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The optical and thermal properties of nanoparticles have attracted much interest due to their extinction effectiveness in industries like electronics, medical devices, photonics, materials sciences, drug delivery, hyperthermia, etc. In this work, the optical response of eight different geometrical nanostructures, namely Solid-CuNS, Au–Ag-coated-Solid-CuNS, Hollow-CuNS, Au–Ag-coated-Hollow-CuNS, Solid-CuNR, Au–Ag-coated-Solid-CuNR, Hollow-CuNR, and Au–Ag-coated-Hollow-CuNR, were studied for hyperthermia. Finite element analysis was performed in COMSOL Multiphysics to simulate the optical and thermal responses of the nanostructures. The size range of nanostructures was selected differently based on the hyperthermia condition to attain a temperature between 42 °C and 46 °C. An appropriate temperature distribution of 43.8 °C was achieved in the 500 nm liver domain using an Au–Ag-coated-Solid-CuNS with Dsphere = 20 nm. The minimum thermal response was 42.1 °C for both Solid-CuNS and Solid-CuNR when Dsphere was 35 nm and Lcylinder was 35 nm. The highest electric field distribution of 11.2 V/m in the surrounding area was seen for Au–Ag-coated-Solid-CuNR, with an extinction resonance peak at 475 nm. On the other hand, the Au–Ag-coated-Solid-CuNS showed the least electric field distribution of 2.22 V/m with a corresponding resonance peak of 480 nm when Dsphere was 15 nm.

Waspaloy, a nickel-based super alloy, has much potential in gas turbine component production. Machining this type of material using the traditional manufacturing method is very hard. Hence, electrical discharge machining (EDM) was selected for the investigation of the machining characteristics of Waspaloy by choosing the control parameters, namely, current, voltage, pulse on time, pulse off time, and spark gap. Gray relational analysis is carried out using 18 experiments according to an orthogonal array (L18) to determine the gray relation grade for this machining process with multiple machining characteristics such as material removal rate (MR), tool wear rate (TW), and surface roughness (SR). The significant objective of the multi objective optimization to attain high MR, low TW, and low SR is found out. From gray relation optimization, the optimum machining conditions for EDM, namely, current: 26 A, voltage: 200 V, pulse on time: 120 μs, pulse on time: 10 μs, and spark gap: 0.15 m, have been identified. The foremost parameter affecting the responses was found to be peak current.

Offering intended products at an affordable price is a highly challenging task in a business environment. To meet such challenges, industrial experts and researchers have different approaches, such as shifting the purchase of materials from import mode to local vendors and optimizing machining cost by optimizing process parameters, waste reduction, rework reduction, and technological improvements. The novelty of this study lies in reducing material cost by accurate materials forecasting to minimize storage cost, ablation cost, average annual consumption cost, etc. This study aims to compare the efficacy of the Seasonal Decomposition Method (SDM) against Multi-Echelon Inventory Optimization (MEIO) for minimizing inventory through accurate materials forecasting. Through rigorous evaluation and analysis, this research seeks to clarify the strengths and weaknesses of each approach, thereby providing insights into their applicability and effectiveness in addressing inventory management challenges across diverse seasonal demand patterns. The sample size was taken as 50 per group. The G-power applied is about 80%. The significance value obtained is 0.001 (p < 0.05), indicating a statistically significant difference between the two algorithms used for inventory reduction and materials forecasting. SDM (63.43) outperforms MEIO (48.57) in terms of accuracy. SDM yielded a better accuracy when compared to MEIO for inventory minimization and materials forecasting.

