Search Results (193)

Variations in the groundwater chemical environment are a critical factor affecting the mechanical property degradation and structural alteration of coal measure strata. Addressing the engineering challenges commonly encountered in coal mining areas of Northwest China, where groundwater with varying pH leads to difficulties in controlling surrounding rock in underground spaces, this study established a comprehensive experimental methodology integrating mechanical loading, nuclear magnetic resonance (NMR) quantitative pore analysis, and scanning electron microscopy (SEM) microstructural characterization. The study revealed the mechanical degradation mechanisms and microstructural evolution characteristics of coal measure coarse sandstone under groundwater environments with different pH values (6–10). With prolonged immersion time, the peak strength and elastic modulus of the coarse sandstone exhibited exponential decay across all pH environments. NMR analysis revealed that the porosity evolved through a path of “increase–decrease–re-increase,” while the macroscopic mechanical failure mode shifted from brittle to brittle-ductile and finally to ductile characteristics. Micropores continuously transformed into medium and large pores, and the macroscopic failure mode exhibited a transition from brittle to brittle-ductile. The findings indicate that groundwater with varying acidity/alkalinity systematically alters the integrity and load-bearing capacity of coal measure coarse sandstone through the complex mechanism of “mineral dissolution (acidic H⁺ corrosion, alkaline OH⁻ hydrolysis)—structural damage—pore/fracture evolution—mechanical degradation.” This mechanism not only reveals the essence of progressive rock damage in weak acid to moderately strong alkaline environments but also provides important insights for the integrity, sealing capacity, and permeability modification of various underground engineering applications, such as CO₂ geological storage, unconventional natural gas development, and underground space utilization. Full article

With the development of unconventional oil and gas resources (such as shale gas and tight oil/gas), the widespread application of multistage fracturing technology has significantly increased the difficulty of wellbore integrity maintaining. The cement sheath serves as the core barrier for preserving wellbore integrity, particularly at the first interface (cement–casing) and the second interface (cement–formation). The high temperature, high pressure, and cyclic dynamic loading imposed by multistage fracturing represent severe challenges to the integrity of cement sheath. To simulate underground conditions realistically, a high-temperature, complex stress path loading system coupled with real-time gas flow monitoring was developed. Using this system, gas leakage monitoring and displacement-controlled cyclic loading tests were conducted on cement–steel (simulating the first interface) and cement–shale (simulating the second interface) composite specimens. It focused on investigating the effects of different temperatures, cyclic stress levels, and cycle counts on the sealing performance of the cement–steel and cement–shale composites. The findings reveal that elevated temperatures significantly degrade cement properties and accelerate damage accumulation. Cyclic stress levels and cycle counts are core drivers of interface fatigue failure, exhibiting synergistic destructive effects with temperature. The first interface is more prone to seal failure due to material property differences and a relatively high stress level. This research elucidates the cumulative damage mechanism underlying interfacial seal failure. It is of significant engineering implications for enhancing well safety and development efficiency. Full article

A productivity simulation for hydraulically fractured wells with complex fracture geometry involves a heavy computational burden and is therefore not suitable for engineering-scale fracture-optimization designs and production-analysis applications. This paper develops a productivity-prediction surrogate model based on a deep convolution–bidirectional gated recurrent unit temporal network (DC-BiGRU) framework where a deep convolutional neural network is used to extract features from fracture images, while a BiGRU model was designed to fully capture valuable information from the production sequence. Some additional inputs, e.g., cluster spacing and stage spacing, that account for different fracture-placement designs in horizontal wells were also considered. A large number of shale-gas production data samples at different times were generated using a fractured-horizontal-well productivity simulator under diverse hydraulic-fracture geometries and bottom-hole flowing pressures. The surrogate model had relative errors below 10% with an average error of about 6%. Compared to high-fidelity capacity prediction simulators, the computational efficiency of the deep learning surrogate models was improved by two to three orders of magnitude. The runtime of the high-fidelity numerical simulator was about 20 min, while the surrogate model, which was run on an NVIDIA Tesla P100 GPU (NVIDIA, Santa Clara, CA, USA), took less than 1 s, which is almost negligible. The proposed surrogate model resolved the low efficiency of the productivity simulation for complex-fracture hydraulic fracturing wells in unconventional reservoirs, enabling rapid dynamic forecasting of fractured-well productivity. Full article

