Solar-driven semiconductor photocatalytic water splitting for hydrogen production is regarded as a green and sustainable approach to address energy and environmental challenges.However,traditional wide-bandgap semiconductor photocatalysts generally suffer from insufficient visible-light response and rapid photogenerated charge carrier recombination,leading to relatively low solar-to-hydrogen conversion efficiency.Therefore,the construction of photocatalytic material systems that combine high activity with low cost has become a critical scientific issue in this field.BaTiO3(BTO)has attracted considerable attention owing to its nontoxicity,low cost,a nd robust structural stability.Nevertheless,its wide bandgap limits its visible-light response,and inefficient separation and transport of photogenerated charge carriers severely restrict its photocatalytic performance.To address these issues,this study proposes a synergistic strategy combining Nb doping with a core-shell-like heterojunction construction to systematically regulate the band structure and interfacial charge behavior of BTO,aiming to achieve low-cost and efficient photocatalytic water splitting for hydrogen production.The chemical structure and morphology of the as-prepared materials were systematically characterized using multiple techniques,followed by a comparative evaluation of photocatalytic performance and an in-depth analysis of the hydrogen production mechanism.The results demonstrate that an appropriate Nb doping level(2%)induces a negative shift in the conduction band edge and significantly improves bulk charge transport,thereby enhancing the photoreduction capability and increasing the hydrogen production rate to 1535.3 μmol·g-1·h-1,which is approximately 4.6 times that of pristine BTO.Subsequently,a Nb-BTO/CN core-shell-like heterojunction was constructed on the surface of Nb-BTO.This configuration optimizes the energy band alignment and markedly broadens the light absorption range.The formation of an S-scheme heterojunction between Nb-BTO and carbon nitride(CN),together with the built-in electric field at the interface,significantly promotes the spatial separation and directional migration of photogenerated charge carriers both within the bulk phase and across the heterointerface.In this S-scheme system,the internal electric field drives electrons from the CN conduction band to recombine with holes from the Nb-BTO valence band while preserving the highly energetic electrons in the Nb-BTO conduction band for efficient proton reduction.Importantly,this configuration preserves the maximum redox capability of the spatially separated electrons and holes.Consequently,the hydrogen production rate of the resultant composite photocatalyst reaches 2993.9 μmol·g-1·h-1,corresponding to 9.2 times and 2.0 times that of pristine BTO and Nb-BTO,respectively.The results demonstrate the synergistic effects of Nb doping and CN core-shell-like heterojunction engineering on energy band modulation and interfacial charge carrier dynamics in BTO-based photocatalysts.Specifically,Nb doping shifts the conduction band edge of BTO toward a more negative potential and improves crystallinity as well as bulk carrier transport characteristics.Meanwhile,the S-scheme heterojunction constructed with CN further refines the charge transfer kinetics and enhances the thermodynamic driving force for reduction reactions.The synergy arises from the fact that Nb doping optimizes the bulk properties of BTO,while the CN shell extends light absorption and facilitates interfacial charge separation.Collectively,these synergistic modifications substantially enhance photocatalytic hydrogen production performance.This work provides a rational material design strategy and an experimental basis for constructing high-efficiency titanate-based photocatalytic hydrogen generation systems and offers new insights into the cooperative use of elemental doping and heterojunction engineering in wide-bandgap oxide photocatalysts.
