Search Results (508)

Two-dimensional (2D) Ruddlesden–Popper (RP) tin halide perovskites have attracted considerable attention as lead-free photovoltaic absorbers; however, the impact of organic A-site cations on their structure and pressure-dependent optoelectronic behavior remains underexplored. In this study, density functional theory (DFT) is used to investigate the structural, electronic, and optical properties of A₂SnI₄ (A = GUA⁺, DMA⁺, MA⁺) under ambient conditions and under hydrostatic pressure. All three compounds adopt layered frameworks in which the organic cations occupy the interlayer region, while SnI₆ octahedra form the inorganic slabs. Band-gap calculations are performed using HSE06 for ambient pressure, known for its accuracy in electronic structure predictions, and PBE for pressure simulations, due to its computational efficiency in large-scale systems. At ambient pressure, Hybrid-functional (HSE06) calculations indicate that all three materials are direct-gap semiconductors, with band gaps of 2.25 eV for MA2SnI₄, 2.98 eV for DMA2SnI4, and 2.85 eV for GUA2SnI4. Under hydrostatic compression, DMA₂SnI₄ shows comparatively modest band-gap variation and saturates near 1.7 eV. In contrast, GUA₂SnI₄ and MA₂SnI₄ exhibit pronounced band-gap narrowing, including a pressure-induced direct-to-indirect transition near 2 GPa, with band gaps decreasing to 0.59 eV (GUA₂SnI₄) and 0.34 eV (MA₂SnI₄) at elevated pressures. Overall, these findings highlight that A-site chemistry, combined with hydrostatic pressure, enables tuning the electronic and optical responses in tin-based 2D RP perovskites, demonstrating their promise as tunable, lead-free photovoltaic absorbers. Full article

Delamination is a major failure Mode in laminated composites, typically triggered by premature interlaminar matrix cracking and leading to severe structural degradation. To address this, various through-thickness reinforcement strategies have been explored, including three-dimensional woven architecture. Although these designs significantly improve delamination resistance, their industrial adoption stays limited due to reproducibility challenges and the high cost and operational complexity of advanced manufacturing systems needed for controlled through-thickness reinforcement. This study investigates an alternative interlaminar reinforcement method, through-thickness stitching, aimed at enhancing Mode-I delamination resistance of a commercial fiberglass laminate without changing its native architecture. Composites were manufactured using a low-viscosity epoxy infusion system (MAX 1618 A/B) and a [0/90] biaxial fiberglass fabric. An eight-filament polyethylene thread (Ø = 0.12 mm) was introduced in predefined stitch architectures consisting of three longitudinal patterns having two, three, and five continuous stitch lines, referred to as AV, BV and CV samples, respectively. Results show that stitching highly increases Mode-I interlaminar fracture toughness G_IC by 0.3808, 0.4152 and 0.5192 kJ/m² for AV, BV and CV respectively, compared to 0.0265 kJ/m² for the unstitched composite O, highlighting the strong influence of stitch orientation and spacing on interlaminar performance. But scanning electron microscopy revealed added failure mechanisms in stitched specimens, including localized fiber misalignment of up to 33° and resin-rich regions approximately 0.6 mm in length, suggesting that while stitching enhances delamination resistance, it may also influence other mechanical properties. Full article

Phase-change materials of the Ge–Sb–Te (GST) system are promising for non-volatile memory and programmable photonics owing to their reversible amorphous–crystalline transitions. Among these materials, GeSb₄Te₇ stands out for its optimal balance of thermal stability, switching speed, and energy efficiency. The properties of GST materials are critically dependent on structural defects, particularly germanium vacancies that occur during synthesis and operation. Using density functional theory, we demonstrate that Ge vacancies and Ge–Sb intermixing significantly influence the electronic and optical properties of GeSb₄Te₇. Positive binding energies reveal vacancy clustering tendencies, which systematically reduce p-type degeneracy and widen the band gap (from 0.47 to 0.67 eV at a 2.7% vacancy concentration). Consequently, the metallic optical response in the visible range diminishes, as reflected in the less negative real dielectric function. Furthermore, we extend our investigation to the fundamental building block of this material system, the GeSb₂Te₄ monolayer. By studying controlled interlayer displacements of Ge and Te atoms in an otherwise stoichiometric slab, we elucidate the switching mechanism in the two-dimensional limit. The pristine monolayer exhibits semiconducting behavior with an indirect band gap of 0.74 eV, while layer sliding induces a semiconductor-to-metal transition accompanied by pronounced changes in the optical absorption spectrum. The asymmetric energy barrier (1.69 eV forward, 0.60 eV reverse) suggests favorable reversible switching via structural distortions alone, without requiring chemical modifications. The obtained results, spanning from defective bulk crystals to structurally distorted monolayers, are important for the targeted optimization of GST material properties in memory devices, optical elements, and emerging nanoscale phase-change applications. Full article

