Search Results (95)

The rapid proliferation of Internet of Things (IoT) devices in cloud-based e-learning platforms has posed significant security risks, particularly in protecting learner information, authentication of devices, and safe communication in the highly heterogeneous learning settings. Current cryptographic solutions are largely based on classical public-key infrastructure (PKI) protocols such as RSA and ECC, which will become vulnerable with the advent of large-scale quantum computers capable of executing Shor’s algorithm. In addition, traditional perimeter-based security models are inadequate for handling the dynamics, scattered, and resource-limited characteristics of IoT-enabled educational systems. As a solution to these problems, this paper introduces ZeroTrustEdu, a scalable zero-trust cryptographic solution that combines lightweight post-quantum key management with adaptive trust scoring of cloud-connected IoT e-learning infrastructure. The proposed framework makes three fundamental contributions namely: (1) a hierarchical zero-trust security model with no implicit trust, operating across device, edge, and cloud layers; (2) a lightweight key distribution protocol based on the Module-Lattice Key Encapsulation Mechanism (ML-KEM) compliant with NIST FIPS 203 standards and (3) an adaptive behavioral trust scoring engine that dynamically adjusts device and user trust levels based on real-time interaction analytics. The architecture is evaluated using extensive NS-3 network simulations with up to 100,000 concurrent IoT nodes with formal security analysis under Chosen Plaintext Attack (CPA) and Chosen Ciphertext Attack (CCA) threat models. Comparative evaluation against RSA-2048, ECC-P256, and AES-256 baselines demonstrates that, ZeroTrustEdu delivers a 62% ± 3% (95% CI, 10 independent runs) reduction in ML-KEM encapsulation latency (12.8 ms for key encapsulation/decapsulation, contributing to a complete device authentication latency of 47.3 ms including ML-DSA signature operations), 45% reduced communication overheads, and 38% reduction in energy consumption on ARM Cortex-M4 constrained devices compared to RSA-2048 and achieves provable post-quantum security reducible to the hardness of the Module Learning With Errors (MLWE) problem. These findings demonstrate that the proposed architecture provides a viable, scalable, and quantum-resilient security solution for next-generation IoT-enabled e-learning environments. The cryptographic security of ZeroTrustEdu is guaranteed at the primitive level through NIST-standardized ML-KEM (FIPS 203) and ML-DSA (FIPS 204), with IND-CCA2 and EUF-CMA security formally proven in the respective standards; full protocol-level formal verification using automated theorem provers (ProVerif, Tamarin) is identified as valuable future work to rule out protocol-composition vulnerabilities beyond primitive-level guarantees. Full article

Additive manufacturing has emerged as a powerful approach for producing architected materials with tailored mechanical properties and enhanced energy absorption capabilities. By enabling precise control over geometry, relative density, and hierarchical topology, additive manufacturing facilitates the design of lightweight cellular structures with superior crashworthiness compared to conventional energy-absorbing materials. This review provides a comprehensive overview of recent advances in additively manufactured energy-absorbing structures, with particular emphasis on the interplay between structural architecture, fabrication technologies, and mechanical performance. Key additive manufacturing processes, including fused deposition modeling, stereolithography, selective laser sintering, and multi-jet fusion, are evaluated in terms of their fabrication capabilities, material compatibility, and inherent limitations. Special attention is given to the mechanical behavior of representative architectures, including two-dimensional cellular structures, three-dimensional lattice geometries, sandwich systems, and emerging four-dimensional programmable materials. Depending on topology and material system, additively manufactured lattices can achieve specific energy absorption values exceeding 20–40 J g⁻¹, significantly outperforming many conventional foams. Finally, current challenges, such as process-induced defects, anisotropic mechanical behavior, and the lack of standardized testing methodologies, are discussed, along with future research directions, including multi-material printing, functionally graded architectures, and adaptive metamaterials for next-generation impact mitigation systems. Full article