Poor planning and scheduling increase buying, storage, and obsolescence expenses. Material shortages increase labor, machine optimum time, etc. Industrial raw materials, semi-finished items, spares, and consumables have distinct consumption patterns, reorder points, purchase lead times, quantity limits, discounts, etc. To save money, machine learning predicts demand and prepares materials. This study employs ARIMA or Support Vector Machine (SVM) machine learning-based forecasting approaches to forecast materials for less inventory. Feature engineering eliminates seasonality, time series, and external demand and ignores data irregularities, missing figures, and disparities. This approach needs to adapt traits to factors, separate test and training data, and consider many future models to represent the best forecasts. Forecast reliability and consistency were examined for each model. Inventory management systems were evaluated for computational complexity and installation ease and found implementation issues. Both models’ input data and resilience were examined using sensitivity analysis. Accurate prediction SVM and ARIMA predict material demand differently. Meaningful statistics show the optimal model. Performance differences between SVM and ARIMA enhance model selection. Thinking about the execution of high inventory system integration and computational complexity, response surface methodology chooses factorial variables with the highest or lowest responses. Analysis of variance, factor analysis, and effect modeling expansions demonstrated for the response.

The present work was related to synthesis of graphene (0, 1.25 and 2.5 %) incorporation in cobalt-zinc ferrites by using sol–gel auto combustions method. The face center cubic (FCC) crystal structure and crystallite size varies from 16 to 18 nm was examined by XRD analysis. While as the irregular, non-uniform and flake like surface morphology of cobalt zinc/graphene nanocomposite (Co-Zn-G NCs) were observed via SEM images. In addition, the phase identification and presence of different functional groups such as M−O (metal oxide), OH, C-C, C = O and C = C were investigated by Raman spectroscopy and FTIR analyses. Further, I-V two probe method was used to observed the resistivity decrease (7.5x 108 to 2.5x 106 ohm-cm) and conductivity increased (1.2x10-9 to 3.8x10-7S/cm) of Co-Zn-G NCs. After that the thermoelectric behavior of Co-Zn-G NCs indicated that due to increase the temperature then seebeck coefficient decrease as vice versa. The overall result shows that the 2.5 % graphene incorporation in Co-Zn ferrites expressed the excellent electrical and thermoelectric properties. In future the Co-Zn-G nanocomposite used for the development of temperature sensor, reduce eddy current loss and spintronic devices.

Thin film technology has gained significant attention in the industry due to its superior properties in comparison to bulk materials. Specifically, iron nitride-based thin films are a fascinating area of research in semiconductor technologies. The crystal structure and optical properties of these films are highly dependent on the growth method, deposition technique, gas flow, pressure, and several other factors. In this work, we prepared polycrystalline thin films of iron nitride on silicon (100) substrates by using the active screen low temperature plasma-assisted method. These films are deposited under the varying atmosphere of nitrogen and hydrogen in four proportions: 80% N2 + 15% H2; 60% N2 + 35% H2; 40% N2 + 55% H2; and 20% N2 + 75% H2, along with 5% argon to investigate its influence on the structural, morphological, and optical properties. The structural properties are determined by x-ray diffraction and revealed a mixture of ζ-Fe2N, ε-Fe3N, and γ′-Fe4N phases. Surface morphology is analyzed by scanning electron microscopy, which confirms the formation of a thin layer on the substrate. In addition, UV–Vis spectrophotometry is used to assess the optical response and bandgap energy of the samples. The result of UV–Vis optical diffuse reflectance spectra shows a decrease in the bandgap from 3.17 to 3.06 eV of substrate. For the S4 sample, we obtained a pure Fe4N phase, showing a significant decrease in the energy bandgap.

An efficient technique was used to model corona discharges without incorporating flux corrections. A position-state separation (POSS) technique was employed to solve the convection-dominated continuity equations prevalent in designing efficient corona discharges. The suggested approach utilizes an Eulerian scheme to solve the convective acceleration, diffusion, and response subproblems. The exceptional performance of POSS in terms of computing cost, resilience, and resolution is demonstrated by a series of numerical tests in various dimensions and coordinate systems. MATLAB was used to run four tests, namely, the square test, Davies test, general advection-diffusion test, and corona test. The average error in POSS was calculated as 0.06, which is much lower than the error in FEM-FCT (0.2677) and FVM-MUSCL (0.2650) algorithms. A 1 mm thick peak of space charge was formed around the anode after 1.2 ns. For an outer radius of 2 cm, ripples in ionizing waves were seen at t = 5000 ns. All the ionizing peaks were produced at around 0.5 cm away from the anode.