In recent years, artificial intelligence (AI) has witnessed tremendous progress, particularly in the field of engineering applications. From simple machine learning (ML) models and deep learning (DL) models equipped with complex image processing capabilities to the rapidly advancing large language models, these have demonstrated formidable capabilities in engineering applications. Microfluidics, a crucial technology in chemical synthesis and life sciences, integrated with AI has emerged as a significant trend in the field of microfluidics. The combination of AI’s powerful data processing capabilities with the high-throughput generation, high-precision controllability, and rapid reaction analysis capabilities of microfluidics provides a powerful toolkit for fields such as materials science, chemical reaction, and biomedicine. Compared to traditional manual analysis methods, AI-assisted microfluidic technology provides faster processing speeds and reduced human intervention to address the challenges in conventional microfluidics, such as reliance on researcher experience, poor experimental repeatability, and time-consuming optimization processes. Simultaneously, AI models can facilitate investigations into fundamental microfluidic principles. This represents a highly promising direction, yet literature that systematically summarizes and elaborates on this emerging interdisciplinary field remains scarce. This paper systematically reviews the research progress of AI-assisted microfluidic technology. We start by introducing the AI models, which are employed in the microfluidics domain, including four common ML models: tree-based models, support vector machines (SVM), DL, and reinforcement learning (RL). Tree models typically possess strong interpretability. SVM is a common classification model suitable for complex data. DL models are frequently used image processing models that achieve functions such as object detection and image classification by simulating the human brain via neural networks. RL is a distinct trial-and-error AI algorithm that continuously enhances its own performance through interaction with the environment. Next, we discuss the applications of AI-assisted microfluidic technology from several aspects: microfluidic droplet generation, microreactor optimization design, micro/nanomaterial synthesis, catalytic reactions, and biomedical detection. Regarding droplet microfluidics, we examine how AI models predict the generation performance and employ explainable frameworks to investigate the underlying factors governing these processes. We showcase the use of DL for flow pattern recognition and the real-time tracking of droplets and bubbles. Turning to microreactors, we analyze the integration of ML for structural design and performance optimization. In the realm of micro/nanomaterial synthesis, we explore AI-driven approaches for performance prediction and the construction of autonomous synthesis platforms. In the field of chemical reactions, we discuss the application of AI to identify optimal reaction conditions and enhance substance detection. Finally, we discuss the advancement of AI in cell sorting, high-precision detection, and the forecasting of cellular changes within the biomedical field. In conclusion, this review provides an outlook on the future development of this interdisciplinary field. We propose four perspectives: the expand of standardized protocols and highly versatile interfaces, the development of efficient label-free, semi-supervised models and high-precision models suitable for small datasets; the creation of end-to-end AI models that bypass complex feature extraction steps; and the integration of AI models with integrated microfluidic chips to develop automated platforms and realize innovative microfluidic application modes.
Ultrasound image segmentation is critical for accurate diagnosis and effective treatment planning.Although numerous deep learning methods have been developed for organ and lesion segmentation in ultrasound images,their performance remains limited by indistinct anatomical boundaries and high speckle noise.To solve these problems,this study improves the U-Net model and proposes a network model,MFDU-Net,based on multiscale feature extraction and frequency attention denoising.First,a multi-resolution input(MI)module is introduced at the network input end to select pooling windows of different sizes for size reduction from the feature maps extracted after one convolution of the input image.The feature maps are then overlaid with the corresponding-size encoding layers by channels for feature fusion,and the model provides input information for different encoding layers for subsequent encoding operations.Second,the convolutional encoding layer of the original U-Net is replaced with a multi-scale separation convolution(MSC)module to capture the image feature information at different scales.Unlike existing multiscale methods that use different sizes of convolution kernels in parallel,the proposed framework employs multiple feature extraction branches with the same 3×3 convolution kernel.By c hanging the number of convolutions,the same effect as changing the size of the convolution kernel is achieved,enabling multiscale feature extraction and allowing the network to adapt to targets of different sizes and positions,thus enhancing its expressive power and robustness.Finally,considering that the U-Net skip connection does not consider the direct transmission of noise and irrelevant information when fusing the encoder and decoder features,a frequency-denoising attention module is introduced in the skip connection.Drawing on the ideas of the CBAM module,frequency-space attention and frequency-channel attention modules are designed to remove noise and weight feature information,respectively.This overcomes the limitations of spatial domain feature learning commonly found in most medical image segmentation networks and reduces the impact of irrelevant information and noise on the model segmentation performance.U-Net,DeepLabv3+,Att U-Net,ACC-Unet,and FCRNet were used as comparative methods to evaluate the proposed MFDU-Net on two self-built kidney ultrasound datasets and two publicly available datasets:DDTI and ISIC2018.Considering the small number of collected images and the requirement of segmentation models for the training set size,data augmentation was performed on all the datasets used in this study.Six quantitative evaluation indicators were selected:Jaccard coefficient,recall rate,accuracy rate,Dice coefficient,HD95,and ASSD.Compared with the baseline U-Net,MFDU-Net improves the dice index by 5.47,9.66,4.83,and 3.59 percentage points,respectively,and the HD95 distance index decreased by 36.59,5.76,48.25 and 25.75,respectively,achieving good segmentation results.To verify the effectiveness of the improved modules designed in this study,ablation experiments were conducted using a kidney ultrasound dataset.The experimental results show that,compared with existing advanced medical image segmentation models,MFDU-Net demonstrates superior performance in segmenting organs and lesions.