In the post-Moore’s Law era, conventional Von Neumann architectures face critical limitations, such as the “memory wall” and excessive power consumption, particularly when processing unstructured data. Neuromorphic computing, inspired by the human brain, offers a promising solution through parallel processing and adaptive learning. Among the candidates for artificial synapses, memristors based on two-dimensional MXenes (specifically Ti₃C₂T_x) have attracted significant attention due to their unique layered structure, high metallic conductivity, and tunable physicochemical properties. This review provides a comprehensive analysis of MXene-based memristors, from material synthesis to system-level applications. We examine how different synthesis strategies, including etching methods, directly influence device performance and elucidate the underlying resistive switching mechanisms driven by ion migration, valence change, and interfacial processes. Furthermore, the review demonstrates the efficacy of MXenes in emulating biological synaptic functions—such as spike-timing-dependent plasticity (STDP) and long-term potentiation/depression (LTP/LTD)—and their application in tasks like handwritten digit recognition. Finally, we highlight emerging frontiers in flexible electronics and in-sensor computing, offering insights into the future trajectory of integrated sensing, memory, and computation. Full article

Silicon (Si) has attracted extensive attention as a promising anode material for next-generation lithium-ion batteries (LIBs) due to its ultra-high theoretical capacity, low lithiation potential, and economic advantages. However, drastic volume expansion during cycling and slow reaction kinetics severely compromise its structural stability and practical application. Recently, two-dimensional (2D) MXenes have been explored as effective functional components in Si-based electrodes because of their excellent electrical conductivity, high specific surface area, adjustable surface terminations, and mechanical robustness. When integrated with Si, MXenes serve as conductive matrices that alleviate volumetric stress, enhance charge transport, and accelerate electron/ion diffusion. Consequently, Si/MXene nanocomposites (NCs) exhibit significantly improved lithium (Li) storage performance. This review outlines recent advances in Si/MXene NCs, covering fabrication strategies, structural engineering, and various configurations, including particulate materials, three-dimensional (3D) architectures, films, and fibrous systems, and establishes the relationship between structural design and electrochemical behavior. Remaining challenges and prospective research directions are also discussed to guide the development of high-energy-density LIB anodes. Full article

Diamond coatings with three distinct surface textures, namely spherical, pyramidal, and prismatic morphologies, were fabricated using the hot-filament chemical-vapor deposition (HFCVD) method. Scanning electron microscopy (SEM) was employed to analyze the surface morphological characteristics and differences among the coatings. Raman spectroscopic analysis further confirmed that all three diamond films exhibited excellent deposition uniformity and high crystalline quality. A three-dimensional optical microscopy system was used to measure the surface roughness values, which were determined to be Ra 0.423 μm, Ra 0.515 μm, and Ra 0.809 μm, respectively. An HFCVD diamond-coated tool was innovatively employed for the lapping of sapphire wafers, enabling a systematic investigation of the tribological behavior during the lapping process. Based on the experimental results, three morphological material removal models were established. The study demonstrates that the spherical diamond coating achieves a superior surface finish (Ra 0.22 μm) due to its continuous multi-point contact geometry, governed by the agglomerated nanocrystalline structure. Sample 3 had the highest removal rate of 24.3 μm/min. This is related to its surface morphology characteristics and is also due to the two-body contact between the diamond-coated tool and sapphire, offering a high-efficiency alternative for precision machining. Full article

