Search Results (104)

Owing to the inherent safety, environmental friendliness, and high theoretical capacity (820 mAh g⁻¹) of zinc metal, aqueous zinc-ion batteries (AZIBs) have emerged as up-and-coming alternatives to organic lithium-ion batteries. However, the insufficient electrochemically active sites, poor structural stability, and severe interfacial side reactions of cathode materials have always been key challenges, restricting battery gravimetric energy density and cycling stability. This article systematically reviews current mainstream AZIB cathode material systems, encompassing layered manganese- and vanadium-based metal oxides, Prussian blue analogs, and emerging organic polymers. It focuses on analyzing the energy storage mechanisms of different material systems and their structural evolution during Zn²⁺ (de)intercalation. Furthermore, mechanisms of innovative strategies for improving cathodes are thoroughly examined here, such as nanostructure engineering, lattice doping control, and surface coating modification, to address common issues like structural degradation, manganese/vanadium dissolution, and interface passivation. Finally, this article proposes future research directions: utilizing multi-scale in situ characterization to elucidate actual reaction pathways, constructing artificial interface layers to suppress side reactions, and optimizing full-cell design. This review provides a new perspective for developing practical AZIBs with high specific energy and long lifespans. Full article

High-entropy alloys (HEAs) are highly concentrated, multicomponent alloys that have received significant attention due to their superior properties compared to conventional alloys. The mechanical properties and hardness are interrelated, and it is widely known that the hardness of HEAs depends on the principal alloying elements and their composition. Therefore, the desired hardness prediction to develop new HEAs is more interesting. However, the relationship of these compositions with the HEA hardness is very complex and nonlinear. In this study, we develop an artificial neural network (ANN) model using experimental data sets (535). The compositional elements—Al, Co, Cr, Cu, Mn, Ni, Fe, W, Mo, and Ti—are considered input parameters, and hardness is considered as an output parameter. The developed model shows excellent correlation coefficients (Adj R2) of 99.84% and 99.3% for training and testing data sets, respectively. We developed a user-friendly graphical interface for the model. The developed model was used to understand the effect of alloying elements on hardness. It was identified that the Al, Cr, and Mn were found to significantly enhance hardness by promoting the formation and stabilization of BCC and B2 phases, which are inherently harder due to limited active slip systems. In contrast, elements such as Co, Cu, Fe, and Ni led to a reduction in hardness, primarily due to their role in stabilizing the ductile FCC phase. The addition of W markedly increased the hardness by inducing severe lattice distortion and promoting the formation of hard intermetallic compounds. Full article

The reconstruction of segmental bone defects requires patient-specific scaffolds that combine mechanical safety, biological functionality, and rapid manufacturing. We converted CT-derived mandibular geometry into a functionally graded Ti-6Al-4V lattice and optimised porosity, screw layout, and strut thickness through a cyber-physical loop that joins high-fidelity FEM, millisecond ANN, and a BN for uncertainty quantification. Fifteen candidate scaffolds were fabricated by direct metal laser sintering and hot isostatic pressing and were mechanically tested. FEM predicted stress and stiffness with 98% accuracy; the ANN reproduced these outputs with 94% fidelity while evaluating 10,000 designs in real time, and the BN limited failure probability to <3% under worst-case loads. The selected 55–65% porosity design reduced titanium use by 15%, shortened development time by 25% and raised multi-objective optimisation efficiency by 20% relative to a solid-plate baseline, while resisting a 600 N bite with a peak von Mises stress of 225 MPa and micromotion < 150 µm. Integrating physics-based simulation, AI speed, and probabilistic rigour yields a validated, additively manufactured scaffold that meets surgical timelines and biomechanical requirements, offering a transferable blueprint for functional scaffolds in bone and joint surgery. Full article

Gliders are engineless aircraft capable of maintaining altitude for extended periods and achieving long ranges. This paper presents an optimal aerodynamic design method for flying wing gliders, leveraging a combination of artificial neural networks (ANNs) as a surrogate model and genetic algorithms (GAs) for optimization. Data for training the ANN is generated using the vortex-lattice method (VLM). The study identifies optimal aerodynamic shapes for two objectives: maximum flight endurance and maximum range. A key finding is the inherent conflict between aerodynamic performance and static stability in tailless designs. By introducing a stability constraint via a penalty function, we successfully generate stable and high-performance configurations. For instance, the stabilized RG15 airfoil design achieves a maximum glide ratio of 24.1 with a robust 5.1% static margin. This represents a calculated 11.5% performance reduction compared to its unstable theoretical optimum, quantitatively demonstrating the crucial trade-off between stability and performance. The methodology provides a computationally efficient path to designing practical, high-performance, and inherently stable flying wing gliders. Full article