Currently, machine learning and artificial intelligence (AI) methods are predominantly applied to the research and development of high-entropy alloys. Although high-entropy alloys exhibit superior properties and significant industrial potential, their manufacturing costs remain prohibitive, and established industrial applications are currently limited. Conversely, the vast majority of steel products are low-entropy alloys primarily composed of Fe, C, and trace amounts of Mn, Cr, Ni, Nb, and other elements. Presently, Chinese iron and steel enterprises rely largely on traditional “trial and error” methods to develop new steel grades. This traditional “research and imitation” process requires time and high research and development (R&D) expenditure. To enhance R&D efficiency, the utilization of AI methods to facilitate new product development, optimize existing process parameters of existing products, and improve product quality is a critical technological requirement for the industry. This paper proposes a novel product development model based on AI methods, utilizing IF steel R&D as an industrial case study. Given the high-dimensionality, strong coupling, and nonlinearity characteristic of industrial production data, eight machine learning models were evaluated to predict the mechanical properties of IF steel. To ensure model generalization, the data was regularized, the super-parameters were optimized, and the training set underwent five-fold cross-validation. The prediction accuracy and applicable scenarios for each model are subsequently discussed. Among the eight models for predicting the mechanical properties of interstitial-free (IF) steel, the random forest (RF) and deep neural network (DNN) models demonstrated superior accuracy, with R2 > 0.97. Furthermore, the kernel principal component analysis (KPCA) model was employed to reduce the dimensionality of high-dimensional components and process parameter features into two-dimensional principal component vectors. A material fingerprint was then established using the Gaussian mixture module (GMM) model. This fingerprinting technique facilitated the visualization of high-dimensional feature data, enabling the analysis and observation of data distributions and the identification of potential mining intervals and the spatial location of the generated data. These potential spaces represent optimal search intervals for the discovery of novel materials. Finally, a generative model based on Wasserstein auto-encoders (WAE) was proposed to explore the potential composition and process parameter spaces. The WAE model can generate thousands of potentially valuable samples from which specific candidates can be selected for industrial testing. This digital “trial and error” approach serves as a robust alternative to traditional methods, accelerating product development while significantly reducing R&D costs and durations.
