each. The experimentation was based on Taguchi’s L9 orthogonal array design. The material removal rate (MRR) and surface roughness of machined ash components were Ra. A total of three Ra results were analyzed using ANOVA. It was shown that response surface methodology, pulse time ton and peak electric current had more significant effects on MRR. Effect-wise results indicated that peak current and time on P ring test allow surface finish to be within MRR levels. It is peak electric current that determines a 72.75% effect on MRR whereas extreme time has an 11.68 balanced effect on peak current. In the case of Ra, peak electric current and extreme pulse time remain dominant factors. The results suggest that higher Ra is favored by less increase in input energy as both peak current and time have been decreased.

This study investigates the complex relationships between process parameters and material properties in FDM-based 3D-printed biocomposites using explainable AI techniques. We examine the effects of key parameters, including biochar content (BC), layer thickness (LT), raster angle (RA), infill pattern (IP), and infill density (ID), on the tensile, flexural, and impact strengths of FDM-printed pure PLA and biochar-reinforced PLA composites. Mechanical testing was used to measure the ultimate tensile strength (UTS), flexural strength (FS), and impact strength (IS) of the 3D-printed samples. The extreme gradient boosting (XGB) algorithm was used to build a predictive model based on the data collected from mechanical testing. Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Partial Dependence Plot (PDP) techniques were implemented to understand the effects of the interactions of key parameters on mechanical properties such as UTS, FS, and IS. Prediction by XGB was accurate for UTS, FS, and IS, with R-squared values of 0.96, 0.95, and 0.85, respectively. The explanation showed that infill density has the most significant influence on UTS and FS, with SHAP values of +2.75 and +5.8, respectively. BC has the most significant influence on IS, with a SHAP value of +2.69. PDP reveals that using 0.3 mm LT and 30° RA enhances mechanical properties. This study contributes to the field of the application of artificial intelligence in additive manufacturing. A novel approach is presented in which machine learning and XAI techniques such as SHAP, LIME, and PDP are combined and used not only for optimization but also to provide valuable insights about the interaction of the process parameters with mechanical properties.

Wear is induced when two surfaces are in relative motion. The wear phenomenon is mostly data-driven and affected by various parameters such as load, sliding velocity, sliding distance, interface temperature, surface roughness, etc. Hence, it is difficult to predict the wear rate of interacting surfaces from fundamental physics principles. The machine learning (ML) approach has not only made it possible to establish the relation between the operating parameters and wear but also helps in predicting the behavior of the material in polymer tribological applications. In this study, an attempt is made to apply different machine learning algorithms to the experimental data for the prediction of the specific wear rate of glass-filled PTFE (Polytetrafluoroethylene) composite. Orthogonal array L25 is used for experimentation for evaluating the specific wear rate of glass-filled PTFE with variations in the operating parameters such as applied load, sliding velocity, and sliding distance. The experimental data are analysed using ML algorithms such as linear regression (LR), gradient boosting (GB), and random forest (RF). The R2 value is obtained as 0.91, 0.97, and 0.94 for LR, GB, and RF, respectively. The R2 value of the GB model is the highest among the models, close to 1.0, indicating an almost perfect fit on the experimental data. Pearson’s correlation analysis reveals that load and sliding distance have a considerable impact on specific wear rate as compared to sliding velocity.

The energy demand generated by fossil fuels is increasing day by day, and it has drastically increased after the COVID-19 pandemic as industries and household utilities rejuvenate. Renewable sources are thus becoming more essential as easily available, alternative methods of low-cost energy generation. Among these renewables, solar energy, i.e., solar power, is a promising energy source and can be used for solar-based H2 evolution because H2 technology is a leading source of eco-friendly electricity generation, and most of the worldwide efforts to develop this method involve heterogeneous catalysis for H2 evolution via water splitting and its storage, i.e., using a fuel cell. In the current scenario, there is a need to develop a stable, recyclable, and reusable heterogeneous catalyst system, which is a great challenge. In the current study, we have focused on novel ferrite magnetic nanomaterials for recyclable and reusable robust photocatalysis. Moreover, discussions of the factors contributing to the photocatalytic hydrogen evolution, low-cost synthesis techniques, and prospects for making them ideal photocatalysts are uncommon in the literature. The study will impart possible approaches for the design and development of novel ferrite nanomaterials and their nanocomposites for H2 generation in the forthcoming years.

Acid phosphatases (EC 3.1.3.2) are the enzymes that catalyse transphosphorylation reactions and promotes the hydrolysis of numerous orthophosphate esters in acidic media, as a crucial element for the metabolism of phosphate in tissues. Inorganic phosphate (Pi) utilisation and scavenging, as well as the turnover of Pi-rich sources found in plant vacuoles, are major processes in which intracellular and secretory acid phosphatases function. Therefore, a thorough understanding of these enzymes' structural characteristics, specificity, and physiochemical properties is required to comprehend the function of acid phosphatases in plant energy metabolism. Furthermore, acid phosphatases are gaining increasing importance in industrial biotechnology due to their involvement in transphosphorylation processes and their ability to reduce phosphate levels in food products. Hence, this review aims to provide a comprehensive overview of the purification methods employed for isolating acid phosphatases from diverse plant sources, as well as their structural and functional properties. Additionally, the review explores the potential applications of these enzymes in various fields.