The trade-off between strength and ductility remains a pivotal challenge in the development of multi-principal element alloys (MPEAs) for structural applications. Here, we report a dual-scale ordering strategy to achieve triple strengthening in a Ni-26.6Co-18.4Cr-5.4Nb-4.1Mo-2.3Al-0.3Ti-0.05Y (wt.%) MPEA through the synergistic interplay of L1₂ nanoprecipitates and local chemical order (LCO). The alloy was processed via cold rolling followed by aging at 750 °C for 8 h, resulting in a high density of coherent L1₂ precipitates (average size 47 ± 1 nm, volume fraction ~27%) with an ultra-low lattice misfit of 0.5%. Additionally, sub-nanoscale LCO domains with an average diameter of 0.62 nm were identified within the face-centered cubic matrix. This hierarchical microstructure yields an exceptional combination of mechanical properties at room temperature: yield strength of 1480 ± 6 MPa, ultimate tensile strength of 1678 ± 10 MPa, and a total elongation of 13.9 ± 0.2%. Quantitative strengthening analysis reveals that precipitation strengthening (697 MPa) is the dominant contributor, followed by dislocation strengthening (397 MPa). Transmission electron microscopy characterization of deformed samples reveals that the low stacking fault energy, promoted by LCO, facilitates the dissociation of perfect dislocations and the formation of extensive stacking faults. The intersection of stacking faults on different {111} planes generates a large number of Lomer–Cottrell locks, which significantly enhance work hardening and delay plastic instability. The findings demonstrate that engineering dual-scale ordered structures offers a promising pathway for developing MPEAs with a superior strength-ductility combination. Full article

This paper develops a rigorous mathematical and computational framework for four-dimensional chess defined on the discrete hypercubic lattice $\{1,\dots , 8{\}}^4$. We formalize piece movement using displacement sets in ${\mathbb{Z}}^4$, define adjacency via the Chebyshev metric, and analyze the resulting move graphs for rooks, bishops, knights, queens, and kings. We establish exact mobility formulas, parity invariants, and connectivity properties, consolidating known product-graph results for rooks and kings while introducing a boundary-sensitive analysis of the four-dimensional knight verified by exhaustive enumeration. The mathematical framework is complemented by a fully implemented 4D chess engine and interactive visualization environment rendering all 64 (z,w)-slices of the hypercube simultaneously. The system supports full move legality, generalized special rules, multi-king checkmate detection, and reproducible state enumeration. Performance measurements and exploratory branching-factor estimates are obtained through reproducible random playouts using the publicly available implementation. We contextualize this ruleset within existing work on move graphs on ${\mathbb{Z}}_n^m$, higher-dimensional leapers, spectral properties of grid graphs, toroidal analogs, and multidimensional visualization. Exploratory qualitative feedback (N = 18) is included to examine whether the visualization design is interpretable and navigable in practice, providing feasibility-oriented observations on how slice-based 4D projection and layered board rendering are perceived by non-expert users in an exploratory context. Together, the mathematical results, implemented engine, and visualization form a coherent foundation for the study of strategy, complexity, and human interaction in four-dimensional game systems. The framework provides a basis for future investigations into spectral analysis of move graphs, symmetry-aware search, hierarchical planning, and educational applications in high-dimensional geometry. Full article

Dust pollution has emerged as a critical issue in a wide range of industrial applications, creating an urgent demand for effective strategies to mitigate particle deposition. Recent experimental studies have demonstrated that superhydrophobic coatings represent a promising class of self-cleaning materials, primarily attributed to their hierarchical rough structures and intrinsically low surface energy. Nevertheless, the underlying self-cleaning mechanisms of superhydrophobic surfaces have not yet been fully elucidated. This work examines particle deposition on superhydrophobic surfaces featuring stochastic roughness distributions through computational modeling. Surface topographies were generated using Fast Fourier Transform techniques. An integrated lattice Boltzmann–discrete element method (LBM–DEM) framework simulated particle transport in superhydrophobic-coated channels. Particle–fluid coupling was achieved via the immersed moving boundary approach, while particle–surface interactions employed a modified Johnson–Kendall–Roberts (JKR) adhesion model. Parametric studies quantified effects of particle size, interfacial energy, flow Reynolds number, and topographical statistics on deposition dynamics. Experimental validation demonstrates good agreement between numerical predictions and measurements. Smaller particles exhibit a lower tendency to deposit on superhydrophobic surfaces, whereas increasing surface energy significantly enhances particle deposition due to stronger adhesion forces and the suppression of particle resuspension. In addition, higher Reynolds numbers effectively reduce particle deposition. The revealed self-cleaning mechanisms provide theoretical guidance for the design of high-performance self-cleaning coatings, and the identified effects of particle and surface parameters offer practical insights for anti-pollution engineering applications. Full article

