Search Results (75)

Characterizing and simulating complex reservoirs, particularly unconventional resources with multiscale and non-homogeneous features, presents significant bottlenecks in cost, efficiency, and accuracy for conventional research methods. Consequently, there is an urgent need for the digital and intelligent transformation of the field. To address this challenge, this paper proposes that the core solution lies in the deep integration of physical mechanisms and data intelligence. We systematically review and define a new research paradigm characterized by the trinity of digital cores (geometric foundation), physical simulation (mechanism constraints), and artificial intelligence (efficient reasoning). This review clarifies the core technological path: first, AI technologies such as generative adversarial networks and super-resolution empower digital cores to achieve high-fidelity, multiscale geometric characterization; second, cross-scale physical simulations (e.g., molecular dynamics and the lattice Boltzmann method) provide indispensable constraints and high-fidelity training data. Building on this, the methodology evolves from surrogate models to physics-informed neural networks, and ultimately to neural operators that learn the solution operator. The analysis demonstrates that integrating these techniques into an automated “generation–simulation–inversion” closed-loop system effectively overcomes the limitations of isolated data and the lack of physical interpretability. This closed-loop workflow offers innovative solutions to complex engineering problems such as parameter inversion and history matching. In conclusion, this integration paradigm serves not only as a cornerstone for constructing reservoir digital twins and realizing real-time decision-making but also provides robust technical support for emerging energy industries, including carbon capture, utilization, and sequestration (CCUS), geothermal energy, and underground hydrogen storage. Full article

The influence of solution treatment time on the microstructural and mechanical properties of a super duplex stainless steel was investigated. A solution annealing treatment at 1120 °C was applied to the hot-rolled alloy, with soaking times varying between 10 and 30 min. The microstructural characteristics before and after solution treatment were examined using XRD and EBSD techniques by measuring lattice parameters and micro-strains, weight fraction, average grain size, and maximum misorientation angle. The experimental results showed that the constituent phases are δ-Fe and γ-Fe, regardless of the alloy state. The mechanical properties of the solution-treated alloy were evaluated by tensile testing, measuring the ultimate tensile strength (σ_UTS), yield strength (σ_0.2), fracture strain (ε_f), and impact toughness (KCV). Increasing the solution treatment time from 10 min to 30 min leads to improved ductility and reduced mechanical strength, with the volume of the ferrite phase increasing, the average austenite grain size decreasing, and the maximum misorientation angle decreasing. This is due to the ability of ferrite to absorb stress and to the greater participation of grains in the deformation process. Important decreases in high elastic strains and residual stress fields after solution treatment were also noted. Full article

High-density component-induced metal artifacts in industrial computed tomography (CT) severely impair image quality and make further analysis more difficult. To suppress artifacts and improve image quality, this research suggests a practical approach that combines lightweight attention-enhanced super-resolution networks with Radon-domain artifact elimination. First, the original CT slices are subjected to bicubic interpolation, which enhances resolution and reduces sampling errors during transformation. The Radon transform, which detects and suppresses metal artifacts in the Radon domain, is then used to convert the interpolated pictures into sinograms. The artifact-suppressed sinograms are then reconstructed at better resolution using a lightweight Enhanced Deep Super-Resolution (EDSR) network with a channel attention mechanism, which consists of only one residual block. The inverse Radon transform is used to recreate the final CT images. An average peak signal-to-noise ratio (PSNR) of 40.39 dB and an average signal-to-noise ratio (SNR) of 29.75 dB, with an SNR improvement of 15.48 dB over the original artifact-laden images, show the success of the suggested strategy in experiments. This method offers a workable and effective way to improve image quality in industrial CT applications that involve intricate structures that incorporate metal. 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

Superaustenitic stainless steels (SASSs) are one of the families of high-performance stainless steels, the so-called “super” grades. While sharing the face-centered cubic lattice structure typical of standard austenitic stainless steels, their chemical composition is significantly more complex. This enables them to offer an exceptional balance of superior corrosion resistance and high mechanical strength. However, the intricate chemical makeup of SASSs brings challenges, such as the phenomenon of segregation and precipitation of deleterious intermetallics. Consequently, this leads to several challenges in their processing and use. This work aims to present SASSs in detail, starting from their chemistry and metallurgy and ending with processing and applications. Hence, the first part will be dedicated to the analysis of chemistry, resulting grades, microstructure and secondary phases along with the conditions determining their formation. Afterwards, physical, mechanical and corrosion resistance characteristics will be set forth in such a way as to understand their origin and implications for processing and possible uses, with a focus on processability limitations. In fact, manufacturing and processing options significantly affect the types of products that can be developed, and, when considered alongside material attributes and costs, they help define the target markets for these alloys. Full article