During hot stamping,the sliding contact between an aluminum alloy sheet and H13 tool steel often results in severe wear and adhesive material transfer.These tribological phenomena can significantly degrade forming quality and reduce die service life.In this study,the high-temperature tribological behavior of a heated 7075 aluminum alloy sheet sliding against H13 steel was investigated under conditions designed to simulate the aluminum hot-stamping practice.Experiments were conducted using a self-developed strip-type high-temperature friction tester.Based on the actual process sequence for aluminum hot stamping,the tester was used to reproduce a combined"solution treatment-forming-quenching"cycle such that the friction pair experienced thermal and mechanical histories similar to those encountered in industrial production.The main purpose was to elucidate how normal pressure and die surface condition i nfluenced friction evolution,adhesive transfer,and the dominant wear mechanisms at elevated temperature.Three representative surface conditions of H13 steel were examined:(I)quenched and tempered(Q&T);(II)plasma nitrided after Q&T(PN);and(III)physical vapor deposition TiN coating applied after Q&T(PVD-TiN).Under different normal loads,the coefficient of friction and extent of adhesive wear/transfer were quantified and the friction and wear mechanisms were analyzed for each surface condition.The results show that as the normal pressure increases,the friction coefficient generally rises and adhesive wear becomes more severe.Higher contact pressure increases the real area of contact and promotes stronger interfacial bonding between the softened,high-temperature 7075 aluminum and steel surface,thereby accelerating sticking,tearing,and the formation of transfer layers.In contrast,both the PVD-TiN coating and plasma-nitrided layer exhibit excellent friction-reducing performance compared with the baseline Q&T surface,indicating that surface engineering is an effective approach for mitigating galling during aluminum hot stamping.At a moderate pressure of 4.5 MPa,the PVD-TiN surface produces a lower coefficient of friction than the nitrided surface.However,because the TiN coating is relatively thin,it can undergo local spallation under combined thermal effects and tangential shear.Once delamination occurs,the fresh metallic substrate is exposed,providing highly reactive sites that facilitate strong adhesion to the aluminum sheet.As a result,despite the lower measured friction coefficient,adhesive wear on the PVD-TiN die surface is more pronounced than on the nitrided die under this intermediate-pressure condition.By comparison,the plasma-nitrided layer contains nitride compound phases,such as Fe3N and Fe4N,which create a harder and more chemically stable near-surface region and can effectively suppress adhesion,thereby reducing material pickup and transfer.At a higher pressure of 5.4 MPa,the PVD-TiNTiN-modified die not only maintains a relatively low coefficient of friction but also benefits from TiN-related hard particles formed or retained at the interface.These particles can function as protective third-body constituents,reducing direct metal-to-metal contact and significantly alleviating adhesive wear.Under this high-pressure condition,the overall anti-galling performance of the PVD-TiN surface is superior to that of the nitrided surface.In contrast,the nitrided die under high pressure tends to experience plastic deformation,which generates agglomerated Fe-Cr alloy particles and worsens the interfacial contact state,ultimately destabilizing friction behavior and aggravating wear.In summary,PVD-TiN provides a more pronounced friction-reduction effect than plasma nitriding in the simulated 7075/H13 hot-stamping tribosystem and is particularly suitable for medium-to-low pressure hot-stamping applications where low friction is critical.Plasma nitriding,on the other hand,offers superior structural stability of the modified layer,making it better suited to heavy-load,high-pressure stamping scenarios in which resistance to deformation and long-term surface integrity are essential.
To solve the existing problems in deep-hole blasting,such as the high clamping effect of rock at the bottom of blast holes,low utilization rate of explosive energy,and poor directional fracture control effect,and to reveal the fracturing mechanism of circular double-shaped charge blasting,this study adopted the ABAQUS numerical simulation software and employed the smoothed particle hydrodynamics-finite element method(SPH-FEM)coupled algorithm to establish blasting models of circular single-and double-shaped charge tubes.A systematic comparison was carried out between the two charge structures in terms of the explosive energy release law,migration characteristics of explosive products,stress response,and deformation characteristics of the shaped charge tube.B lasting test on organic glass model was conducted to systematically analyze the mechanisms of crack initiation,propagation,and penetration under the action of circular-shaped charge blasting.The results show that the circular single-shaped charge tube can effectively control the flow direction of explosive products and facilitate the directional release of explosive energy.The peak stress of the tube wall at the shaped charge slot is approximately 22.9%higher than that at the non-shaped charge slot.Owing to the buffering effect of the inner tube,the circular double-shaped charge tube reduces the peak stress at the shaped charge slot of the outer tube by approximately 16.4%,which significantly reduces the deformation of the outer tube.At the same time,the peak velocity of particles ejected along the shaped charge slot is approximately 24.9%higher than in the single-shaped-charge structure,indicating the higher efficiency of directional energy convergence.The model test results demonstrate that circular-shaped charge blasting can preferentially form circular cracks extending along the direction of the shaped charge slot at the bottom of the blast hole,which greatly reduces the clamping effect at the bottom of the hole,guides the radial cracks to extend along the circular crack surface,and improves the crushing effect of the rock mass at the bottom of the hole.Compared to circular single-shaped charge blasting,double-shaped charge blasting exhibits earlier crack initiation and accelerated propagation.It yields a 28%increase in peak velocity,a more linear crack trajectory,and a highly uniform fracture surface.This study elucidates the synergistic fracturing mechanism of the circular double shaped charge,and the findings provide a theoretical foundation and practical guidelines for the precise regulation of explosive crack propagation in deep-hole blasting engineering.
