Search Results (5,078)

Sodium-ion batteries (SIBs) represent a compelling alternative to lithium-ion batteries for grid-scale energy storage, owing to the high natural abundance and low cost of sodium resources, as well as their strategic alignment with national energy security priorities. Nevertheless, the sluggish Na⁺ diffusion kinetics and limited specific capacity of anode materials continue to impede practical deployment. Herein, nitrogen-doped carbon-coated TiO₂ nanofibers (TiO₂/C-N) were rationally engineered through a facile electrospinning route integrated with synergistic defect and coating engineering. The in situ-formed N-doped carbon shell establishes a continuous, high-conductivity electron-transport network while simultaneously buffering volumetric strain during repeated (de)sodiation, thereby preserving long-term structural integrity. Electrochemical assessments demonstrate that the TiO₂/C-N electrode delivers a reversible specific capacity of 233.64 mAh g⁻¹ at 0.1 A g⁻¹ (initial Coulombic efficiency 54.13%). Quantitative kinetic analysis reveals a pronounced pseudocapacitive contribution of 41.4% at 1.2 mV s⁻¹, confirming a surface-controlled Na⁺ storage pathway that markedly enhances rate capability. Moreover, the electrode retains 245.5 mAh g⁻¹ after 150 cycles at 1 A g⁻¹, underscoring exceptional cycling stability. This work elucidates the synergistic regulation of N-doped carbon coating and pseudocapacitive kinetics in TiO₂-based anodes, offering a robust design strategy for high-rate, long-cycle-life SIB anodes. Full article

With the requirement for reducing carbon footprint in engineering construction, porous vegetation concrete is increasingly receiving attention for use in completed slope restoration. Cemented soil is introduced after the completion of porous vegetation concrete stabilization and functions mainly as a revegetation substrate. An important consideration for cemented soil in this application is its ability to maintain strength and water stability and possess moisture retention capacity, without causing much increase in alkali release or diffusion. This present study investigated a newly developed twofold stabilization system involving both cement binders and organic waterborne epoxy resin to meet the requirements of synthetically enhancing slope stabilization and restoration. Changes in the unconfined compressive strength and water stability were analyzed, whilst mineralogical composition and microstructure characteristics were investigated. The results indicated that moderate incorporation of triethylenediamine (TETA)-cured epoxy resin (1–2% by soil mass) moderately reduced strength and increased water stability with controlled alkali release in cemented soil. Mineralogical and microstructural analysis revealed that TETA-cured epoxy resin retarded cement hydration and refined particle bonding, exhibiting less consolidated pore structure characteristics. The twofold stabilization was exceptional in enhancing structural stability exposed to repeated humidity variation, albeit it yielded increased strength reduction rate from <7% to 9–16% under UV irradiation. Potentials of calcium sulfoaluminate cement and Portland slag cement were also investigated. A pilot-scale vegetation trial with representative plant species gave general agreement with effects observed in the laboratory in alkali reduction and moisture retention. The results provided an ecological approach for better restoring completed slopes that were stabilized using porous vegetation concrete. Full article

The development of high-performance electrode materials with controlled morphology remains a key challenge for advancing supercapacitor technologies. In this study, polyethylene glycol (PEG)-assisted hydrothermal synthesis was employed to engineer NiCo₂S₄ nanostructures with controlled morphology for enhanced supercapacitor performance. The influence of PEG concentration on nucleation behavior, structural evolution, and electrochemical characteristics was systematically investigated. The optimized NiCo₂S₄ electrode synthesized with 0.2% PEG (NiCoS-P2) exhibited a hierarchical flower-like nanosheet architecture with reduced agglomeration and improved electrochemically accessible surface area. As a result, the electrode delivered a high areal capacitance of 13.689 F/cm² (specific capacitance of 6845 F/g) at 5 mA/cm², along with excellent rate capability and superior cycling stability, retaining 84.16% capacitance after 12,000 cycles. Electrochemical analysis revealed that the charge storage process is predominantly diffusion-controlled with enhanced ion transport kinetics. Furthermore, an asymmetric supercapacitor device assembled using NiCoS-P2 as the positive electrode and activated carbon as the negative electrode demonstrated a wide operating voltage of 1.5 V, delivering an areal capacitance of 0.409 F/cm² (specific capacitance of 204.5 F/g), an energy density of 0.128 mWh/cm², and a power density of 2.99 mW/cm². The device also exhibited excellent long-term stability with 85.3% capacitance retention after 7000 cycles. This work highlights the effectiveness of polymer-assisted structural engineering in optimizing transition metal sulfide electrodes for advanced energy storage applications.: Full article