This paper systematically investigates interval-valued intuitionistic fuzzy (IVIF) sets and filters within the framework of hoop algebras, unifying and extending classical fuzzy set theory and intuitionistic fuzzy sets (IFS) in algebraic logic. We clarify the foundational relationships among fuzzy sets, IFS, and hoop algebras, and introduce novel characterizations of IVIF filters, including necessary and sufficient conditions for their existence and structure. Theoretical advancements include the demonstration that IVIF filters can be described via their endpoint functions, the establishment of a bounded distributive lattice of IVIF filters, and the identification of congruence relations induced by these filters. Algorithmic and numerical aspects are addressed through explicit pseudocode and detailed examples, illustrating how the verification and construction of IVIF filters can be performed in finite hoop algebras. Practical implications are highlighted in decision-making scenarios where modeling uncertainty and vagueness with interval-valued membership and non-membership degrees offers enhanced flexibility and robustness. Our results lay a rigorous foundation for further applications of IVIF filters in fuzzy logic, artificial intelligence, and multi-criteria decision analysis. Full article

Biomimetic lattice structures, inspired by natural architectures such as bone, coral, mollusk shells, and Euplectella aspergillum, have gained increasing attention for their exceptional strength-to-weight ratios, energy absorption, and deformation control. These properties make them ideal for advanced engineering applications in aerospace, biomedical devices, and structural impact protection. This study presents a comprehensive bibliometric analysis of global research on biomimetic lattice structures published between 2020 and 2025, aiming to identify thematic trends, collaboration patterns, and underexplored areas. A curated dataset of 3685 publications was extracted from databases like PubMed, Dimensions, Scopus, IEEE, Google Scholar, and Science Direct and merged together. After the removal of duplication and cleaning, about 2226 full research articles selected for the bibliometric analysis excluding review works, conference papers, book chapters, and notes using Cite space, VOS viewer version 1.6.20, and Bibliometrix R packages (4.5. 64-bit) for mapping co-authorship networks, institutional affiliations, keyword co-occurrence, and citation relationships. A significant increase in the number of publications was found over the past year, reflecting growing interest in this area. The results identify China as the most prolific contributor, with substantial institutional support and active collaboration networks, especially with European research groups. Key research focuses include additive manufacturing, finite element modeling, machine learning-based design optimization, and the performance evaluation of bioinspired geometries. Notably, the integration of artificial intelligence into structural modeling is accelerating a shift toward data-driven design frameworks. However, gaps remain in geometric modeling standardization, fatigue behavior analysis, and the real-world validation of lattice structures under complex loading conditions. This study provides a strategic overview of current research directions and offers guidance for future interdisciplinary exploration. The insights are intended to support researchers and practitioners in advancing next-generation biomimetic materials with superior mechanical performance and application-specific adaptability. Full article

This study presents a multi-objective optimization framework for designing cylindrical triply periodic minimal surface (TPMS) lattices tailored for bone implant applications. Using an artificial neural network (ANN) as a surrogate model trained on simulated data, four key properties—ultimate stress (U), energy absorption (EA), surface area-to-volume ratio (SA/VR), and relative density (RD)—were predicted from seven lattice design parameters. To address anatomical variability, a novel implant size-based categorization (small, medium, and large) was introduced, and separate optimization runs were conducted for each group. The optimization was performed via the NSGA-II algorithm to maximize mechanical performance (U and EA) and surface efficiency (SA/VR), while filtering for biologically relevant RD values (20–40%). Separate optimization runs were conducted for small, medium, and large implant size groups. A total of 105 Pareto-optimal designs were identified, with 75 designs retained after RD filtering. SHapley Additive exPlanations (SHAP) analysis revealed the dominant influence of thickness and unit cell size on target properties. Kernel density and boxplot comparisons confirmed distinct performance trends across size groups. The framework effectively balances competing design goals and enables the selection of size-specific lattices. The proposed approach provides a reproducible pathway for optimizing bioarchitectures, with the potential to accelerate the development of lattice-based implants in personalized medicine. Full article

Hip implant optimization is increasingly receiving attention due to the development of manufacturing technology and artificial intelligence interaction in the current research. This study investigates the development of hip implant stem design with the application of lattice structures, and the utilization of the MATLAB regression learner app in finding the best predictive regression model to calculate the mechanical behavior of the implant’s stem based on some of the design parameters. Many cases of latticed hip implants (using 3D lattice infill type) were designed in the ANSYS software, and then 3D printed to undergo simulations and lab experiments. A surrogate model of the implant was used in the finite element analysis (FEA) instead of the geometrically latticed model to save computation time. The model was then generalized and used to calculate the mechanical behavior of new variables of hip implant stem and a database was generated for surgeon so they can choose the lattice parameters for desirable mechanical behavior. This study shows that neural networks algorithms showed the highest accuracy with predicting the mechanical behavior reaching a percentage above 90%. Patients’ weight and shell thickness were proven to be the most affecting factors on the implant’s mechanical behavior. Full article