Multivariate time-series forecasting is a core technology for a wide range of critical applications,including intelligent scheduling,risk management,and resource allocation in complex modern systems.Although transformer-based models have demonstrated formidable capabilities in capturing long-range dependencies through sophisticated self-attention mechanisms,their practical effectiveness in high-dimensional scenarios remains significantly restricted by inherent deficiencies in channel modeling strategies and supervision mechanisms.In typical high-dimensional real-world settings,modeling all variables jointly and uniformly tends to introduce a disproportionate number of redundant or weakly correlated channels,which inevitably leads to distortion of the underlying channel structures.This structural distortion not only interferes with the extraction of critical informative temporal features,but also disrupts the model's ability to accurately capture and learn the label autoregressive dependencies inherently prevalent in future sequences,triggering a cascading amplification of prediction errors that exacerbates supervision bias and degrades forecasting stability.To address these multifaceted challenges,this paper proposes a novel channel-aware time-frequency disentangled(CATFD)forecasting m odel specifically oriented toward achieving long-term prediction stability.The model adopts a sophisticated dual-branch architecture comprising a main branch and an auxiliary branch to synergistically refine the temporal dynamics and purify channel structures.The main branch focuses on deep temporal modeling by integrating a mixture-of-experts mechanism with a multiscale decoupling module,where a dynamic routing system enables the adaptive selection of specialized representations under heterogeneous patterns,whereas adaptive filters capture multiscale dynamics by effectively separating long-term trends from seasonal fluctuations.Simultaneously,the auxiliary branch operates within the frequency domain to construct a learnable channel mask using a soft sparsity mechanism designed to filter out noisy and redundant channels,thereby ensuring that the model concentrates on the most informative variables.To achieve seamless cross-branch synergy,a masked attention mechanism is introduced to dynamically regulate the modeling paths of the main branch based on the refined structural importance provided by the auxiliary branch.Consequently,we further introduce a dual-domain dynamic joint supervision mechanism to collaboratively optimize time-domain prediction losses and frequency-domain reconstruction losses.By explicitly incorporating label autoregressive dependencies and leveraging the orthogonal properties of the frequency domain to weaken interlabel interference,this approach effectively mitigates the supervision bias problem and ensures temporal consistency across the prediction steps.Extensive experiments are conducted on multiple real-world datasets from diverse domains,including power systems(Electricity transformer temperature,ETT),transportation networks(Traffic),and financial markets(Exchange).The experimental results demonstrate that CATFD consistently achieves superior performance compared with representative state-of-the-art transformer-and graph-based baselines,with gains being particularly evident in long-horizon tasks,where error accumulation is traditionally the most significant.Furthermore,robustness tests conducted under various noisy environments and perturbation conditions verify that CATFD maintains stable performance with limited degradation,showcasing its exceptional anti-interference capability.These results collectively indicate that the proposed method significantly improves prediction consistency and generalization ability,providing a robust solution for multivariate time-series forecasting in complex and uncertain real-world scenarios.