Carbon dioxide injection is one of the most advanced and commercially proven methods of enhanced hydrocarbon recovery, and CO₂ injection has been shown to be very effective in conventional oil reservoirs and is gaining attention in gas, unconventional, and coalbed methane reservoirs. The advantages of CO₂ injection lie in the favorable phase properties and interactions with reservoir fluids, such as swelling, reduction in oil viscosity, reduction in interfacial tension, and miscible displacement in favorable cases. But the low viscosity and density of CO₂ compared to the reservoir fluids result in unfavorable mobility ratios and gravity override, resulting in sweep efficiency limitations. This review offers a broad and EOR-centric evaluation of the various CO₂ injection methods for a broad array of reservoir types, such as depleted oil reservoirs, gas reservoirs for the purpose of gas recovery, tight gas/sands, as well as coalbed methane reservoirs. Particular attention will be given to the use of mobility control/sweep enhancement techniques such as water alternating gas (CO₂-WAG), foam-assisted CO₂ injection, polymer-assisted WAG processes, as well as hybrid processes that combine the use of CO₂ injection with low salinity or engineered waterflood. Further, recent developments in compositional simulation, fracture-resolving simulation, hysteresis modeling, and data-driven optimization techniques have been highlighted. Operational challenges such as injectivity reduction, asphaltene precipitation, corrosion, and conformance problems have been reviewed, along with the existing methods to mitigate such issues. Finally, key gaps in the current studies have been identified, with an emphasis on the development of EHR processes using CO₂ in complex and low-permeability reservoirs, enhancing the resistance of chemical and foam methods in realistic conditions, and the development of reliable methods for optimizing the process on the field scale. This review article will act as an aid in the technical development process for the implementation of CO₂ injection projects for the recovery of hydrocarbons. Full article

Background/Objectives: Antisense oligonucleotides (ASOs) hold great therapeutic potential due to their precise ability to modulate gene expression, particularly for treating genetic and neurological disorders. However, effective delivery of ASOs remains a major challenge. While most recent research focused on lipid nanoparticles (LNPs) as ASO carriers, alternative formulations, preparation methods and lipid compositions on delivery optimization are not fully explored. In this study, we investigated two types of formulations, lipoplexes (LPXes) and LNPs, prepared using lysine-type cationic lipids, K3C14 or K3C16, selected from a lysine-type lipid mini-library for their superior formulation stability and distinct cellular entry mechanisms. Methods: The physicochemical properties of the formulations were characterized using dynamic light scattering. Cytotoxicity was evaluated in spleen and liver cell lines. LPXes and LNPs were assessed for ASO delivery efficiency using an engineered HEK293 split-luciferase cell line, while immune response was evaluated in human peripheral blood mononuclear cells. Cryogenic electron microscopy (Cryo-EM) images were captured for structural analysis. Results: Lysine-type lipid mini-library screening identified lipids with either a hydrocarbon spacer K3 or C14 fatty acid tail exhibiting great stability and safety. Among the tested LPX and LNP formulations, the K3C16 lipoplex demonstrated ASO delivery efficiency and immune responses comparable to the benchmark SpikeVax LNP formulation. Notably, Cryo-EM imaging revealed novel structures that have not been reported previously; the K3C14 lipoplex formed a rouleaux-like structure, whereas the K3C16 lipoplex exhibited a lipid nanosheet-like structure, distinct from the conventional LNP structure. Conclusions: These results highlight the potential of an unconventional type of lipid assembly for efficient ASO delivery. Full article