Lattice structures emerged as a revolutionary class of materials with significant applications in aerospace, biomedical engineering, and mechanical design due to their exceptional strength-to-weight ratio, energy absorption properties, and structural efficiency. This review systematically examines recent advancements in lattice structures, with a focus on their classification, mechanical behavior, and optimization methodologies. Stress distribution, deformation capacity, energy absorption, and computational modeling challenges are critically analyzed, highlighting the impact of manufacturing defects on structural integrity. The review explores the latest progress in hybrid additive manufacturing, hierarchical lattice structures, modeling and simulation, and smart adaptive materials, emphasizing their potential for self-healing and real-time monitoring applications. Furthermore, key research gaps are identified, including the need for improved predictive computational models using artificial intelligence, scalable manufacturing techniques, and multi-functional lattice systems integrating thermal, acoustic, and impact resistance properties. Future directions emphasize cost-effective material development, sustainability considerations, and enhanced experimental validation across multiple length scales. This work provides a comprehensive foundation for future research aimed at optimizing biomimetic lattice structures for enhanced mechanical performance, scalability, and industrial applicability. Full article

Key exchange mechanisms are foundational to secure communication, yet traditional methods face challenges from quantum computing. The Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM) is a post-quantum cryptographic key exchange protocol with unknown successful quantum vulnerabilities. This study evaluates the ML-KEM using experimental benchmarks. We implement the ML-KEM in Python for clarity and in C++ for performance, demonstrating the latter’s substantial performance improvements. The C++ implementation achieves microsecond-level execution times for key generation, encapsulation, and decapsulation. Python, while slower, provides a user-friendly introduction to the ML-KEM’s operation. Moreover, our Python benchmark confirmed that the ML-KEM consistently outperformed RSA in execution speed across all tested parameters. Beyond benchmarking, the ML-KEM is shown to handle the computational hardness of the Module Learning With Errors (MLWE) problem, ensuring resilience against quantum attacks, classical attacks, and Artificial Intelligence (AI)-based attacks, since the ML-KEM has no pattern that could be detected. To evaluate its practical feasibility on constrained devices, we also tested the C++ implementation on a Raspberry Pi 4B, representing IoT use cases. Additionally, we attempted to run integration and benchmark tests for the ML-KEM on microcontrollers such as the ESP32 DevKit, ESP32 Super Mini, ESP8266, and Raspberry Pi Pico, but these trials were unsuccessful due to memory constraints. The results showed that while the ML-KEM can operate effectively in such environments, only devices with sufficient resources and runtimes can support its computational demands. While resource-intensive, the ML-KEM offers scalable security across diverse domains such as IoT, cloud environments, and financial systems, making it a key solution for future cryptographic standards. Full article

Structural symmetry significantly influences the fundamental characteristics of two-dimensional (2D) materials. In conventional transition metal dichalcogenides (TMDs), the absence of in-plane symmetry introduces distinct optoelectronic behaviors. To further enrich the functionality of such materials, recent efforts have focused on disrupting out-of-plane symmetry—often through the application of external electric fields—which leads to the generation of an intrinsic electric field within the lattice. This internal field alters the electronic band configuration, broadening the material’s applicability in fields like optoelectronics and spintronics. Among various engineered 2D systems, Janus transition metal dichalcogenides (JTMDs) have shown as a compelling class. Their intrinsic structural asymmetry, resulting from the replacement of chalcogen atoms on one side, naturally breaks out-of-plane symmetry and surpasses certain limitations of traditional TMDs. This unique arrangement imparts exceptional physical properties, such as vertical piezoelectric responses, pronounced Rashba spin splitting, and notable changes in Raman modes. These distinctive traits position JTMDs as promising candidates for use in sensors, spintronic devices, valleytronic applications, advanced optoelectronics, and catalytic processes. In this Review, we discuss the synthesis methods, structural features, properties, and potential applications of 2D JTMDs. We also highlight key challenges and propose future research directions. Compared with previous reviews, this work focusing on the latest scientific research breakthroughs and discoveries in recent years, not only provides an in-depth discussion of the out-of-plane asymmetry in JTMDs but also emphasizes recent advances in their synthesis techniques and the prospects for scalable industrial production. In addition, it highlights the rapid development of JTMD-based applications in recent years and explores their potential integration with machine learning and artificial intelligence for the development of next-generation intelligent devices. Full article