This study investigates the abrasion effects of coarse aggregate-modified paste on 16Mn steel elbow pipes used in mining pipeline backfill systems. Incorporating coarse aggregates into paste backfill is an effective strategy to increase the backfill strength and reduce costs. However, it increases the risk of abrasion in pipeline elbows, which can shorten pipeline life and compromise slurry transport safety and efficiency. Elbow sections are also vulnerable to wear from flow direction changes and particle impacts; thus, understanding these effects is key to pipeline reliability. The objective of this study is to establish the abrasion influence laws of coarse aggregate-modified paste on elbow pipes. To this end, the study uses contact stress and indentation depth as key indices to systematically evaluate pipeline wear severity. Using Hertz contact theory, quantitative models are developed to describe the interaction between spherical coarse particles and the inner wall of the elbow pipe. These models enable the calculation of contact stress and indentation depth as functions of particle size, sliding velocity, and elbow curvature. The study also employs a combination of gray relational analysis and the SHapley additive exPlanation (SHAP) method to determine the contributions of various factors to the abrasion process. Specifically, it investigates the influence of coarse aggregate particle radius, slurry sliding velocity, and elbow pipe radius on wear. Gray relational analysis provides a measure of the correlation between each factor and the abrasion indices, while SHAP analysis quantifies the impact of each factor in a predictive model analysis. The results reveal clear trends in how the influencing factors affect abrasion. The analysis identifies sliding velocity as having the most pronounced effect on contact stress and indentation depth, with both increasing as the coarse aggregate radius and sliding velocity increase. This relationship follows an approximate power-law form, indicating nonlinear sensitivity to changes in particle size and velocity. Larger elbow pipe radii (gentler curvature) lead to lower contact stresses and shallower indentations for particles of a given size and speed. The peak contact stress is 105.9 MPa, which is approximately 30.7% of the typical yield strength of 16Mn steel (approximately 345 MPa). This indicates that under these conditions, the pipe wall does not experience plastic deformation due to individual particle impacts. Instead, wear is attributed to repetitive impacts causing fatigue damage. Observations of worn pipe surfaces suggest that the dominant abrasion mechanisms are fatigue cracking and thin-layer spalling. Small cracks initiate and propagate under cyclic loading, ultimately causing thin layers of material to spall off the surface. In conclusion, the study clarifies the hierarchy of factors influencing abrasion in pipelines transporting coarse aggregate-modified backfill and identifies the dominant wear mechanisms. These insights provide guidance for mitigating pipe wear in practice. For example, optimizing the elbow geometry (increasing the radius of curvature), selecting the appropriate aggregate size distribution (to limit the effect of large particles), and applying surface treatments or coatings to the pipe wall are recommended to mitigate abrasion effectively. Implementing these strategies can significantly extend pipeline service life and reduce maintenance requirements in abrasive slurry transportation.
Heavy mining trucks are key equipment in open-pit haulage systems,where the available roadway space is often narrow in relation to the vehicle's size,resulting in extremely difficult driving.With the rapid advancement of mining intelligence,autonomous-driving technology has become an essential means of improving production efficiency to ensure operational safety and reduce operating costs.As a core component of autonomous-driving systems,path tracking control plays a decisive role in ensuring stable vehicle motion along a reference path.However,heavy mining trucks exhibit pronounced steering-mechanism constraints and significant signal transmission delays.Under the combined influence of sharp curves and long delays,path tracking systems tend to exhibit sluggish responses that rapidly increase tracking errors and even instability.Existing control methods struggle to simultaneously handle the c ompound effects of steering constraints and time delay,limiting their engineering applicability.To address the response lag caused by front-wheel steering-rate constraints in sharp-curve environments,a preview correct control(PCC)algorithm was developed by introducing the future heading of the reference path as preview information and incorporating the keypoint displacement error.The preview component improves steering proactiveness,while the correction component enhances responsiveness to current deviations to enable stable posture adjustments during curve entry,mid-curve,and exit.The PCC does not rely on complex models or high-performance computing platforms,making it suitable for the real-time operation of low-power onboard controllers.To address signal transmission delays in autonomous-driving systems,a multistep motion-compensation delay compensator is established by analyzing the PCC output structure and dynamic characteristics of a heavy mining truck to predict the vehicle's posture evolution during the delay interval and generate new control inputs that counteract the delay effects.By integrating the PCC with the delay compensator,a path tracking control system capable of simultaneously handling steering mechanism constraints and long delays was achieved for heavy mining trucks.Simulations were conducted under no-load and full-load conditions,followed by full-load field experiments.In no-load simulations at 20 km·h-1 on a U-shaped curve with a radius of 35 m,the PCC achieved a maximum displacement error of 0.0892 m,which is significantly more accurate than proportional-integral-derivative(PID)and preview PID and close to the nonlinear model predictive control(NMPC).Its average computation time was only 0.1514 ms,outperforming NMPC in terms of real-time capability.Under fully loaded conditions with a 0.4 s signal delay,the PCC combined with the delay compensator maintained the maximum displacement error within 0.1537 m,while the uncompensated PCC showed error divergence in sharp-curve sections.This demonstrates the critical role of the proposed compensation strategy in ensuring system stability under long-delay conditions.The compensator increased the average computation time by only 0.0982 ms,which had a negligible impact on real-time performance.Two sets of full-load field tests were conducted,with an actual signal delay of approximately 0.4 s.The maximum displacement errors were 0.1976 and 0.2073 m.In both tests,the vehicle navigated the sharp curve stably,without any loss of control or noticeable yaw deviations.Overall,the simulation and experimental results demonstrate that the proposed control system maintained a stable and reliable path tracking performance under significant steering-mechanism constraints and long signal delays,achieving a favorable balance between accuracy,real-time capability,and engineering deployability.Therefore,it is well suited for practical autonomous-driving applications in heavy mining trucks.
