This study focuses on the quaternary Heusler alloy CoFeZrGe, explored through first-principles calculations to evaluate its potential in spintronic and optoelectronic applications. Heusler alloys attract significant interest due to their diverse electronic and magnetic characteristics, particularly their capacity to exhibit half-metallic ferromagnetism, a property crucial for high-performance spintronic devices. Here, the structural, electronic, magnetic, mechanical, and optical properties of CoFeZrGe are systematically investigated.

Computations were carried out within the framework of density functional theory (DFT) using the full-potential linearized augmented plane wave (FP-LAPW) method implemented in WIEN2k. Three atomic configurations compatible with the F-43m space group (Y1, Y2, and Y3) were considered, with structural optimization identifying the Y1 arrangement as the lowest energy configuration. The electronic structure was analyzed using both the generalized gradient approximation (GGA-PBE) and the modified Becke–Johnson (TB-mBJ) potential.

The findings reveal that CoFeZrGe possesses half-metallic ferromagnetism, featuring an indirect minority-spin band gap of 0.48 eV (GGA) and 1.27 eV (TB-mBJ). The material exhibits a total magnetic moment of 1 μB per formula unit, consistent with the Slater–Pauling rule. Calculated elastic constants verify mechanical stability and indicate ductility, supported by favorable values of bulk modulus, shear modulus, Poisson’s ratio, and Pugh’s ratio. Optical results, including the dielectric function, absorption coefficient, and energy loss spectra, highlight pronounced interband transitions and strong absorption across the visible and ultraviolet regions.

Overall, CoFeZrGe demonstrates a combination of stable half-metallicity, mechanical robustness, and excellent optical response, making it a strong candidate for future spintronic and optoelectronic technologies.

Understanding the complexity of glass formation remains a significant challenge in materials science. Solving the mystery of the dynamic processes involved during glass transition involves answering the key questions of where and why the transition begins and ends during the cooling process.

This study focuses on silica glass, considered to be the most fundamental glass-forming material. The research community has gathered extensive experimental data on both the physical properties and analytical techniques related to silica crystals and silica glass. These data can be used to assess new theories. This study recognizes that both the crystal and glass forms of silica are made up of SiO₄ tetrahedra. A thorough understanding of the crystallization process requires knowledge of how SiO₄ tetrahedra behave under different temperatures during slow-cooling. Based on this understanding and fundamental physical laws, it becomes possible to predict how SiO₄ tetrahedra react during rapid cooling. The available experimental data can help to verify the accuracy of these predictions. Once the silica glass transition process is understood, the insights gained can also be applied to the transitions of more complex glasses.

This analysis indicates that, during rapid cooling, silica structures within the temperature range from the melting point to the polymorphic inversion temperature, 1470°C, are heterogeneous, featuring embryonic clusters, and begin to shift toward more stable structures at 1470°C. Experimental data confirm that this is a continuous structural transition occurring over several hundred degrees.

It is concluded that the silica glass transition can be identified as a second-order phase transition, resulting in a glass state with a unique structure and properties that differ from those of liquid and crystalline silica. The method for determining the glass transition temperatures where the transition begins is straightforward and can also be applied to complex silicate glasses.

Non-destructive evaluation techniques are increasingly recognised as effective alternatives to destructive testing for estimating the compressive strength of lightweight aggregate concrete (LWAC). Among these, ultrasonic pulse velocity (UPV) is a well-established and widely employed method, characterised by its rapidity, non-invasiveness, and relative simplicity of implementation. In this study, an experimental dataset comprising 640 core segments from 160 cylindrical specimens, provided for analysis, was investigated. Each segment was described by physical and processing variables, including lightweight aggregate and concrete densities, casting and vibration times, experimental dry density, and P-wave velocity obtained through UPV testing. A segregation index (SI), derived from UPV measurements and defined as the ratio of local to mean P-wave velocity within each specimen, was also considered, following approaches previously suggested in the literature. A range of machine learning techniques was applied to assess the predictive capacity of local P-wave velocity and SI. Most ensemble-based methods and support vector regression achieved the highest accuracy when SI was excluded, indicating that its contribution was redundant. By contrast, Gaussian process regression showed slight improvements when SI was included. The results confirmed that the P-wave velocity measured by UPV testing is a reliable non-destructive predictor of compressive strength in LWAC. At the same time, the added value of SI remains negligible under conditions of low segregation, as reflected by SI values above 0.8. These findings highlight the practical potential of integrating UPV-based measurements with data-driven modelling to enhance the reliability of concrete characterisation and quality control.