Large-scale advanced energy systems, including fusion devices, high-power plasma sources, and accelerator-driven energy platforms, increasingly depend on real-time, hardware-level data processing for diagnostics, control, and protection. In such installations, ultra-low latency, deterministic throughput, and multi-decade operational lifetimes are not optional design goals but strict system-level requirements. While similar timing constraints exist in high-energy physics infrastructures, energy applications place a stronger emphasis on long-term stability, maintainability, and reproducibility of digital signal processing pipelines. This work investigates whether high-level synthesis (HLS) provides a practical and sustainable design methodology for implementing both classical pattern-based and compact neural network (NN) trigger logic on Field-Programmable Gate Arrays (FPGAs) under realistic energy-system constraints. Using representative commercial toolchains (Intel HLS and hls4ml) as reference workflows, we demonstrate the capabilities of fixed-point, fully pipelined streaming architectures, while also identifying critical shortcomings of pragma-driven HLS approaches in terms of architecture transparency, long-term portability, and systematic multi-objective design-space exploration, all of which are crucial for long-lived energy projects and plasma diagnostic systems. These limitations directly motivate the development of a custom, vendor-agnostic, extensible HLS framework (PyHLS), specifically oriented toward deterministic latency, reproducibility, and physics-grade verification demands of advanced energy infrastructures. Gas Electron Multipliers (GEMs) are modern gaseous detectors increasingly employed in plasma diagnostics, radiation monitoring, and high-power energy experiments, where high rate capability, fine spatial resolution, and radiation tolerance are required. Their massively parallel signal structure and continuous data streams make GEMs a representative and demanding benchmark for FPGA-based real-time trigger and preprocessing systems in energy-related environments. The primary objective of this study is to establish a pragmatic technological baseline, demonstrating that contemporary HLS workflows can reliably support both template-based and neural inference-based trigger architectures within strict timing, resource, and power constraints typical for advanced energy installations. Furthermore, we outline a scalable development path toward multi-channel and two-dimensional (pixelated) GEM readout architectures, directly applicable to fusion diagnostics, plasma accelerators, beam–plasma interaction studies, and radiation-hard energy monitoring platforms. Although the proposed methodology remains fully transferable to large-scale physics trigger systems, its principal relevance is directed toward real-time diagnostics and protection layers in next-generation energy systems. Full article

We investigate how symmetries present in datasets affect the structure of the latent space learned by Variational Autoencoders (VAEs). Understanding symmetries in data is essential because symmetries determine the true degrees of freedom, constrain generalization, and provide physically interpretable coordinates. We therefore study whether a standard, non-equivariant VAE can reveal symmetry-induced dimensional reduction directly from data, without imposing the symmetry in the architecture. By training VAEs on data originating from simple mechanical systems and particle collisions, we analyze the organization of the latent space through a relevance measure that identifies the most meaningful latent directions. We show that when symmetries or approximate symmetries are present, the VAE self-organizes its latent space, effectively compressing the data along a reduced number of latent variables. This behavior captures the intrinsic dimensionality determined by the symmetry constraints and reveals hidden relations among the features. Furthermore, we provide a theoretical analysis of a simple toy model, demonstrating how, under idealized conditions, the latent space aligns with the symmetry directions of the data manifold. We illustrate these findings with examples ranging from two-dimensional datasets with $O\left(2\right)$ symmetry to realistic datasets from electron–positron and proton–proton collisions. Our results highlight the potential of unsupervised generative models to expose underlying structures in data and offer a novel approach to symmetry discovery without explicit supervision. Full article

Photodynamic antibacterial therapy presents a promising strategy for combating bacterial infections due to its non-invasive nature and low potential for inducing resistance. In this work, we developed a series of electron beam-modified graphitic carbon nitride (g-C₃N₄, CN) and titanium carbide (Ti₃C₂, TC) nanocomposites, which were subsequently incorporated into polyvinyl alcohol/sodium alginate (PVA/SA) hydrogels through physical cross-linking. The optimized 200CN/1TC composite hydrogel (where 200CN denotes 200 kGy irradiation dose, and 1TC represents 1 wt% TC content) maintained excellent biocompatibility with cell viability exceeding 80% even at the highest nanomaterial loading (8% 200CN/1TC). Notably, the 8% 200CN/1TC composite hydrogel displayed substantial antibacterial activity, forming inhibition zones of 12.3 mm and 10.8 mm against Staphylococcus aureus and Escherichia coli, respectively. The improved performance may be explained by the combined effects of enhanced electron transfer between the component materials and the unique two-dimensional structure of the nanocomposites, though further investigation is required to fully elucidate the underlying mechanisms. This study provides a feasible approach for developing efficient antibacterial hydrogel systems and offers valuable perspectives on the design of nanomaterial-based biomedical materials for wound healing and infection control applications. Full article