Citric acid functionalized magnetic nanoparticles (CA-MNP) were synthesized and studied for sorption of two representative trivalent lanthanide and actinide ions (Eu 3+ and Am 3+ ). The material was characterized using various analytical techniques such as FTIR, SEM, TEM and zeta potential measurement etc. which indicated successful synthesis of magnetic nanoparticles and citric acid coating on its surface. The uptake study indicated efficient sorption of Eu 3+ (76.7%) and Am 3+ (60.9%) at optimum concentration of CA-MNP) 2.5 mg mL − 1 . Sorption of metal ion onto the substrate was confirmed by EDXRF results. The sorption process indicated better kinetics for Eu 3+ compared to previously reported studies and both the radionuclides were found to be following pseudo-second order rate kinetics. The rate constants were found to be 4.76 × 10 − 9 mg g − 1 min − 1 and 2.45 × 10 − 7 mg g − 1 min − 1 for Am 3+ and Eu 3+ , respectively. Thermodynamic study indicated ΔG values as − 15.8 kJ mol − 1 and − 17.9 kJ mol − 1 for Am 3+ and Eu 3+ , respectively indicating the spontaneity of the sorption processes. Stripping with different reagents viz. 0.1 M EDTA, 0.1 M oxalic acid, 0.1 M Na 2 CO 3 , and 0.1 M HNO 3 indicated best results with 0.1 M EDTA. >99% of loaded Eu 3+ is stripped in two stripping cycles while stripping of Am 3+ requires more number of cycles to quantitatively strip the loaded radionuclide.

New hydrophilic 2D graphene oxide (GO) nanosheets with various oxygen functional groups were employed to maintain high sensitivity in highly unfavorable environments (extremely high humidity, strong acidic or basic). Novel one-headed polymer optical fiber sensor arrays using hydrophilic GO and hydrophobic reduced graphene oxide (rGO) were carefully designed, leading to the selective sensing of volatile organic gases for the first time. The two physically different surfaces of GO and rGO could provide the sensing ability to distinguish between tetrahydrofuran (THF) and dichloromethane (MC), respectively, which is the most challenging issue in the area of gas sensors. The eco-friendly physical properties of GO allowed for faster sensing and higher sensitivity when compared to previous results for rGO even under extreme environments of over 90% humidity, making it the best choice for an environmentally friendly gas sensor.

Ti6Al4V alloy is widely used in aerospace and biomedical applications due to its excellent mechanical and thermal properties, but its poor machinability makes it a difficult-to-cut material. Electrical Discharge Machining (EDM) offers an effective non-conventional machining approach for such materials, where tool electrode selection and process parameters critically influence performance. This study presents a comprehensive experimental investigation into the effect of three tool electrodes—graphite, copper, and brass—on the EDM performance of Ti6Al4V alloy. Key input parameters, including pulse-on time (T on ), pulse-off time (T off ), and current, were selected based on equipment limits and prior studies. Taguchi’s L9 orthogonal array was used for experimental design, and analysis of variance (ANOVA) was employed to determine the statistical significance of each factor. Output responses—material removal rate (MRR), tool wear rate (TWR), surface roughness (SR), and dimensional deviation (DD)—were measured and optimized using the Teaching–Learning-Based Optimization (TLBO) algorithm. Among the electrodes, graphite achieved the highest MRR (31.03 mm³/min), lowest TWR (0.4648 mm³/min), and minimal DD (101.76 μm), while brass produced the smoothest surface (SR = 3.19 μm). A collection of non-dominated responses was also found using Pareto optimal points. A minor adequate deviance was observed between the TLBO algorithm’s predicted and actual findings. Scanning electron microscopy (SEM) analysis was conducted to evaluate surface morphology. The qualitative SEM results confirmed fewer defects and better surface integrity for graphite electrodes. The findings validate TLBO as an effective tool for EDM process optimization and provide practical guidance for electrode selection in machining Ti6Al4V.

Three different faults, namely, element fault, feed point fault, and feed network fault of antenna array are addressed. The ensemble ML detects the type of fault and location to diagnose a failure in an antenna array and also provides visualization. The machine learning (ML) algorithms, viz, Decision tree, Random forest, K-nearest neighbors, and Naïve Bayes, proven to be efficient in many other applications, are tried for fault detection of 4 × 4 planar antenna array. The 4 × 4 planar antenna array with fault scenarios is simulated using Ansys HFSS tool. The design center frequency of the array is 3.5 GHz. Ensemble of optimized ML algorithms enhances the performance in terms of accuracy and generalization. The proposed tuned stacking ensemble learning (TSEL) model outperforms the individual ML models, including the Support vector machine and tuned majority voting ensemble learning (TMVEL). The TSEL model provides 2% more accuracy than the TMVEL model. The accuracy attained for a single type of fault as well as three types of fault is 97% using 199 and 574 test samples, respectively. The visualisation of the detected fault(s) also is presented.