Time-varying or temporal metamaterials and metasurfaces, in which electromagnetic parameters are deliberately modulated in time, have emerged as a powerful route to engineer wave–matter interaction beyond what is possible in static media. By enabling the controlled exchange of energy and momentum with the fields, they underpin magnet-free nonreciprocity, low-loss frequency conversion, temporal impedance matching beyond Bode-Fano limit, and unconventional parametric gain and noise control. This survey provides a coherent framework that unifies the main theoretical and experimental developments in the area, from early analyses of velocity-modulated dielectrics to recent demonstrations of temporal photonic crystals, non-Foster temporal boundaries, and spatiotemporally driven metasurfaces relevant to nanophotonic platforms. We systematically compare time-varying permittivity, joint $\epsilon$-$\mu$ modulation, time-varying conductivity, plasmas, and circuit-equivalent implementations, including stochastic and rapidly sign-switching regimes, and relate them to acoustic and quantum analogs using common figures of merit, such as conversion efficiency, isolation versus insertion loss, modulation depth and speed, dynamic range, and stability. Our work concludes by outlining key challenges, loss and pump efficiency, high-speed modulation at the nanoscale, dispersion engineering for broadband operation, and fair benchmarking, which must be addressed for robust, integrable temporal metasurfaces. Full article

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

Acoustic metamaterials are artificially engineered materials composed of subwavelength structural units, whose effective acoustic properties are primarily determined by structural design rather than intrinsic material composition. By introducing local resonances, these materials can exhibit unconventional acoustic behavior, enabling enhanced sound insulation beyond the limitations of conventional structures. In this study, a thin plate (thin sheet) refers to a structural element whose thickness is much smaller than its in-plane dimensions and can be accurately described using classical thin-plate vibration theory. When resonant mass blocks are attached to a thin plate, a thin-plate acoustic metamaterial is formed through the coupling between plate bending vibrations and local resonances. Thin-plate acoustic metamaterials exhibit excellent sound insulation performance in the low- and mid-frequency ranges. Multilayer configurations and the combination with porous materials can effectively broaden the insulation bandwidth and improve overall performance. However, the large number of structural parameters in multilayer composite thin-plate acoustic metamaterials significantly increases design complexity, making conventional trial-and-error approaches inefficient. To address this challenge, a neural-network-based inverse design framework is proposed for multilayer composite thin-plate acoustic metamaterials. An analytical model of thin-plate metamaterials with multiple attached cylindrical masses is established using the point matching and modal superposition methods and validated by finite element simulations. A multilayer composite unit cell is then constructed, and a dataset of 30,000 samples is generated through numerical simulations. Based on this dataset, a forward prediction network achieves a test error of 1.06%, while the inverse design network converges to an error of 2.27%. The inverse-designed structure is finally validated through impedance tube experiments. The objective of this study is to establish a systematic theoretical and neural-network-assisted inverse design framework for multilayer thin-plate acoustic metamaterials. The main novelties include the development of an accurate analytical model for thin-plate metamaterials with multiple attached masses, the construction of a large-scale simulation dataset, and the proposal of a neural-network-assisted inverse design strategy to address non-uniqueness in inverse design. The proposed approach provides an efficient and practical solution for low-frequency sound insulation design. Full article

Electrokinetics has established itself as a central pillar in microfluidic research, offering a powerful, non-mechanical means to manipulate fluids and analytes. Mechanisms such as electroosmotic flow (EOF), electrophoresis (EP), and dielectrophoresis (DEP) re-main central to the field, once more layers of complexity emerge heterogeneous interfaces, viscoelastic liquids, or anisotropic droplets are introduced. Five research directions have become prominent. Field-driven manipulation of droplets and emulsions—most strikingly Janus droplets—demonstrates how asymmetric interfacial structures generate unconventional transport modes. Electrokinetic injection techniques follow as a second focus, because sharply defined sample plugs are essential for high-resolution separations and for maintaining analytical accuracy. Control of EOF is then framed as an integrated design challenge that involves tuning surface chemistry, engineering zeta potential, implementing nanoscale patterning, and navigating non-Newtonian flow behavior. Next, electrokinetic instabilities and electrically driven micromixing are examined through the lens of vortex-mediated perturbations that break diffusion limits in low-Reynolds-number flows. Finally, electrokinetic enrichment strategies—ranging from ion concentration polarization focusing to stacking-based preconcentration—demonstrate how trace analytes can be selectively accumulated to achieve detection sensitivity. Ultimately, electrokinetics is converging towards sophisticated integrated platforms and hybrid powering schemes, promising to expand microfluidic capabilities into previously inaccessible domains for analytical chemistry and diagnostics. Full article