Efficient condensation is fundamental for high-performance passive two-phase heat transfer devices, such as grooved heat pipes, which are widely used in thermal management for electronic, automotive, aerospace and energy systems. Enhancing condensation heat transfer requires precise control of the condensate distribution and liquid drainage, which can be achieved through the optimization of fin geometry. This study investigates the condensation heat transfer over rectangular, trapezoidal and inverted trapezoidal fins under horizontal and vertical downflow conditions for four refrigerants (R134a, R245fa, R290 and R717) by means of three-dimensional steady-state CFD simulations using the volume-of-fluid (VOF) method. The fin surfaces, inspired by grooved wick heat pipes, are aimed at improving condensate removal and overall condensation heat transfer. The numerical model is validated through comparison with experimental data taken from the literature. Numerical results show that ammonia achieves the highest condensation heat transfer, due to its favorable thermophysical properties. In horizontal flow, inverted trapezoidal and rectangular fins yield up to 10% higher heat transfer than trapezoidal fins, with the inverted trapezoid promoting a more uniform condensate film. Vertical downflow enhances gravity-driven drainage, producing thinner, more stable films and up to 88% higher local heat flow rates in the grooves. These results provide insights into the coupled influence of geometry, working fluid, and flow conditions on condensation mechanisms, offering useful guidelines for the design and optimization of condensers in passive heat transfer devices. Full article

The emergence of advanced low-dimensional materials of the graphene family opens up unique opportunities for energy-efficient and fast processing of electrical and optical signals in a wide spectral range from ultraviolet to infrared. Non-volatile resistive states in memristors based on two-dimensional (2D) crystals, 1D nanoribbons, and 0D quantum dots are accessible for control by light and an electric field due to polarization and rearrangement of sp²-sp³ hybridization of carbon atoms, as well as due to photoinduced phase transitions. Two-dimensional materials possess unique structural and electronic properties required for the development of highly efficient nanoenergy memristor devices for low-energy information technology. This article discusses memristors and photomemristors based on graphene, graphene oxide, diamane, and chalcogenide semiconductors such as MoS₂, WSe₂, MoS_2−xO_x, which are structurally similar to graphene and have a 2D layered structure. Memristors based on graphene and graphene oxide, bigraphene, and diamane, fabricated using localized electron irradiation, exhibit nonlinear behavior and well-controlled memristive states associated with sp²-sp³ transitions of carbon atoms under low-power conditions. The review highlights the dual role of graphene as an active material and electrode, as well as the redox control mechanism. Due to a well-controlled redox process, graphene-based devices exhibit the dynamic behavior required for neuromorphic computing directly in the sensor, reducing the energy and time costs associated with data processing. Neuromorphic computing in a photomemristor-based sensor enables the creation of a compact nano-energy system for real-time information recognition in a wide spectral range, similar to biological vision, for use in self-driving cars, personalized medicine, and other applications. Full article

Two-dimensional (2D) materials offer exceptional electrical, optical, and mechanical properties but face challenges in terms of scalability, stability, and integration. Hybridizing 2D materials with polymers provides an effective route to overcome these limitations by enabling tunable interfaces, mechanical compliance, chemical functionality, and three-dimensional device processability. This review summarizes the fundamental structural configurations of 2D–polymer hybrids, including embedded composites, stacked heterostructures, covalently functionalized interfaces, polymer-encapsulated layers, and fiber–network architecture, and describes how their interfacial interactions dictate charge transport, environmental robustness, and mechanical behavior. We also highlight major fabrication strategies, such as solution dispersion, in situ polymerization, and vapor-phase deposition. Finally, we discuss emerging applications in sensors, optoelectronics, neuromorphic systems, and energy devices, demonstrating how synergistic coupling between 2D materials and functional polymers enables enhanced sensitivity, programmable electronic states, broadband photodetection, and improved electrochemical performance. These insights provide design guidelines for future multifunctional and scalable 2D–polymer hybrid platforms. Full article