Aiming at the difficult problem of floor heave control in deep coal mine roadways, this paper took the 1224 transportation roadway of Shuguang Coal Mine in Shanxi as the engineering background and carried out the first underground industrial test of floor-slotting pressure relief technology by using special slotting equipment. The aim is to reveal the energy transfer law of the floor rock mass during slotting pressure relief and clarify its inherent connection with stress redistribution and floor heave deformation control. The research adopts a combination of theoretical analysis, numerical simulation, and field tests to systematically explore the energy accumulation characteristics of the floor and the induced mechanism of floor heave. Results show that the maximum energy accumulated in the floor after roadway excavation reaches 6.0 × 10⁵ J, which is the fundamental cause of floor heave. After optimizing the slotting parameters (depth 2.5 m, width 0.2 m), numerical simulation indicates that the surrounding rock stress concentration zone migrates to the deep part, the energy peak shifts down by 2.5 m, the floor plastic zone expands, and the range of the high-energy zone shrinks. Field test results show that the floor heave amount decreases from 30 cm to 20 cm, with a reduction rate of 33%. This study reveals the synergistic mechanism of “energy transfer–stress regulation–deformation control”, verifies the effectiveness and feasibility of the slotting pressure relief technology in the floor heave control of deep, high-stress roadways, and provides a guarantee for the safe and efficient advancement of the working face. Full article

Dye-sensitized solar cells (DSSCs) remain attractive as low-cost photovoltaic devices; however, their practical efficiency is still constrained by electron-transport losses, interfacial recombination, and incomplete light harvesting in conventional titanium dioxide (TiO₂) photoanodes. The effects of TiO₂ film thickness, multi-walled carbon nanotube (MWCNT) incorporation, and multilayer oxide interface engineering on DSSC performance were examined. Degussa P25-TiO₂ photoanodes were first optimized with respect to thickness, after which controlled MWCNT loadings and sequential compact sol–gel TiO₂ and tin dioxide (SnO₂) sublayers were introduced. The optimum pristine P25-TiO₂ photoanode thickness was 9.11 μm, yielding an open-circuit voltage of 0.74 ± 0.01 V, a short-circuit current density of 14.10 ± 0.40 mA/cm², a fill factor of 56.24 ± 1.00%, and a power-conversion efficiency of 5.93 ± 0.20%. The incorporation of 0.025 wt% MWCNTs increased the efficiency to 6.04 ± 0.20%, corresponding to an absolute gain of 0.11 percentage points. The best performance was obtained with the sol–gel SnO₂/sol–gel TiO₂/P25-CNT multilayer photoanode, which delivered 0.74 ± 0.02 V, 16.22 ± 0.40 mA/cm², 57.59 ± 1.00%, and 6.89 ± 0.30%, respectively. FE-SEM, EIS, XRD, Heated Ultrasonic Cleaner and UV–visible analyses indicate that the multilayer architecture preserves porosity, enhances light harvesting, and suppresses interfacial recombination, while the CNT network facilitates charge transport. Full article

Prefabricated steel structures have been increasingly adopted in modern construction due to their high efficiency, sustainability, and industrialized production. However, their construction quality and efficiency are often compromised by accumulated geometric deviations during fabrication, transportation, assembly, and welding, while traditional construction control and welding processes remain highly dependent on manual measurements and empirical operations. To address these challenges, this study proposes a digital construction framework for prefabricated steel structures, integrating high-precision three-dimensional (3D) laser scanning, Building Information Modeling (BIM), and intelligent welding technologies. First, high-precision 3D laser scanning is employed to capture the as-built geometric information of prefabricated steel components, generating dense point cloud data for construction-stage deviation detection and quantitative comparison with BIM-based design models. Based on deviation analysis, a digital construction control strategy is established to support real-time feedback, error compensation, and assembly adjustment. An engineering case study involving a complex prefabricated steel structure is conducted to validate the proposed framework. The results demonstrate that the integrated digital construction and intelligent welding approach significantly improves assembly accuracy, weld positioning precision, and construction efficiency, while reducing manual intervention and error accumulation. Overall, this study contributes to the body of knowledge by proposing a unified closed-loop digital construction paradigm that integrates geometric perception, deviation-driven decision-making, and intelligent welding execution, thereby bridging the gap between construction control and robotic fabrication in prefabricated steel structures. Full article