Large-scale extreme-ultraviolet waves commonly exhibit as single wave front and are believed to be caused by coronal mass ejections. Utilizing high spatiotemporal resolution imaging observations from the Solar Dynamics Observatory, we present two sequentially generated wave trains originating from the same active region: a narrow quasiperiodic fast-propagating (QFP) wave train that propagates along the coronal loop system above the jet and a broad QFP wave train that travels along the solar surface beneath the jet. The measurements indicate that the narrow QFP wave train and the accompanying flare’s quasiperiodic pulsations (QPPs) have nearly identical onsets and periods. This result suggests that the accompanying flare process excites the observed narrow QFP wave train. However, the broad QFP wave train starts approximately 2 minutes before the QPPs of the flare, but it is consistent with the interaction between the unwinding jet and the solar surface. Moreover, we find that the period of the broad QFP wave train, approximately 130 s, closely matches that of the unwinding jet. This period is significantly longer than the 30 s period of the accompanying flare’s QPPs. Based on these findings, we propose that the intermittent energy release of the accompanying flare excited the narrow QFP wave train confined propagating in the coronal loop system. The unwinding jet, rather than the intermittent energy release in the accompanying flare, triggered the broad QFP wave train propagating along the solar surface.

Nanophotocatalysts are becoming increasingly popular for removing heavy metals from wastewater, degrading organic pollutants, and producing hydrogen. In this work, MgFe2O4 nanoparticles were prepared by a sol-gel process to study the photocatalytic degradation of crystal violet (CV), rhodamine blue (RhB), and methylene blue (MB) dyes under sunlight irradiation. The prepared MgFe2O4 nanoparticles were calcined at 500°C and then treated with nonthermal microwave plasma for 60 min. The X-ray diffraction (XRD) analysis revealed that the crystallinity of plasma-treated MgFe2O4 nanoparticles increased by showing a different cation distribution compared with plasma-treated nanoparticles based on the intensity ratios of (220), (400), and (420) diffraction peaks. The intensity ratios of XRD peaks decreased, and the lattice parameter increased after plasma treatment. Crystallite size also increases after plasma treatment. The optical band gap energy of the treated ferrites decreased from 2.21 eV to 2.17, showing increased light absorption in the visible region compared with pristine ferrites. The saturation magnetization of magnetic photocatalysts increased from 40.9 emu/g to 62.16 emu/g after plasma modification. The photocatalytic activity for CV, RhB, and MB degradation increased from 60%, 65%, and 53% to 90%, 91%, and 87% after plasma modification. Plasma treatment enhances dye degradation by promoting the formation of reactive species that can effectively break down the dye molecules.

Finding new catalysts and pyrolysis technologies for efficiently recycling wasted plastics into fuels and structured solid materials of high selectivity is the need of time. Catalytic pyrolysis is a thermochemical process that cracks the feedstock in an inert gas environment into gaseous and liquid fuels and a residue. This study is conducted on microwave-assisted catalytic recycling of wasted plastics into nanostructured carbon and hydrogen fuel using composite magnetic ferrite catalysts. The composite ferrite catalysts, namely, NiZnFe2O4, NiMgFe2O4, and MgZnFe2O4 were produced through the coprecipitation method and characterized for onward use in the microwave-assisted valorization of wasted plastics. The ferrite nanoparticles worked as a catalyst and heat susceptor for uniformly distributed energy transfer from microwaves to the feedstock at a moderate temperature of 450°C. The type of catalyst and the working parameters significantly impacted the process efficiency, gas yield, and structural properties of the carbonaceous residue. The tested process took 2–8 minutes to pulverize feedstock into gas and carbon nanotubes (CNTs), depending on the catalyst type. The NiZnFe2O4-catalyzed process produced CNTs with good structural properties and fewer impurities compared to other catalysts. The NiMgFe2O4 catalyst performed better in terms of hydrogen evolution by showing 87.5% hydrogen (H2) composition in the evolved gases. Almost 90% of extractable hydrogen from the feedstock evolved during the first 2 minutes of the reaction.