Additive manufacturing (AM) enables the consolidation of components and the integration of new functionalities in metallic parts, but layered fabrication often results in poor surface quality and geometric deviations. Among various surface treatment techniques, machining is often favoured for its capability to enhance not only surface finish but also critical geometric tolerances such as flatness and circularity, in addition to dimensional accuracy. However, machining AM components, particularly thin-walled structures, poses challenges related to unconventional material properties, complex fixturing, and heightened susceptibility to chatter. This study investigates the vibrational behaviour of thin-walled Ti6Al4V components produced via laser powder bed fusion, using a jet-engine compressor blade demonstrator. Four stock envelope designs were evaluated: constant, tapered, and two topologically optimised variants. After fabrication by Laser Powder Bed Fusion, the blades underwent tap testing and subsequent machining to assess changes in modal characteristics. The results show that optimised geometries can enhance modal performance without increasing the volume of the stock material. However, these designs exhibit more pronounced in situ modal changes during machining, due to greater variability in material removal and chip load, which amplifies vibration sensitivity compared to constant or tapered stock designs. Full article

In practical engineering, large-scale wind power integration typically requires long-distance transmission lines to deliver power to load centers. The resulting weak sending-end systems lack support from synchronous power sources. Under extreme weather conditions, the rapid increase in active power output caused by high wind power generation may lead to voltage instability. In existing projects, a phenomenon of repeated voltage fluctuations has been observed under fault-free system conditions. This phenomenon is induced by the coupling of the characteristics of weak sending-end systems and low-voltage ride-through (LVRT) discrimination mechanisms, posing a serious threat to the safe and stable operation of power grids. However, most existing studies focus on the analysis of voltage instability mechanisms and the optimization of control strategies for single devices, with insufficient consideration given to voltage fluctuation suppression methods under the coordinated operation of wind power and energy storage systems. Based on the actual scenario of energy storage configuration in wind farms, this paper improves the traditional LVRT discrimination mechanism and develops a coordinated voltage ride-through control strategy for permanent magnet synchronous generator (PMSG) wind turbines and energy storage batteries. It can effectively cope with unconventional operating conditions, such as repeated voltage ride-through and deep voltage ride-through that may occur under extreme meteorological conditions, and improve the safe and stable operation capability of wind farms. Using a hardware-in-the-loop (HIL) test platform, the coordinated voltage ride-through control strategy is verified. The test results indicate that it effectively enhances the wind–storage system’s voltage ride-through reliability and suppresses repeated voltage fluctuations. Full article

With the continuous advancement of shale gas field development, well productivity following initial hydraulic fracturing often declines due to mechanisms such as proppant embedment and fracture conductivity degradation. However, such wells may still retain significant development potential, making re-fracturing crucial for restoring production and highlighting the critical importance of accurate candidate selection for re-fracturing. To improve the precision of candidate well selection for re-fracturing in shale gas wells, this study focuses on a shale gas block in the Southern Chuan Basin. Through comparative analysis of existing selection methods, 14 key parameters were finalized. The threshold values for some of these key parameters were recalibrated based on the specific geological, engineering, and production characteristics of the target block in the Southern Chuan Basin. Furthermore, the AHP-GRA (Analytic Hierarchy Process-Gray Relational Analysis) weighting method was integrated to achieve a balance between empirical knowledge and quantitative objectivity. Ultimately, a more targeted, comprehensive, and combined subjective–objective methodology for selecting re-fracturing candidate wells was developed. A computational tool developed in Python 3.9 was utilized to evaluate 13 candidate wells in the block, successfully identifying three high-priority wells for re-fracturing implementation. The reliability of this selection result was validated by analyzing production data before and after re-fracturing, confirming that the production performance of the selected wells showed relatively significant improvement post re-fracturing, with a notable increase in recovery factor. This model provides critical decision-making support for the low-cost and large-scale development of shale gas. It holds significant theoretical and practical value for promoting the secondary development of mature shale gas wells and contributes positively to the efficient utilization of unconventional natural gas resources and energy security. Full article