Tin selenide (SnSe) is a sustainable, lead-free IV–VI semiconductor whose layered orthorhombic crystal structure induces pronounced electronic and phononic anisotropy, enabling diverse energy-related functionalities. This review systematically summarizes recent progress in understanding the structure–property–processing relationships that govern SnSe performance in thermoelectric and optoelectronic applications. Key crystallographic characteristics are first discussed, including the temperature-driven Pnma–Cmcm phase transition, anisotropic band and valley structures, and phonon transport mechanisms that lead to intrinsically low lattice thermal conductivity below 0.5 W m⁻¹ K⁻¹ and tunable carrier transport. Subsequently, major synthesis strategies are critically compared, spanning Bridgman and vertical-gradient single-crystal growth, spark plasma sintering and hot pressing of polycrystals, as well as vapor- and solution-based thin-film fabrication, with emphasis on process windows, stoichiometry control, defect chemistry, and microstructure engineering. For thermoelectric applications, directional and temperature-dependent transport behaviors are analyzed, highlighting record thermoelectric performance in single-crystal SnSe at hi. We analyze directional and temperature-dependent transport, highlighting record thermoelectric figure of merit values exceeding 2.6 along the b-axis in single-crystal SnSe at ~900 K, as well as recent progress in polycrystalline and thin-film systems through alkali/coinage-metal doping (Ag, Na, Cu), isovalent and heterovalent substitution (Zn, S), and hierarchical microstructural design. For optoelectronic applications, optical properties, carrier dynamics, and photoresponse characteristics are summarized, underscoring high absorption coefficients exceeding 10⁴ cm⁻¹ and bandgap tunability across the visible to near-infrared range, together with interface engineering strategies for thin-film photovoltaics and broadband photodetectors. Emerging applications beyond energy conversion, including phase-change memory and electrochemical energy storage, are also reviewed. Finally, key challenges related to selenium volatility, performance reproducibility, long-term stability, and scalable manufacturing are identified. Overall, this review provides a process-oriented and application-driven framework to guide the rational design, synthesis optimization, and device integration of SnSe-based materials. Full article

The oxygen evolution reaction (OER) in water splitting involves complex multi-electron–proton transfer processes and represents the rate-determining step limiting overall electrolysis efficiency. Developing non-noble-metal catalysts with high activity and stability is therefore essential. Herein, a heterogeneous synthesis strategy was employed to in situ construct an iron-rich layered sulfate precursor (Fe_0.42Co_0.58-SO₄/NF) on nickel foam, which underwent deep self-reconstruction in alkaline electrolyte to form nanoflower-like Fe_0.42Co_0.58OOH/NF. The optimized catalyst maintained its iron-rich composition and hierarchical structure, delivering outstanding OER performance with an overpotential of 220 mV at 10 mA·cm⁻², a Tafel slope of 31.9 mV·dec⁻¹, and stability exceeding 12 h at 600 mA·cm⁻². Synchrotron analyses revealed dynamic transitions between mono-μ-O and di-μ-O Fe–M (M = Fe, Co) oxygen bridges during reconstruction, which enhanced both structural robustness and active-site density. The Fe-rich environment promoted the formation of Fe³⁺–O–Fe³⁺ units that synergized with Co⁴⁺ species to activate the lattice oxygen mechanism (LOM), thereby accelerating OER kinetics. This work elucidates the key role of oxygen-bridge geometry in optimizing catalytic activity and durability, providing valuable insights into the rational design of Fe–Co-based non-noble-metal catalysts with high iron content for efficient water oxidation. Full article

Polyimide aerogels (PAs) are ideal for applications in thermal protection, lightweight electronics, and energy devices due to their excellent mechanical properties, ultra-low density, extremely low thermal conductivity, and high thermal-oxidative stability. Conventional PA manufacturing involves a sol–gel process followed by post-processing (drying and imidization). However, PAs fabricated using this method are geometrically limited by the mold shape and are fragile, have poor sample machinability, and are prone to shrinkage and deformation. Direct ink writing (DIW) additive manufacturing (AM) overcomes these limitations of conventional manufacturing processes by extruding ink to construct architectural lattices with high dimensional fidelity, enabling the fabrication of complex, conformal, and multi-scale structures. DIW AM can produce PA components that are thermally and electrically stable, as well as geometric freedom, thus supporting high-precision and functional hierarchical design. This review provides the first overview of DIW AM of PAs. By summarizing printable ink formulations, printing parameters, drying routes and thermal/chemical imidization processes, as well as applications of printed samples, it comprehensively describes the current state of the art in DIW additive manufacturing of PAs and highlights key technical bottlenecks (printability vs. porosity trade-off, economic and environmental, etc.). It also outlines possible future research directions. Full article