Copper oxide nanoparticles (CuO-NPs) were produced by implementing an electrolysis technique. The prepared CuO-NPs were processed with nonthermal microwave plasma to check the effect of plasma treatment on their morphology and photocatalytic response. The plasma processed and blank CuO-NPs samples were used in dye degradation and water splitting applications under simulated sunlight. The blank sample posed undefined morphology, which turned to spherical particles on plasma processing. The particle size grows slightly with processing time. The blank samples showed a crystallite size of 4.512 nm, which grew to 5.34 nm, 5.40 nm, and 5.49 nm after plasma processing for 10, 20, and 30 min, respectively. The lattice parameter UT sample was measured to be 2.4308 Å, with turned to 3.1091, 3.2112, and 3.3099 after 10, 20, and 30 min of plasma treatment, respectively. Similarly, band gap of CuO-NPs reduced from 2.4 eV to 2.24 eV after plasma processing for 30 min. The porosity of the nanoparticles also showed a similar trend. The plasma processing of CuO-NPs for 30 min produced the best results for photocatalytic water splitting and dye degradation applications. The photocatalytic activity revealed hydrogen evolution of 38.05 mmol.g ^−1 .h ^−1 and dye removal efficiency of 91%.

This paper presents three distinct wave trains that occurred on 2023 April 21: a broad quasiperiodic fast-propagating (QFP) wave train and bidirectional narrow QFP wave trains. The broad QFP wave train expands outward in a circular wave front, while bidirectional narrow QFP wave trains propagate in the northward and southward directions, respectively. The concurrent presence of the wave trains offers a remarkable opportunity to investigate their respective triggering mechanisms. Measurement shows that the speed of the broad QFP wave train is in the range of 300–1100 km s ^−1 in different propagating directions. There is a significant difference in the speed of the bidirectional narrow QFP wave trains: the southward propagation achieves 1400 km s ^−1 , while the northward propagation only reaches about 550 km s ^−1 accompanied by a deceleration of about 1–2 km s ^−2 . Using the wavelet analysis, we find that the periodicity of the propagating wave trains in the southward and northward directions closely matches the quasiperiodic pulsations exhibited by the flares. Based on these results, the narrow QFP wave trains were most likely excited by the intermittent energy release in the accompanying flare. In contrast, the broad QFP wave train had a tight relationship with the erupting filament, probably attributed to the unwinding motion of the erupting filament, or the leakage of the fast sausage wave train inside the filament body.

Double-decker filaments and their eruptions have been widely observed in recent years, but their physical formation mechanism is still unclear. Using high spatiotemporal resolution, multi-wavelength observations taken by the New Vacuum Solar Telescope and the Solar Dynamics Observatory, we show the formation of a double-decker pair of flux rope system by two successive tether-cutting eruptions in a bipolar active region. Due to the combined effect of photospheric shearing and convergence motions around the active region’s polarity inversion line (PIL), the arms of two overlapping inverse-S-shaped short filaments reconnected at their intersection, which created a simultaneous upward-moving magnetic flux rope (MFR) and a downward-moving post-flare-loop (PFL) system striding the PIL. Meanwhile, four bright flare ribbons appeared at the footpoints of the newly formed MFR and the PFL. As the MFR rose, two elongated flare ribbons connected by a relatively larger PFL appeared on either side of the PIL. After a few minutes, another MFR formed in the same way at the same location and then erupted in the same direction as the first one. Detailed observational results suggest that the eruption of the first MFR might experienced a short pause before its successful eruption, while the second MFR was a failed eruption. This implies that the two newly formed MFRs might reach a new equilibrium at relatively higher heights for a while, which can be regarded as a transient double-decker flux rope system. The observations can well be explained by the tether-cutting model, and we propose that two successive confined tether-cutting eruptions can naturally produce a double-decker flux rope system, especially when the background coronal magnetic field has a saddle-like distribution of magnetic decay index profile in height.