In the process of extracting and transporting waxy crude oil, pipeline blockages resulting from wax deposition significantly impede production efficiency and lead to substantial economic losses. Ethylene vinyl acetate copolymer (EVA) is a widely used chemical wax inhibitor; however, its performance is influenced by multiple factors, including its molecular structure, concentration, and the carbon number distribution of the wax system. A systematic elucidation of its mechanism of action and associated phase changes is therefore necessary. In this study, differential scanning calorimetry (DSC) was employed to systematically investigate the thermal behavior of a wax system with a broad carbon number distribution (C5–C50). The objectives were to analyze the influence of EVA concentration, vinyl acetate (VA) content, and molecular weight on the phase transition parameters, to elucidate the wax inhibition mechanism, and to construct a phase prediction model based on the Flory–Huggins theory. The results demonstrate that the wax appearance temperature (WAT), phase transition temperature, and phase transition enthalpy of the wax systems increase monotonically with carbon number. Furthermore, the addition of EVA was found to significantly reduce both the WAT and the amount of wax precipitated. The optimal structural parameters were identified as a VA content of 10%, a number average molecular weight of 20,000, and an optimal concentration of 800 ppm. The medium-carbon wax system (C16–C30) was found to be the most sensitive to the EVA response. The established phase model exhibited high predictive accuracy, with a mean relative error of less than 4%, a root mean square error (RMSE) of 0.32%, and a coefficient of determination (R²) of 0.987, thereby providing preliminary insights and a practical tool for optimizing EVA wax inhibitor formulations under simplified conditions and guiding their potential engineering applications. Full article

Translating preclinical findings into effective clinical cancer immunotherapies remains a major challenge, mainly because conventional in vitro and animal models often fail to capture the complexity, dynamics, and species-specific features of human immune responses. Organ-on-a-chip (OoC) technologies that combine engineered tissue architectures with precisely controlled microfluidic transport provide human-relevant microphysiological platforms for mechanistic studies of immune–tumor interactions and evaluation of therapeutic efficacy and immunotoxicity under defined microenvironmental conditions. However, immune responses involve time-dependent and interconnected processes, including immune cell trafficking, cytokine programs, metabolic shifts, and cytolysis, that are not adequately resolved by static or endpoint assays. Engineering immune-competent OoC systems therefore requires coordinated design of platform architectures, immune cell incorporation strategies, and integrated measurement workflows capable of capturing dynamic and state-dependent responses. In this review, we summarize engineering strategies for building immune-competent OoC platforms for cancer immunotherapy, focusing on platform architectures, immune cell incorporation methods, and fit-for-purpose assay workflows. Emphasis is placed on embedded sensing modalities (e.g., cytokine, oxygen, and impedance readouts) that provide valuable kinetic and state-variable data. Finally, we discuss key translational challenges, including reproducibility, standardization, and benchmarking, and outline near-term priorities to accelerate the adoption of immune-competent OoC systems in immunotherapy research and development. Full article

Existing studies conduct general cost analyses for prefabricated components, yet structural heterogeneity results in distinct cost drivers. Most studies concentrate on the technical performance of prefabricated staircases, with insufficient investigation into dedicated cost-estimation methods. This study establishes a hybrid prediction framework integrating GAN-based data augmentation and SHAP-empowered Multilayer Perceptron (SHAP-MLP) modeling, using prefabricated straight staircases as empirical objects for multidimensional analysis. Total cost is classified into production, transportation, and on-site installation phases, followed by systematic screening of 33 influencing factors for predictive modeling. The Analytic Hierarchy Process (AHP), with a 1–9 scale, is adopted to quantify indicator weights and prioritize features. Triple verification (multi-expert consistency test, group opinion coordination test, and sensitivity analysis) removes five weakly correlated parameters to form a preliminary indicator system. Based on 240 original engineering data samples, the GAN generates 60 high-fidelity synthetic samples. Distribution consistency between synthetic and original data is validated via the Kolmogorov–Smirnov (KS) test, p-value verification, and kernel density estimation (KDE). SHAP interpretability analysis identifies four core determinants: prefabrication rate, total staircase area, standardization level, and number of floors. Eight low-impact parameters are excluded to optimize model input, leaving 20 validated indicators. The GAN-SHAP-MLP model maintains superior performance in testing, with a test-set RMSE of 49.538, representing improvements of 41.3%, 22.5%, and 25.7% over LSTM (89.33), CNN (67.59), and standard MLP (70.56), respectively. The difference between its test-set and overall R² is only 0.69%, significantly lower than 2.06% for LSTM and 5.47% for MLP. Empirical validation with real engineering cases from four different regions further confirms the model’s high prediction accuracy, with a minimum error of only 1.49%. The integration of data augmentation and interpretable deep learning provides a high-precision, interpretable cost prediction tool for prefabricated straight staircases, promoting methodological progress in construction economics. Full article