The conventional experimental procedure involving Titanium–Nickel (Ti-Ni) shape memory alloys requires conducting dozens or even hundreds of heating and cooling cycles performed by the actuator to generate thermal hysteresis curves. This study proposes the development of a proxy model based on machine learning techniques, using experimental results, with the goal of replicating the actuator's function in this experiment. The proxy model should be capable of accurately predicting the actuator’s thermomechanical response based on time series data of heating and cooling cycles over time. It is important to highlight that this is not the traditional time series forecasting problem aimed at predicting future values, but rather a problem of predicting the dynamic responses of the actuator associated with new input profiles (temperature, mechanical stress, and strain). The proposed strategy is based on the use of deep neural network algorithms, aiming to capture the actuator’s dynamics from experimental data. The main architecture used for modeling temporal dependencies is the recurrent neural network (RNN), specifically the Long Short-Term Memory (LSTM) type, known for its ability to extract complex and nonlinear temporal patterns in time series data. To evaluate the performance of the proxy model, an experimental dataset was generated using a helical spring-shaped actuator under load. The model's predictions were compared with the experimentally obtained hysteresis curve in order to validate its generalization capability. The results demonstrate that the proposed technique is highly promising, achieving a mean squared error on the order of 1.2%.

Terahertz (THz) metasurfaces enable subwavelength electromagnetic wave control, offering a path to compact and tunable filters for spectroscopy, sensing, and communications. Here, we present a simulation-driven design for a compact multilayer THz bandpass metasurface filter [1]. The 3D device geometry consists of a silicon substrate supporting a thin gold film perforated with subwavelength annular apertures, a high-transmittance dielectric spacer (PTFE or HDPE), and an aligned array of gold rings on top. This stack can be fabricated by depositing gold through annular masks, eliminating the need for lithographic etching of the substrate presented in our previous model [2].

Full-wave electromagnetic simulations (COMSOL) guided the design process, optimizing spacer thickness and geometric parameters. Simulated transmission spectra show that devices with either PTFE or HDPE spacers yield broad passbands with high transmission. The spectral peak position and shape remain largely invariant under minor geometric deviations, indicating a robust design. The enhanced transmission is attributed to constructive interference between waves reflected at multiple interfaces in the multilayer structure. The combination of a simple mask-defined architecture, material-dependent tunability, and tolerance to fabrication imperfections makes this metasurface filter a promising candidate for THz bandpass filtering, anti-reflection coatings, spectroscopy, and biosensing. This work highlights the effectiveness of computational design in advancing THz metasurface technologies.

Foundation

This work was supported by Grants No. 22rl-056 and 24AA-2J068 of the Higher Education and Science Committee of the RA MoESCS.

References
[1] G.-M. Li, et al. Terahertz bandpass and bandstop filter based on the babinet complementary metamaterials, Optics Communications 571, 130944 (2024).

[2] K. Simonyan, et al. Broadband THz metasurface bandpass filter/antireflection coating based on metalized Si cylindrical rings, Semicond. Sci. Technol. 39 095012 (2024).

Increased use of plastic is one of the main reasons for pollution. It takes about 20 to 500 years for traditional plastic to decompose; hence, it finds its way into landfills or oceans. The solution is to produce starch-based biodegradable plastic. The main ingredient of this would be starch, which is organic and natural, and all sorts of toxic additives would be avoided to ensure that plastic remains one hundred percent organic. This would allow it to come from the soil and go back to it without causing harm, completing a full natural life cycle. This study aimed to synthesize bioplastic, and involved the design and development an automated machine capable of producing biodegradable plastic bags using starch-based polymer films. The system described in this study integrates a mixing unit followed by a film-sliding mechanism, drying, and a thermal sealing unit, all controlled via Arduino to ensure precision and repeatability. Compared to conventional machines, this design consumes less energy and supports environmentally friendly materials. It efficiently processes starch-based polymers into usable bags with acceptable mechanical properties and biodegradability. It is an alternative to traditional plastic, which is vital, as removing the use of plastic from day-to-day life appears to be an impossible task. It contributes to sustainable manufacturing and serves as a foundation for further innovations in green packaging technologies.

This work presents a comparative study on the mechanical homogenisation of Triply Periodic Minimal Surface (TPMS) lattice structures, which have attracted significant interest for their unique ability to combine lightweight design with tailored mechanical, thermal, and acoustic properties. The study investigates the effective mechanical behaviour of Representative Unit Cells (RUCs) generated using the open-source Python tool Microgen. Two homogenisation strategies are considered: (i) Finite Element (FE)-based homogenisation carried out in Abaqus, and (ii) the Mechanics of Structure Genome (MSG), a unified theory for multiscale constitutive modelling, implemented in a specialized software framework. The comparison encompasses multiple TPMS topologies, including well-studied cases used for validation as well as less-explored ones to provide new insights, namely gyroid, diamond, PMY, and F-Rhombic Dodecahedron (F-RD). RUCs are analysed across relative densities ranging from 10% to 50%. Equivalent linear elastic properties (Young’s moduli, shear moduli, and Poisson’s ratios) are derived and compared to assess the consistency, accuracy, and computational efficiency of the two approaches. Furthermore, the anisotropy of each TPMS topology across the range of relative densities is examined through the directional distribution of Young’s moduli. The outcomes are expected to clarify the strengths and limitations of FE versus MSG in capturing the effective behaviour of architected cellular solids, thus supporting the selection of homogenisation strategies for the design of lattice-based lightweight structures.