The continuous demand for faster processing systems, driven by the rise of artificial intelligence, has exposed limitations in traditional transistor-based electronics, including quantum tunneling, heat dissipation, and switching delays due to challenges in further miniaturization. This study explores optical systems as a promising alternative, leveraging the speed of photons over electrons. Specifically, we design and simulate optical NAND and NOR logic gates using a two-dimensional photonic crystal structure with a square lattice. Symmetrical waveguides are used for the input paths to make the structure relatively more straightforward to fabricate. A key innovation is the ability to realize both gates within a single structure by adjusting the phases of the input sources. To optimize the phase parameters efficiently, we employ the ML-FOLD (Meta-Learning and Formula Optimization for Logic Design) optimization formula, which outperforms traditional methods and machine learning approaches in terms of computational efficiency and data requirements. Through finite-difference time-domain (FDTD) simulations, the proposed optical structure demonstrates successful implementation of NAND and NOR gate logic, achieving high contrast ratios of 4.2 dB and 4.8 dB, respectively. The results validate the effectiveness of the ML-FOLD method in identifying optimal configurations, offering a streamlined approach for the design of all-optical logic devices. Full article

The relationship between structure and properties is fundamental in materials science, particularly for aliovalently doped perovskites, where structural changes significantly influence material performance. Accurate prediction of key structural parameters is essential for tailoring these materials for advanced applications. In this study, we developed an Artificial Neural Network (ANN) model to predict lattice constants with high accuracy, achieving near-perfect R² values and minimal prediction errors across training and testing datasets. To address the interpretability challenge commonly associated with black-box models, we integrated Partial Dependence Plots (PDPs), enabling a transparent analysis of how input features, including lattice parameters a, b, c, and the number of formula units per unit cell (Z), affect model predictions. Our findings show that parameters a, b, and c generally contribute to lattice expansion, while Z exhibits an inverse relationship due to its impact on packing density. The inclusion of PDPs offers novel insights into the underlying physical relationships and enhances the trustworthiness of machine learning (ML) predictions in the context of perovskite design. This approach demonstrates the utility of combining high-accuracy ML models with interpretability techniques to accelerate discovery in materials science. Full article

Lattice structures, with their unique design, offer properties like a programmable elastic modulus, an adjustable Poisson’s ratio, high specific strength, and a large specific surface area, making them the key to achieving structural lightweighting, improving impact resistance, vibration suppression, and maintaining high thermal efficiency in the aerospace field. However, functional prediction and inverse design remain challenging due to cross-scale effects, extensive spatial freedom, and high computational costs. Recent advancements in AI have driven progress in predicting lattice structure functionality. This paper begins with an introduction to the lattice types, their properties, and applications. Then the development process for the performance-prediction methods of lattice structures is summarized. The current applications of performance-prediction methods, which are data-driven and related to material properties, structural properties, and performance under conditions of coupled multi-physical fields, are analyzed, and this analysis further extends to the data-driven methods in relation to their prediction of lattice structure functionality. This paper summarizes the application of data-driven methods in the prediction of the mechanical, energy absorption, acoustic, and thermal properties of lattice structures; elaborates on the application of these methods in the optimization design of lattice structures in the aerospace field; and details the relevant theory and references for the field of lattice structure performance analysis. Finally, the progress and problems in the functional prediction of lattice structures under the current research is demonstrated, and the future development direction of this field is envisioned. Full article

Fluid flow in microporous and nanoporous media exhibits unique behaviors that deviate from classical continuum predictions due to dominant surface forces at small scales. Understanding these microscale flow mechanisms is critical for optimizing unconventional reservoir recovery and other energy applications. This review provides a comparative analysis of the existing literature, highlighting key advances in experimental techniques, theoretical models, and numerical simulations. We discuss how innovative micro/nanofluidic devices and high-resolution imaging methods now enable direct observation of confined flow phenomena, such as slip flow, phase transitions, and non-Darcy behavior. Recent theoretical models have clarified scale-dependent flow regimes by distinguishing microscale effects from macroscopic Darcy flow. Likewise, advanced numerical simulations—including molecular dynamics (MD), lattice Boltzmann methods (LBM), and hybrid multiscale frameworks—capture complex fluid–solid interactions and multiphase dynamics under realistic pressure and wettability conditions. Moreover, the integration of artificial intelligence (e.g., data-driven modeling and physics-informed neural networks) is accelerating data interpretation and multiscale modeling, offering improved predictive capabilities. Through this critical review, key phenomena, such as adsorption layers, fluid–solid interactions, and pore surface heterogeneity, are examined across studies, and persistent challenges are identified. Despite notable progress, challenges remain in replicating true reservoir conditions, bridging microscale and continuum models, and fully characterizing multiphase interface dynamics. By consolidating recent progress and perspectives, this review not only summarizes the state-of-the-art but underscores remaining knowledge gaps and future directions in micro/nanopore flow research. Full article