Since 2010, BiCuSeO has emerged as a captivating subject of investigation within the realm of thermoelectric materials. Its allure lies in a remarkable confluence of characteristics: a distinctive natural super-lattice structure, an elevated Seebeck coefficient, and a low thermal conductivity, all of which have collectively piqued the intense interest of scientists worldwide. Over the subsequent eight-year period, an extensive array of research endeavors has been meticulously carried out, delving deep into the multifaceted properties of BiCuSeO and exploring avenues for performance enhancement. In this comprehensive review, we embark on a detailed exploration of the fundamental properties of BiCuSeO, encompassing its preparation methodologies, as well as its thermoelectric and mechanical attributes. A thorough synthesis of diverse strategies for optimizing the composition and structure of BiCuSeO is presented, elucidating how these modifications contribute to the enhancement of its thermoelectric and mechanical performance. Finally, the current state of research on N-type BiCuSeO is systematically summarized, offering a panoramic view of the advancements and challenges in this particular area. Full article

In the field of remote sensing, super-resolution methods based on deep learning have made significant progress. However, redundant feature extraction and inefficient feature fusion can, respectively, result in excessive parameters and restrict the precise reconstruction of features, making the model difficult to deploy in practical remote-sensing tasks. To address this issue, we propose a lightweight Dual Attention Fusion Enhancement Network (DAFEN) for remote-sensing image super-resolution. Firstly, we design a lightweight Channel-Spatial Lattice Block (CSLB), which consists of Group Residual Shuffle Blocks (GRSB) and a Channel-Spatial Attention Interaction Module (CSAIM). The GRSB improves the efficiency of redundant convolution operations, while the CSAIM enhances interactive learning. Secondly, to achieve superior feature fusion and enhancement, we design a Forward Fusion Enhancement Module (FFEM). Through the forward fusion strategy, more high-level feature details are retained for better adaptation to remote-sensing tasks. In addition, the fused features are further refined and rescaled by Self-Calibrated Group Convolution (SCGC) and Contrast-aware Channel Attention (CCA), respectively. Extensive experiments demonstrate that DAFEN achieves better or comparable performance compared with state-of-the-art lightweight super-resolution models while reducing complexity by approximately 10∼48%. Full article

The anomalous flow behavior of the γ′-Ni₃Al phase at high temperature is closely related to a cross-slip of $1/2⟨110⟩\left\{111\right\}$ super-partial dislocations. The acceleration of cross-slips induced by the addition of rhenium (Re) is known as Re-effects. In this work, by means of a series of lattice transitions, a hybrid model including a preexisting anti-phase boundary $\mathrm{APB}\left(111\right)$ was constructed to assess the difficulty of cross-slips of $1/2⟨110⟩\left\{111\right\}$ super-partial dislocations from $\left\{111\right\}$ planes to $\left\{001\right\}$ planes in the γ′-Ni₃Al phases, and the impact of the addition of Re on these dislocation mediated creep resistances was reinvestigated by first-principles calculations. The results showed that the addition of Re at preferential Al sublattice sites was indeed beneficial for the cross-slip of the first leading $1/2⟨110⟩\left\{111\right\}$ super-partial dislocations, and the existence of $\mathrm{APB}\left(111\right)$ could promote cross-slip of second leading $1/2⟨110⟩\left\{111\right\}$ super-partial dislocations. A detailed calculation of stacking fault energies demonstrated that an obvious Suzuki segregation of Re existed at $\mathrm{APB}\left(111\right)$ and $\mathrm{APB}\left(001\right)$, and Re preferentially occupied Ni sublattice sites. It is found Re-segregations at $\mathrm{APB}\left(111\right)$ were disadvantageous for the cross-slip of new $1/2⟨110⟩\left\{111\right\}$ super-partial dislocations, but the formation of more Kear-Wilsdorf dislocation locks could benefit from Re-segregations at $\mathrm{APB}\left(001\right)$. Full article

Bow-tie diodes on the base of modulation-doped semiconductor structures are often used to detect radiation in GHz to THz frequency range. The operation of the bow-tie microwave diodes is based on carrier heating phenomena in an epitaxial semiconductor structure with broken geometrical symmetry. However, the electrical properties of bow-tie diodes are highly dependent on the purity of the grown epitaxial layer—specifically, the minimal number of defects—and the quality of the ohmic contacts. The quality of MBE-grown semiconductor structure depends on the presence of a buffer layer between a semiconductor substrate and an epitaxial layer. In this paper, we present an investigation of the electrical and optical properties of planar bow-tie microwave diodes fabricated using modulation-doped semiconductor structures grown via the MBE technique, incorporating either a GaAs buffer layer or a GaAs–AlGaAs super-lattice buffer between the semi-insulating substrate and the active epitaxial layer. These properties include voltage sensitivity, electrical resistance, I–V characteristic asymmetry, nonlinearity coefficient, and photoluminescence. The investigation revealed that the buffer layer, as well as the illumination with visible light, strongly influences the properties of the bow-tie diodes. Full article