The present talk addresses the extension of hierarchical shell finite elements based on Carrera’s Unified Formulation (CUF) to a global-local approach for the investigation of Variable-Angle Tow (VAT) composite structures. VAT laminates are characterized by curvilinear fibres laid along predefined paths, enabling enhanced mechanical performance and a wider structural design space. Nevertheless, their analysis typically requires high computational effort to accurately capture the complex displacement and stress fields resulting from the variable in-plane fibre distribution. In the proposed strategy, a global analysis is first performed over the entire structural domain using low-order Abaqus shell elements with a reduced number of degrees of freedom. A subsequent local analysis employs a refined CUF model with higher-order through-the-thickness approximations in a layer-wise manner, enabling accurate and efficient capture of the high stress gradients that typically arise near geometric singularities and discontinuities. The governing equations are derived within the CUF framework using both the Principle of Virtual Displacements (PVD) and the Reissner’s Mixed Variational Theorem (RMVT). Validation against full three-dimensional finite element simulations in Abaqus demonstrates the accuracy of the proposed methodology. Comparisons in terms of degrees of freedom confirm that the global–local CUF-based approach achieves high accuracy near discontinuities at a significantly reduced computational cost relative to full 3D models. Furthermore, the differences observed between the PVD and RMVT formulations highlight the critical role of transverse stress prediction in the analysis of VAT composites.

It is important to quantitatively and graphically characterize the four core effects, the most fundamental yet disputable issues of high-entropy alloys. Yet, the traditional and commonly believed special quasirandom structure (SQS) based on the prefect random mixing structure hypothesis is insufficient as the SQS model ignores the difference of the types of different constituent atoms, the difference of the types of different crystal lattice structure, such as FCC, BCC and HCP, and the difference of the different heat treatment temperatures. In this contribution, based on crystal structure, we propose an alloy thermodynamics model based on the crystallographic structure and then establish the thermodynamic database of the end-member involved by combining computational thermodynamics and first-principle calculations. Thus, the four core effects of high-entropy alloys with various phase structures were quantitatively and graphically characterized, including the site occupying fractions (SOFs), and then the atomic distribution model construction based on SOFs, short-range ordering (SRO) cluster, diverse mechanical property, interstitial atom diffusion, and catalytic characteristic of selected high-entropy alloys. Meanwhile, such behaviors of the commonly believed SQS based on the prefect random mixing structure were also simulated and compared with those of the SOF structures. We conclude that it is quite necessary and also feasible to consider the inherent and inevitable sublattice preference of constituent atoms to simulate the structure and diverse properties of HEAs theoretically, which extends beyond the commonly believed but baseless SQS based on the random mixing hypothesis.

This study details a nonlinear buckling and post-buckling analysis of Functionally Graded Material (FGM) plates, which are increasingly utilized in advanced engineering applications like aerospace due to their superior thermal and mechanical properties. The design and integrity of these structures under complex loading conditions, however, pose significant challenges, particularly regarding their stability under compressive forces.To address this, the structural formulation is based on the First-Order Shear Deformation Theory (FSDT), which is well-suited for capturing shear deformation effects that become significant in thin to moderately thick structures. By minimizing the total potential energy of the system, a comprehensive mathematical model is derived. The subsequent solution of the highly complex nonlinear governing equations is achieved through an innovative hybrid numerical approach. This method leverages the robust Asymptotic Numerical Method (ANM), a powerful continuation technique known for its ability to efficiently trace complex equilibrium paths, and integrates it with the versatile Finite Element Method (FEM). This combination effectively handles the spatial discretization of the plate while providing a stable and accurate means to track the structural behavior beyond the critical buckling point.The presented approach is particularly effective in accurately identifying critical buckling loads and precisely locating bifurcation points along the equilibrium paths. The method's effectiveness is demonstrated by its high computational efficiency and accuracy, which are critical for the design and safety analysis of FGM components. The findings of this research provide a reliable tool for structural engineers to analyze the stability and behavior of FGM plates, contributing to the development of more resilient and efficient structural designs.