Triply Periodic Minimal Surfaces (TPMSs) are mathematically defined surfaces that exhibit periodicity in three dimensions while maintaining a minimal surface property. TPMS-based lattices have gained significant attention in recent years, fueled by advancements in Additive Manufacturing (AM). These structures exhibit exceptional mechanical, thermal, and mass transfer properties, positioning them as a promising class of next-generation materials. However, fully leveraging their potential requires a comprehensive understanding of their design, properties, optimization, and applications. Given the hierarchical nature of TPMSs, achieving optimal performance requires multiscale optimization at the macro- and micro-levels. Addressing these complexities requires advanced computational methods to balance structural integrity and functional performance. In this narrative review, design strategies like functional grading and hybridization to create optimized TPMS-based lattices are summarized. Herein, the performance of such lattices in the mechanical, thermal, and mass transfer domains is focused upon. The role of topology optimization (TO) in the creation of architectured materials for specific application is discussed along with the emerging integration of machine learning. Furthermore, multidisciplinary applications of TPMS structures are examined, particularly in heat sinks, interpenetrating phase composites (IPCs), and biomimetic scaffolds, with their potential to enhance heat dissipation, structural resistance, and biomimicry of biological scaffolds. In addition, various additive manufacturing technologies for fabricating TPMS structures are reviewed, emphasizing how additive manufacturing allows high reproducibility construction of their complex geometry in a precise manner. Further unexplored areas of research are also discussed. Full article

Rare-earth oxides possess distinctive electronic configurations, tunable oxidation states, and inherent structural robustness, making them highly attractive for advanced energy storage applications. Among these, neodymium oxide (Nd₂O₃) stands out due to its high surface redox activity, structural stability, and favorable band alignment, enabling efficient charge storage in electrochemical devices. In this study, Nd₂O₃ electrodes were synthesized via a sol–gel method with systematically varied precursor concentrations (1 mM, 3 mM, and 5 mM) to elucidate the impact of synthesis on crystallinity, morphology, and electrochemical performance. X-ray diffraction (XRD) confirmed the formation of the hexagonal Nd₂O₃ phase, with the 3 mM sample (Nd-2) exhibiting the sharpest reflections, indicative of enhanced crystallinity and reduced lattice defects. X-ray photoelectron spectroscopy (XPS) revealed trivalent Nd species and both lattice and surface oxygen, providing abundant redox-active sites. Field Emission Scanning Electron Microscope (FE-SEM) showed Nd-2 possessed a hierarchically interconnected fibrous network decorated with fine granules, maximizing active surface area and facilitating rapid ion diffusion. Electrochemical testing demonstrated that Nd-2 achieved an areal capacitance of 20 F cm⁻², a diffusion-controlled pseudocapacitive contribution of ~84.9%, and retained 86.3% capacitance over 12,000 cycles. An asymmetric supercapacitor with Nd-2 and activated carbon delivered an energy density of 0.132 mWh cm⁻², power density of 1.8 mW/cm², and 81.1% capacitance retention over 7000 cycles. These results highlight the critical role of precursor concentration in tailoring structure and electrochemical performance, establishing Nd₂O₃ as a promising electrode for high-performance energy storage devices. Full article

This paper proposes and implements a method to efficiently parallelize constraint solving in rigid body simulation using GPUs. Rigid body simulation is widely used in robot development, computer games, movies, and other fields, and there is a growing need for faster computation. As current computers are reaching their limits in terms of scale-up, such as clock frequency improvements, performance improvements are being sought through scale-out, which increases parallelism. However, rigid body simulation is difficult to parallelize efficiently due to its characteristics. This is because, unlike fluid or molecular physics simulations, where each particle or lattice can be independently extracted and processed, rigid bodies can interact with a large number of distant objects depending on the instance. This characteristic causes significant load imbalance, making it difficult to evenly distribute computational resources using simple methods such as spatial partitioning. Therefore, this paper proposes and implements a computational method that enables high-speed computation of large-scale scenes by hierarchically clustering rigid bodies based on their number and associating the hierarchy with the hardware structure of GPUs. In addition, to effectively utilize parallel computing resources, we considered a more relaxed parallelization condition for the conventional Gauss–Seidel block parallelization method and demonstrated that convergence is guaranteed. We investigated how speed and convergence performance change depending on how much computational cost is allocated to each hierarchy and discussed the desirable parameter settings. By conducting experiments comparing our method with several widely used software packages, we demonstrated that our approach enables calculations at speeds previously unattainable with existing techniques, while leveraging GPU computational resources to handle multiple rigid bodies simultaneously without significantly compromising accuracy. Full article

