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We report herein the synthesis of ZnFe2O4 (ZF) nanoparticles via a simple and eco-friendly green route using lemon juice as a reducing agent and fuel. The effect of different calcination temperatures on the particle size and bandgap of grown ZF nanoparticles was investigated. The structural, morphological and optical properties of the synthesized nanoparticles were evaluated using synchrotron x-ray diffraction (S-XRD), field emission scanning electron microscopy (FE-SEM) and UV-visible diffuse reflectance spectroscopy (UV-Vis-DRS), respectively. S-XRD confirmed a spinel F-d3m phase in all four samples calcined at 350°C, 550°C, 750°C and 1000°C. The crystallite size calculated from the Debye–Scherrer equation showed an increase from 14 nm to 20 nm with the increase in calcination temperature. Williamson–Hall (W-H) analysis revealed an increase in the particle size from 16 nm to 21 nm and a decrease in the lattice microstrain from 0.913 × 10−3 to 0.154 × 10−4 with the increase in calcination temperature. The optical bandgap of the ZF nanoparticles obtained from UV-Vis-DRS decreased from 2.265 eV to 2.225 eV with the increase in calcination temperature. The ZF nanoparticles with tunable particle size, lattice microstrain and optical bandgap have potential application in ferrofluid, electromagnetic shielding, photocatalysis, hyperthermia, dye degradation and other areas.

This paper gives an overview on some state-of-the-art characterization methods of SiO2/4H-SiC interfaces in metal oxide semiconductor field effect transistors (MOSFETs). In particular, the work compares the benefits and drawbacks of different techniques to assess the physical parameters describing the electronic properties and the current transport at the SiO2/SiC interfaces (interface states, channel mobility, trapping phenomena, etc.). First, the most common electrical characterization techniques of SiO2/SiC interfaces are presented (e.g., capacitance- and current-voltage techniques, transient capacitance, and current measurements). Then, examples of electrical characterizations at the nanoscale (by scanning probe microscopy techniques) are given, to get insights on the homogeneity of the SiO2/SiC interface and the local interfacial doping effects occurring upon annealing. The trapping effects occurring in SiO2/4H-SiC MOS systems are elucidated using advanced capacitance and current measurements as a function of time. In particular, these measurements give information on the density (~1011 cm−2) of near interface oxide traps (NIOTs) present inside the SiO2 layer and their position with respect to the interface with SiC (at about 1–2 nm). Finally, it will be shown that a comparison of the electrical data with advanced structural and chemical characterization methods makes it possible to ascribe the NIOTs to the presence of a sub-stoichiometric SiOx layer at the interface.

Signal processing techniques play an important role in seismic data analysis. Variational mode decomposition (VMD), as a powerful signal processing method, has been extensively applied in seismic signal processing. A large number of papers on the application of VMD in seismic data analysis have appeared in various journals, conference proceedings, and technical communications. The paper aims to investigate and summarize the recent advancements of VMD and its application in seismic data analysis and give a comprehensive reference for scholars that may be interested in this topic so that researchers can select a more in-depth research direction. Firstly, the VMD principle is briefly introduced, and the advantage and limitations of this approach are illustrated in detail. Secondly, recent applications of the VMD in seismic data analysis are summarized in terms of specific scenarios, such as seismic time–frequency analysis (TFA), seismic denoising, and other applications. Finally, the key problems of VMD in seismic data analysis are discussed, and the potential research directions are listed. It is expected that the review would be constructive to the basic understanding of the VMD concept for beginners and insightful exploration of VMD’s applications in seismic data analysis for advanced researchers.Article HighlightsSeismic data analysis plays an important role in extracting valuable information from seismic recordsThis paper surveys the VMD and its applications in the field of seismic data analysis in a comprehensive wayPromising research prospects of VMD in seismic data analysis are proposed