Cell therapies hold great promise for advancing wound healing; however, translating this promise into consistent clinical benefit has proven elusive. Numerous trials have failed to reproduce the robust outcomes suggested by preclinical studies, reflecting a landscape marked by hidden traps. These include the hostile wound microenvironment, the cytotoxicity of antimicrobial dressings, poor retention and engraftment, immune clearance, and the paradoxical risk of fibrosis and scarring. Across these challenges emerge paradoxes that redefine how traps are understood. The Scarring Paradox reveals that MSCs and EVs may either suppress or reinforce fibrosis, depending on the niche context. The Immune Double-Edged Sword captures the duality of clearance and regenerative modulation. These paradoxes illustrate that traps are not static obstacles but dynamic inflection points. Recognition of these paradoxes has inspired tricks: protective biomaterial carriers, preconditioning strategies, engineered exosomes, and combinatorial therapies with anti-fibrotic, neuromodulatory, or microbiome-targeted adjuncts. Case studies illustrate how classical traps manifest in clinical practice and how paradoxes guide innovation. Emerging adjuncts, ranging from herbal bioactives and bioelectric modulation to circadian synchronization and digital twins, point toward more unconventional but increasingly plausible frameworks for niche control. This perspective review demonstrates that the future of regenerative wound therapy depends not on avoiding traps but on reframing them through paradoxes and converting them into tricks. Stem cell and exosome therapy is thus moving beyond a linear “promise versus failure” narrative toward a systemic, context-aware, programmable approach in which paradoxes drive conceptual renewal and transformative paradigms in wound care. Full article

Traditional route optimization frameworks often suffer from “spatial blindness,” addressing the problem through abstract matrices devoid of geographical context. To address this fundamental methodological gap, this study proposes the Iterative Score Propagation Algorithm (ISPA), a transparent, GNN-inspired framework that reframes optimization as a holistic corridor problem. ISPA’s robustness and superiority were tested against established Multi-Criteria Decision-Making (MCDM) methods (WLC, TOPSIS, VIKOR) across three diverse engineering scenarios (Rural Highway, Pipeline, Trekking Trail) and two distinct weighting philosophies (Entropy and AHP). The holistic analysis reveals that ISPA achieves the highest final score (0.815) across all six test conditions, demonstrating both the highest overall mean performance (0.629) and the greatest stability (1.000). Furthermore, its flexible cost function successfully modeled unconventional objectives, such as a “climbing reward,” enabling a paradigm shift from cost minimization to experience maximization. ISPA’s superior performance stems from its structural advantage in contextualizing spatial data. This work introduces a new, spatially-aware approach that transforms route planning from a static calculation into a dynamic design and scenario analysis tool for planners and engineers. Full article

Flat electronic bands, characterized by a nearly dispersionless energy spectrum, have emerged as fertile ground for exploring strong correlation effects, unconventional magnetism, and topological phases. This review paper provides an overview of the theoretical basis, material realization, and emergent phenomena associated with flat bands. We begin by discussing the geometric and topological origins of flat bands in lattice systems, emphasizing mechanisms such as destructive interference and compact localized states. We will also explain the relationship between quantum metrics and flat bands, which are recent theoretical findings. We then survey various classes of materials—ranging from engineered lattices and Moiré structures to transition metal compounds—where flat bands have been theoretically predicted or experimentally observed. The interplay between flat-band physics and strong correlations is explored through recent developments in ferromagnetism, superconductivity, and various Hall effects. Finally, we outline open questions and potential directions for future research, including the quest for ideal flat-band systems, the role of spin–orbit coupling, and the impact of disorder. This review aims to bridge fundamental concepts with cutting-edge advances, highlighting the rich physics and material prospects of flat bands. Full article