The oilfield gathering and transportation system (OGTS) accounts for a substantial share of total energy consumption, demonstrating considerable potential for energy-saving optimization. Previous research primarily focused on the independent optimization of gathering pipeline networks or processing stations, with little attention given to their integrated optimization. This study investigates the OGTS of a domestic oilfield block. It develops artificial neural network (ANN) surrogate models for both pipelines and processing station equipment to accurately capture system operating states. An optimization model linking the pipeline network and processing station is established, and a differential evolution algorithm is applied to enhance computational efficiency. The results indicate that optimal predictive performance was achieved when the first hidden layer of the backpropagation neural network (BPNN) contained 50 neurons with a rectified linear unit (ReLU) activation function, with the surrogate models achieving coefficient of determination (R²) values exceeding 0.85. Under a 30 min optimization cycle, total system energy consumption decreased by 2.28%, with computation completed in under 3 min, while daily average optimization led to a 1.55% reduction. These findings demonstrate that the proposed integrated optimization framework offers both a robust methodological foundation and practical engineering guidance for coordinated, low-carbon, and energy-efficient operation of oilfields. Full article

This study presents an analytical framework for evaluating the admissibility of biaxial inclination of rectangular sinking wells. The inclination of the well is interpreted as an eccentric transfer of the vertical load to the concrete plug, which produces a two-dimensional linear stress field beneath the base. Closed-form expressions are derived for the stresses at the four corners of the rectangular base as functions of the eccentricity components associated with the two orthogonal tilt directions. Based on these expressions, the admissibility of inclination is represented by a tilt envelope in the space of the two tilt angles, defining the combinations of tilt components that satisfy the adopted serviceability criterion. The analytical formulation also allows for comparison between the stress-based admissibility limit, the geometric condition corresponding to loss of compressive contact beneath the base, and a simplified indicator of lateral wall-pressure asymmetry acting on the shaft. Parametric analyses show that biaxial inclination leads to stress concentration at the corners of the base and that even relatively small tilt components may combine to produce significant stress amplification. The geometry of the well strongly influences the shape of the admissible tilt envelope, with elongated rectangular wells exhibiting directional anisotropy of the allowable inclination. The proposed analytical approach provides a transparent tool for evaluating inclined wells using basic geometric parameters in engineering practice. Full article

Reliable high-precision positioning in railway tunnels is essential for intelligent train operation and safety monitoring, yet GNSS signals are severely degraded by blockage and multipath. This paper proposes a deployment-oriented numerical framework to optimize pseudolite layouts in tunnels by explicitly modeling visibility obstruction and controlling worst-case geometry along the train trajectory. A high-fidelity 3D tunnel–train model is established, in which line-of-sight (LoS) availability is screened under vehicle occlusion and trajectory-level geometric quality is evaluated accordingly. Instead of optimizing only the average PDOP, the proposed framework minimizes the trajectory 90th-percentile PDOP (qPDOP) to suppress tail-risk geometric degradation, while interpreting PDOP as an error amplification factor that directly affects positioning reliability under measurement noise and local multipath. The core contribution is a Voronoi-partition-constrained improved genetic algorithm (IGA) for tunnel pseudolite deployment. Voronoi partitioning enforces segment-wise coverage by requiring at least one pseudolite in each partition cell and avoids clustering-induced blind zones. Meanwhile, the IGA incorporates improved search and constraint-handling mechanisms to satisfy practical engineering requirements, including feasible installation regions, minimum spacing, mounting-face balance (ceiling/side walls), communication range, and continuous satellite visibility. Comparative simulations and ablation studies demonstrate that the proposed method achieves more uniform coverage and significantly improves full-trajectory geometric stability, reducing high-quantile PDOP and mitigating local spikes in occlusion-sensitive sections under cost-constrained sparse deployments. The proposed framework provides a practical and flexible toolchain for designing positioning-oriented pseudolite infrastructures in underground transportation environments. Full article