Composite materials have become indispensable across a wide array of sectors, ranging from aerospace and automotive to energy, marine engineering, infrastructure, and architecture, thanks to their exceptional strength-to-weight ratios. In the automotive and marine industry, high-performance composites are replacing conventional materials with enhanced durability and corrosion resistance. As demand for lighter, stronger, and more durable materials grows, composites continue to outpace traditional metals and ceramics. Despite this rapid expansion, the global supply of natural fibers cannot keep pace with burgeoning demand, which is increasing at an estimated rate of 60% per year. To harness their full potential, fibers must undergo a comprehensive process. Physico-chemical, thermal, mechanical, and morphological characterization; surface treatments may be necessary to remove impurities or enhance interfacial adhesion when fibers exhibit insufficient roughness. To address both material scarcity and environmental concerns, this study identifies and characterizes a novel lignocellulosic fiber source derived from agricultural waste. By transforming residual biomass into high-value reinforcement materials, we not only expand the palette of natural fibers available for composite manufacturing but also contribute to waste reduction and promote a circular ‘waste-to-materials’ economy. This approach promises significant environmental benefits, paving the way for greener using an agricultural waste for sustainable applications.

The increasing demand for more content, services, and security drives the development of high-speed wireless technologies, optical communication, automotive radar, imaging and sensing systems and many other mm-wave and THz applications. S-parameter measurement at mm-wave and sub-mm wave frequencies plays a crucial role in the modern IC design debug. Most importantly, however, is the step of device characterization for development and optimization of device model parameters for new technologies. Accurate characterization of the intrinsic device in its entire operation frequency range becomes extremely important and this task is very challenging. This book presents solutions for accurate mm-wave characterization of advanced semiconductor devices. It guides through the process of development, implementation and verification of the in-situ calibration methods optimized for high-performance silicon technologies. Technical topics discussed in the book include: • Specifics of S-parameter measurements of planar structures • Complete mathematical solution for lumped-standard based calibration methods, including the transfer Thru-Match-Reflect (TMR) algorithms • Design guideline and examples for the on-wafer calibration standards realized in both advanced SiGe BiCMOS and RF CMOS processes • Methods for verification of electrical characteristics of calibration standards and accuracy of the in-situ calibration results • Comparison of the new technique vs. conventional approaches: the probe-tip calibration and the pad parasitic de-embedding for various device types, geometries and model parameters • New aspects of the on-wafer RF measurements at mmWave frequency range and calibration assurance.

Abstract The human gastrointestinal tract harbors a heterogeneous and complex microbial community, which plays a key role in human health. The gut microbiota controls the development of the immune system by setting systemic threshold for immune activation. Glycoconjugates, such as lipopolysaccharides, from gut bacteria have been shown to be able to elicit both systemic proinflammatory and immunomodulatory responses. This phenomenon is particularly intriguing considering that the immune system is charged with the task to distinguish the beneficial microbes from the pathogens, even if the commensal bacteria have molecular patterns resembling those of the pathogenic counterparts. Therefore, the importance of the chemical structure of these macromolecules in fine tuning this delicate equilibrium is beyond question. This review offers an overview of the current understanding of chemical peculiarities of the lipopolysaccharides isolated from the gut microbiota, and their relationships to their biological activity in terms of immune system maturation and development. The gut microbiota lipopolysaccharides: the poorly explored world that can change the perception of endotoxins from harmful to beneficial.

In this work, a 3D printed polymer–metal soft-magnetic composite was developed and characterized for its material, structural, and functional properties. The material comprises acrylonitrile butadiene styrene (ABS) as the polymer matrix, with up to 40 vol. % stainless steel micropowder as the filler. The composites were rheologically analyzed and 3D printed into tensile and flexural test specimens using a commercial desktop 3D printer. Mechanical characterization revealed a linearly decreasing trend of the ultimate tensile strength (UTS) and a sharp decrease in Young’s modulus with increasing filler content. Four-point bending analysis showed a decrease of up to 70% in the flexural strength of the composite and up to a two-factor increase in the secant modulus of elasticity. Magnetic hysteresis characterization revealed retentivities of up to 15.6 mT and coercive forces of up to 4.31 kA/m at an applied magnetic field of 485 kA/m. The composite shows promise as a material for the additive manufacturing of passive magnetic sensors and/or actuators.