Discovered within the North Himalayan Metallogenic Belt (NHMB), the Zhaxikang Pb–Zn–Ag–Sb deposit stands as the sole super-large scale ore deposit in the region. This deposit holds significant quantities of Pb and Zn (2.066 million tons at 6.38% average grade), Ag (2661 tons at an average of 101.64 g/t), and Sb (0.235 million tons at 1.14% average grade), making it one of China’s foremost Sb–polymetallic deposits. Stibnite represents the main carrier of Sb in this deposit and has been of great attention since its initial discovery. However, the trace element composition of stibnite in the Zhaxikang deposit has not yet been determined. This study carried out an analysis of the distribution patterns and substitution processes of trace elements within stibnite gathered from the Zhaxikang deposit, aiming to provide crucial information on ore-forming processes. Utilizing high-precision laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), we discovered that the studied stibnite is notably enriched in arsenic (~100 ppm) and lead (~10 ppm). Furthermore, the notably consistent time-resolved profiles suggest that elements such as Fe, Cu, As, In, Sn, Hg, and Pb predominantly exist as solid solutions within stibnite. Consequently, it is probable that the enrichment of Cu, Pb, and Sn in stibnite is due to isomorphic substitution reactions, including 3Pb²⁺↔2Sb³⁺, Cu⁺ + Pb²⁺↔Sb³⁺, and In³⁺ + Sn³⁺↔2Sb³⁺. Apart from that, Mn, Pb, and Hg with the spiky signals indicate their existence within stibnite as micro-inclusions. Overall, we found that the trace element substitutions in stibnite from the Zhaxikang Pb–Zn–Ag–Sb deposit are complicated. Incorporations of trace elements such as Pb, Cu, and In into stibnite are largely influenced by a variety of factors. The simple lattice structure and constant trace elements in studied stibnite indicate a low-temperature hydrothermal system and a relatively stable process for stibnite formation. Full article

Super invar, with its near-zero coefficient of thermal expansion (CTE), has a great potential to be used in the design and fabrication of high-precision optical structures, such as optical mirror substrates. In order to reduce the weight and maintain the strength of the mirror substrate, several biomimetic lattice designs were investigated in this paper. The static modeling provides a systematic study on different types of biomimetic mirror substrates. The impact of structure parameters, such as the wall thickness, lattice unit length, height of the lattice structure, and the thickness of the side plate, are also studied. It turns out that the three-layer lattice-structured composite mirror substrate has the best performance. With AM techniques, three-layer gyroid optical structures, which are not possible to fabricate with conventional manufacturing technology, were designed and printed with our in-house-built AM machine. The stiffness test of the gyroid specimens was in good agreement with the modeling results. The gyroid structure shows about a 20% improvement over the honeycomb structure. The gyroid design reduces the equivalent density to 1.8 g/cm³ and has an order-of-magnitude improvement on the thermal deformation, while maintaining a comparable strength with that of beryllium. Full article

Double perovskite materials have emerged as key players in the realm of advanced materials due to their unique structural and functional properties. This research mainly focuses on the synthesis and comprehensive characterization of Ba₂CoWO₆ double perovskite nanopowders utilizing a high-temperature conventional solid-state reaction technique. The successful formation of Ba₂CoWO₆ powders was confirmed through detailed analysis employing advanced characterization techniques. Rietveld refinement of X-ray diffraction (XRD) and Raman data established that Ba₂CoWO₆ crystallizes in a cubic crystal structure with the space group Fm-3m, indicative of a highly ordered perovskite lattice. The typical crystallite size, approximately 65 nm, highlights the nanocrystalline nature of the material. Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) discovered a distinctive morphology characterized by spherical shaped particles, suggesting a complex particle formation process influenced by synthesis conditions. To probe the electronic structure, X-ray Photoelectron Spectroscopy (XPS) identified cobalt and tungsten valence states, critical for understanding dielectric properties associated with localized charge carriers. The semiconducting character of the synthesized Ba₂CoWO₆ nanocrystalline material was confirmed through UV-Visible analysis, which revealed an energy bandgap value of 3.3 eV, which aligns well with the theoretical predictions, indicating the accuracy and reliability of the experimental results. The photoluminescence spectrum exhibited two distinct emissions in the blue-green region. These emissions were attributed to the transitions ³P₀→³H₄, ³P₀→³H₅, and ³P₀→³H₆, primarily resulting from the contributions of Ba²⁺ ions. The dielectric characteristics of the compound were analyzed across a different range of frequencies, spanning from 1 kHz to 1 MHz. Magnetic characterization using Vibrating Sample Magnetometry (VSM) revealed antiferromagnetic behavior of Ba₂CoWO₆ ceramics at room temperature, attributed to super-exchange interactions between Co³⁺ and W⁵⁺ ions mediated by oxygen ions in the perovskite lattice. Additionally, first-principles calculations based on the Generalized Gradient Approximation (GGA+U) with a modified Becke–Johnson (mBJ) potential were employed to gain a deeper understanding of the structural and electronic properties of the materials. This approach involved systematically varying the Hubbard U parameter to optimize the description of electron correlation effects. These results deliver an extensive understanding of the structural, optical, morphological, electronic, and magnetic properties of Ba₂CoWO₆ ceramics, underscoring their potential for electronic and magnetic device applications. Full article