This research explores microcrystalline diamond particles in poly(L-lactic acid) matrices to create structured porous composites for advanced biodegradable materials. While nanodiamond–polymer composites are well-documented, microcrystalline diamond particles remain unexplored for controlling hierarchical porosity in systems required by tissue engineering, thermal management, and filtration industries. We investigate diamond–polymer composites with concentrations from 5 to 75 wt% using freeze-drying methodology, employing two particle sizes: 0.125 μm and 1.00 μm diameter particles. Systematic porosity control ranges from 11.4% to 32.8%, with smaller particles demonstrating reduction from 27.3% at 5 wt% to 11.4% at 75 wt% loading. Characterization through infrared spectroscopy, X-ray computed microtomography, and Raman analysis confirms purely physical diamond–polymer interactions without chemical bonding, validated by characteristic diamond lattice vibrations at 1332 cm⁻¹. Thermal analysis reveals modified crystallization behavior with decreased melting temperatures from 180 to 181 °C to 172 °C. The investigation demonstrates a controllable transition from large-volume interconnected pores to numerous small-volume closed pores with increasing diamond content. These composites provide a quantitative framework for designing hierarchical structures applicable to tissue engineering scaffolds, thermal management systems, and specialized filtration technologies requiring biodegradable materials with engineered porosity and enhanced thermal conductivity. Full article

Hierarchically porous polymers can unite macro-scale architected voids with micro-scale pores, enabling unique combinations of low density, high surface area, and controlled transport properties that are difficult to achieve with traditional methods. This review outlines the current advancements in creating such multiscale architectures using fused filament fabrication (FFF), the most widely used polymer additive manufacturing technique. Unlike earlier reviews that consider lattice architectures and foaming chemistries separately, this work integrates both within a single analysis. It begins with an overview of FFF fundamentals and how process parameters affect macropore formation. Design strategies for achieving macroporosity (≳100 µm) with a single thermoplastic are presented and categorized: 2D infill patterns, strut-based lattices, triply periodic minimal surfaces (TPMS), and Voronoi structures, along with functionally graded approaches. The discussion then shifts to functional filaments incorporating chemical or physical blowing agents, thermally expandable or hollow microspheres, and sacrificial porogens, which create microporosity (≲100 µm) either in situ or through post-processing. Each material approach is connected to case studies that demonstrate its application. A comparative analysis highlights the advantages of each method. Key challenges such as viscosity control, thermal gradient management, dimensional instability during foaming, environmental concerns, and the absence of standardized porosity measurement techniques are addressed. Finally, emerging solutions and future directions are explored. Overall, this review provides a comprehensive perspective on strategies that enhance FFF’s capability to fabricate hierarchically porous polymer structures. Full article

This study, inspired by the impact resistance of the pistol shrimp’s predatory claw, investigates the design and optimization of bionic energy absorption structures. Four types of bionic hierarchical porous metamaterial lattice structures with a negative Poisson’s ratio were developed based on the microstructure of the pistol shrimp’s fixed claw. These structures were validated through finite element models and quasi-static compression tests. Results showed that each structure exhibited distinct advantages and shortcomings in specific evaluation indices. To address these limitations, four new bionic structures were designed by coupling the characteristics of the original structures. The coupled structures demonstrated a superior balance across various performance indicators, with the EOS (Eight pillars Orthogonal with Side connectors on square frame) structure showing the most promising results. To further enhance the EOS structure, a parametric study was conducted on the distance d from the edge line to the curve vertex and the length-to-width ratio y of the negative Poisson’s ratio structure beam. A fifth-order polynomial surrogate model was constructed to predict the Specific Energy Absorption (SEA), Crush Force Efficiency (CFE), and Undulation of Load-Carrying fluctuation (ULC) of the EOS structure. A multi-objective genetic algorithm was employed to optimize these three key performance indicators, achieving improvements of 1.98% in SEA, 2.42% in CFE, and 2.05% in ULC. This study provides a theoretical basis for the development of high-performance biomimetic energy absorption structures and demonstrates the effectiveness of coupling design with optimization algorithms to enhance structural performance. Full article

This study investigates the microstructural evolution and mechanical response of an additively manufactured (PBF-LB/M) AlSi10Mg alloy subjected to severe plastic deformation via two passes of twist channel angular pressing (TCAP). Processing was conducted using Route Bc, with the first pass at 150 °C and the second at 250 °C. For the first time, the evolution from the initial hierarchical AM structure to a refined state was characterized in high-fidelity detail using a novel EBSD detector. The two-pass process transformed the initial structure into a heterogeneous, bimodal microstructure existing in a non-equilibrium state, characterized by a high fraction of low-angle grain boundaries (63%) and significant internal lattice distortion. The mechanical properties were dictated by the processing temperature: a single pass at 150 °C induced work hardening, increasing the yield strength from 450 MPa to 482 MPa. Conversely, the second pass at an elevated temperature of 250 °C promoted significant dynamic recovery. This led to a decrease in yield strength to 422 MPa but concurrently resulted in a substantial increase in ultimate compressive strength to 731 MPa. Full article