The growing interconnection of critical engineering infrastructure through IoT introduces unprecedented exposure to cyber threats. Emerging quantum computing capabilities pose a transformative risk to classical cryptographic primitives such as Rivest–Shamir–Adleman and Elliptic-Curve Cryptography, which underpin secure communication and device authentication in industrial control systems, power grids, transportation networks, and healthcare infrastructure. This paper investigates quantum-resistant encryption, often termed post-quantum cryptography (PQC), as a sustainable security paradigm for IoT communication within critical systems. By analyzing lattice-based, code-based, multivariate, and hash-based schemes, the study evaluates trade-offs between computational cost, memory footprint, and latency constraints intrinsic to resource-limited IoT nodes. A hybrid architectural framework integrating the National Institute of Standards and Technology-standardized algorithms (e.g., Cryptographic Suite for Algebraic Lattices—Kyber, Dilithium) with lightweight symmetric primitives (e.g., Ascon, GIFT block cipher in Combined Feedback mode) is proposed for secure data transmission across heterogeneous IoT layers. Experimental simulations benchmark key-exchange throughput, ciphertext expansion, and resilience against quantum-adversarial models, demonstrating up to 65% reduction in handshake latency compared to baseline lattice implementations under constrained conditions. The paper concludes with policy and engineering recommendations for the adoption of quantum-resistant IoT protocols in energy, transportation, and industrial automation sectors, highlighting alignment with global PQC migration roadmaps and IEC 62443 cybersecurity standards. Full article

The Variable Cycle Engine (VCE) is a key enabling technology for addressing the economic and environmental challenges of next-generation supersonic civil aircraft. This paper presents a multidisciplinary design analysis and optimization (MDAO) approach to quantitatively assess the potential benefits of Variable Cycle Engines (VCE) in the conceptual design of supersonic civil aircraft. In this approach, component-level models of a conventional Mixed-Flow Turbofan (MFTF) and a double-bypass VCE with a Core Driven Fan Stage (CDFS) are integrated into the MDAO process. Employing a multi-point optimization strategy, the engine design parameters and off-design control schedules are first determined. Subsequently, for each given engine design (MFTF and CDFS VCE), the airframe geometry parameters are optimized to minimize the aircraft Maximum Take-off Weight (MTOW). The application of this approach is illustrated through a case study of a medium-sized supersonic civil transport. The results indicate that, under the assumption of identical weights for the VCE and the MFTF, the design with the VCE reduces the MTOW by 2.8%, block fuel consumption by 5.7%, and total mission Nitrogen Oxides (NOx) emissions by 24.2% compared to the design with the MFTF. Additionally, lateral noise and flyover noise during the take-off phase are decreased by 2.2 EPNdB and 1.9 EPNdB, respectively. To account for the potential weight increase caused by the structural complexity of the VCE, a parametric weight sensitivity analysis is conducted. Results show that the VCE retains its advantages in MTOW, fuel efficiency, noise, and emissions for weight penalty factors up to 1.15. Full article

The hydrogen fuel cell electric vehicles (FCEVs) are becoming a worldwide recognized eco-friendly choice which produces no tailpipe emissions while providing better energy efficiency than traditional internal combustion engine vehicles. The review delivers an in-depth evaluation of FCEVs through their assessment which focuses on their transportation and power generation functions. The research investigates hydrogen production methods together with storage and distribution systems and vehicle integration practices and performance enhancement techniques. The paper highlights major technical challenges such as high production costs, limited refueling infrastructure, storage inefficiencies, and fuel cell durability. The research uses battery electric and hybrid vehicle comparisons to assess FCEV market competitiveness. The life-cycle environmental impact assessment proves that using clean hydrogen sources and sustainable end-of-life strategies is essential for achieving FCEV operational capabilities. The review examines new electrochemistry materials science and hybridization solutions which have become essential methods for creating better efficiency and durability while decreasing costs. The study shows how policy regulations and collaborative programs fast-track hydrogen adoption through their impact on future hydrogen grid integration and renewable hydrogen production and circular economy methods. The review shows how experts from different fields reached their achievements while still facing challenges to improve FCEVs as fundamental components of environmentally friendly transportation systems and clean energy networks. Full article