Recent developments in micro and nanoencapsulation are promising tools to encounter the different limitations of essential oil formulations, enhance their functionalities, and protect them from the external environmental conditions. This review addresses the current studies and progresses related to the development of encapsulated essential oils using different systems and carrier material types. It also focuses on the formation methods used with the subsequent physicochemical characterization of the developed particles. Moreover, this review considers the factors affecting the release of essential oils with the different physicochemical release models. The choice of the appropriate formation method as well as the carrier material types and system forms were shown to highly depend on the intended purpose of the encapsulated essential oil formulation. Micro and nanoencapsulation are used to control essential oils’ release properties, enhance the various characteristics of essential oils, and allow to expand applications in different fields. This review provides the optimal conditions for micro and nanoencapsulation of essential oil formulations based on the intended end uses.

The New Advanced Telescope for High ENergy Astrophysics (NewAthena) will be the largest space‐based X‐ray observatory ever built. It will have an effective area above 1.1 m2 at 1 keV, which corresponds to a polished mirror surface of about 300 m2 due to the grazing incidence. As such a mirror area is not achievable with an acceptable mass even with nested shells, silicon pore optics (SPO) technology will be utilized. In the PTB laboratory at BESSY II, two dedicated beamlines are in use for their characterization with monochromatic radiation at 1 keV and a low divergence well below 2 arcsec: the X‐ray Pencil Beam Facility (XPBF 1) and the X‐ray Parallel Beam Facility (XPBF 2.0), where beam sizes up to 8 mm × 8 mm are available while maintaining low beam divergence. This beamline is used for characterizing mirror stacks and controlling the focusing properties of mirror modules (MMs) – consisting of four mirror stacks – during their assembly at the beamline. A movable CCD based camera system 12 m from the MM registers the direct and the reflected beams. The positioning of the detector is verified by a laser tracker. The energy‐dependent reflectance in double reflection through the pores of an MM with an Ir coating was measured at the PTB four‐crystal monochromator beamline in the photon energy range 1.75 keV to 10 keV, revealing the effects of the Ir M edges. The measured reflectance properties are in agreement with the design values to achieve the envisaged effective area. Mirror modules for the optics of the X‐ray observatory NewAthena are assembled and characterized at dedicated synchrotron radiation beamlines. The reflectance of a fully assembled and coated module was determined in a wide energy range.

We introduce an optimized physics-informed neural network (PINN) trained to solve the problem of identifying and characterizing a surface breaking crack in a metal plate. PINNs are neural networks that can combine data and physics in the learning process by adding the residuals of a system of partial differential equations to the loss function. Our PINNs is supervised with realistic ultrasonic surface acoustic wave data acquired at a frequency of 5 MHz. The ultrasonic surface wave data is represented as a deformation on the top surface of a metal plate, measured by using the method of laser vibrometry. The PINN is physically informed by the acoustic wave equation and its convergence is sped up using adaptive activation functions. The adaptive activation function uses a trainable hyperparameter, which is optimized to achieve the best performance of the network. The adaptive activation function changes dynamically, involved in the optimization process. The usage of the adaptive activation function significantly improves the convergence, evidently observed in the current study. We use PINNs to estimate the speed of sound of the metal plate, which we do with an error of 1%, and then, by allowing the speed of sound to be space dependent, we identify and characterize the crack as the positions where the speed of sound has decreased. Our study also shows the effect of sub-sampling of the data on the sensitivity of sound speed estimates. More broadly, the resulting model shows a promising deep neural network model for ill-posed inverse problems.