The Plasma Immersion Ion Implantation (PIII) nitriding was used to form a modified layer rich in expanded austenite (γ_N) and expanded ferrite (α_N) phases in super duplex steel. The thermal stability of these phases was investigated through the in situ synchrotron X-ray diffraction. All the surfaces were analyzed by SEM, EDS, and nanoindentation. During the heating stage of the thermal treatments, the crystalline structure of the γ_N phase expanded thermally up to a temperature of 350 °C and, above this temperature, a reduction in the lattice parameter was observed due to the diffusion of nitrogen into the substrate. During the isothermal heating, the gradual diffusion of nitrogen continued and the lattice parameter of the γ_N phase decreased. Increasing the treatment temperature from 450 °C to 550 °C, a greater reduction in the lattice parameter of the γ_N phase occured and the peaks related to the CrN, α, and α_N phases became more evident in the diffractograms. This phenomenon is associated with the decomposition of the γ_N phase into CrN + α + α_N. After the heat treatments, the thickness of the modified layers increased and the hardness values close to the surface decreased, according to the diffusion of the nitrogen to the substrate. Full article

We study the nested topological band-gap structure of one-dimensional (1D) photonic super-lattices. One cell of the super-lattice is composed of two kinds of photonic crystals (PhCs) with different topologies so that there is a domain wall (DW) state at the interface between the two PhCs. We find that the coupling of periodic DWs could form a new band-gap structure inside the original gap. The new band-gap structure could be topologically nontrivial, and a topological phase transition can occur if the structural or material parameters of the PhCs are tuned. Theoretically, we prove that the Hamiltonian of such coupled DWs can be reduced to the simple Su–Schrieffer–Heeger (SSH) model. Then, if two super-lattices carrying different topological phases are attached, a new topological interface state can occur at the interface between the two super-lattices. Finally, we find the nested topological band-gap structure in two-dimensional (2D) photonic super-lattices. Consequently, such nested topological structures can widely exist in complex super-lattices. Our work improves the topological study of photonic super-lattices and provides a new way to realize topological interface states and topological phase transitions in 1D and 2D photonic super-lattices. Topological interface states in super-lattices are sensitive to frequency and have high accuracy, which is desired for high-performance filters and high-finesse cavities. Full article

This research examines the structural and magnetic properties of monodomain cobalt ferrite nanoparticles with the formula (Co_1−xFe_x)^A[Fe_2−xCo_x]^BO₄. The particles were synthesized using various methods, including coprecipitation (with and without ultrasonic assistance), coprecipitation followed by mechanochemical treatment, microemulsion, and microwave-assisted hydrothermal techniques. The resulting materials were extensively analyzed using X-ray diffraction (XRD) and magnetic measurements to investigate how different synthesis methods affect the structure and cation distribution in nanoscale CoFe₂O₄. For particles ranging from 15.8 to 19.0 nm in size, the coercivity showed a near-linear increase from 302 Oe to 1195 Oe as particle size increased. Saturation magnetization values fell between 62.6 emu g⁻¹ and 74.3 emu g⁻¹, primarily influenced by the inversion coefficient x (0.58–0.85). XRD analysis revealed that as the larger Co²⁺ cations migrate from B- to A-sites (decreasing x), the lattice constants and inter-cation hopping distances increase, while the average strength of super-exchange interactions decreases. This study establishes a connection between the magnetic properties of the synthesized samples and their structural features. Importantly, this research demonstrates that careful selection of the synthesis method can be used to control the magnetic properties of these nanoparticles. Full article