The conversion of alkanes/alkenes into useful intermediates is highly important in the chemical industry. In this study, the physicochemical properties and catalytically active forms of bismuth molybdates (BiMo) were investigated using the selective oxidation of propene to acrolein as a model reaction. The catalysts were prepared by two methods, coprecipitation and spray-drying, with emphasis on spray-drying. The catalysts were characterized using X-ray diffraction, N₂ adsorption/desorption isotherms, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. The catalytic properties of the BiMo samples were studied in a conventional fixed-bed reactor operated under different reaction conditions. The one-dimensional (1D) pseudohomogeneous model was applied to describe the obtained experimental results. The experimental kinetic data were correlated with two complex kinetic models based on multiple reactions (parallel and serial reaction systems). The proposed models were verified by comparing computer simulation data with experimental laboratory results. This study aimed to extend the understanding of the relationship between catalyst composition/structure and catalyst activity/selectivity for different BiMo structures, and to propose kinetic models using two approaches based on parallel and series reactions, in line with efforts to improve the valorization of light olefins. Full article

Electronic and spin structures of open-shell molecules and clusters were investigated as possible building blocks for the construction of one- and two-dimensional quantum spin alignment systems which exhibited several characteristic quantum properties of strongly correlated electron systems: high-T_c superconductivity, quantum spin coherence, entanglement, etc. Ab initio calculations were performed to elucidate effective exchange integrals (J) for 3d transition metal oxides, providing the J-model for high-T_c superconductivity. Theoretical investigations such as Monte Carlo simulation, molecular mechanics and quantum mechanical calculations were performed to elucidate effective chemical procedures for through-bond alignments of open-shell transition metal ions by organometallic conjugation and through-space confinements of molecular spins such as molecular oxygen by molecular confinement materials. Theoretical simulations have elucidated the importance of appropriate confinement materials for alignments of molecular spins desired for quantum coherence and quantum sensing. Equivalent transformations among coherent states of superconductors, trapped ion, neutral atom, molecular spin, molecular exciton, etc., are also discussed on theoretical and conceptual grounds such as quantum entanglement and decoherence. Full article

The morphology of solid surfaces encodes fundamental information about the physical mechanisms that govern their formation. Here, we reinterpret scanning electron microscopy (SEM) micrographs of oxide thin films as two-dimensional self-affine morphology fields (not height-metrology) and analyze them using a multiscale statistical-physics framework that integrates spectral, multifractal, geometric, and topological descriptors. Fourier-based power spectral density (PSD) provides the spectral slope $\beta$ and apparent Hurst exponent $H$, while multifractal scaling yields the information dimensions $D_q$, the singularity spectrum $f\left(\alpha \right)$, and its width $\mathsf{\Delta }\alpha$, which quantify scale hierarchy and intermittency. Lacunarity captures intermediate-scale heterogeneity, and Minkowski functionals—especially the Euler characteristic $\chi \left(\theta \right)$—probe connectivity and identify the onset of a percolation-like network structure. Two representative surfaces with contrasting morphologies are used as model systems: one exhibiting an anisotropic, porous, strongly multifractal structure with fragmented domains; the other showing a compact, nearly isotropic, and nearly monofractal organization. The porous surface/topography displays steep PSD decay, broad multifractal spectra, and positive $\chi$, consistent with a sub-percolated, diffusion-limited, Edwards–Wilkinson-like (EW-like) growth regime. Conversely, the compact surface/topography exhibits gentler spectral slopes, narrower $f\left(\alpha \right)$, enhanced lacunarity at intermediate scales, and a $\chi \left(\theta \right)$ zero-crossing indicative of a connectivity transition where a surface becomes a percolating network, consistent with a Kardar–Parisi–Zhang-like (KPZ-like) correlated growth regime. These results demonstrate that individual SEM micrographs encode quantitative fingerprints of nonequilibrium universality classes and topology-driven transitions from fragmented surfaces to connected networks, showing that SEM intensity maps can serve as a quantitative probe for testing theories of rough surfaces and kinetic growth in experimental thin-film systems. Full article