Material service evaluation is crucial to ensure the safety of major engineering equipment and optimize maintenance strategies. It encompasses performance characterization, failure analysis, and the prediction of the remaining life. However, as service environments in fields, such as aerospace, nuclear energy, and transportation, have become increasingly complex, traditional evaluation methods have limitations. These conventional approaches, which often rely on periodic offline testing and empirical formulas, struggle to meet the modern demands for real-time monitoring, high-fidelity accuracy, and intelligent decision-making. Digital twin technology has emerged as a transformative solution to these problems. By integrating high-fidelity physical models, data-driven approaches, and real-time sensing data, a digital twin can enable dynamic bidirectional mapping between a physical entity and its virtual counterpart. This technology enables the continuous perception of material states and the precise prediction of service performance throughout the lifecycle. In terms of material degradation, the four most prevalent and critical failure modes are corrosion, fatigue, fracture, and wear. However, their occurrence directly threatens the operational safety of large-scale structures. This paper provides a systematic review of recent progress in the application of digital twin technology to evaluate these four typical failure modes. For corrosion evaluation, this study highlights how a digital twin integrates environmental sensor data with electrochemical models to transition from offline analysis to real-time rate prediction. Recent studies on fatigue and fracture have emphasized the integration of multiscale simulation techniques. These approaches combine microscopic crystal plasticity modeling with macroscopic finite element analysis. Consequently, the accumulation of fatigue damage and the propagation behavior of cracks can be tracked with improved reliability and accuracy. For wear evaluation, digital twin technology is increasingly incorporating machine-learning and transfer-learning techniques. These methods can identify the degradation patterns in tools, gears, and other mechanical components under varying operational conditions. Such approaches significantly enhance the adaptability and predictive capability of wear-monitoring systems. In addition to reviewing failure-mode applications, this paper provides a comprehensive discussion of the key enabling technologies required to construct an effective digital twin framework for material service evaluation. Multisource heterogeneous data fusion was identified as the fundamental basis for integrating the experimental measurements, operational monitoring data, and simulation outputs. Multiscale modeling is an essential approach for connecting microscopic damage mechanisms to macroscopic structural responses. Furthermore, real-time data transmission and edge computing technologies were examined. These technologies support low-latency communication and ensure synchronization between physical systems and their digital counterparts. Despite considerable progress in recent years, several technical challenges remain. The integration of high-dimensional heterogeneous data requires further methodological developments. Multiscale simulations often involve high computational costs, which limits their real-time application potential. Additionally, balancing model complexity with real-time performance remains a significant issue in practical engineering deployment. Finally, future development directions for digital twin technology in material service evaluation are discussed. Emerging cloud, edge, and terminal collaborative architectures are expected to enhance computational efficiency and data-processing capability. The integration of uncertainty quantification methods is also emphasized, as it enables probabilistic risk assessment and improves decision reliability. These developments will further promote intelligent and reliable material service evaluation in complex engineering systems.
Considering the engineering context in which lining structures in cold regions are successively exposed to freeze-thaw(F-T)cycles and cyclic dynamic disturbances during tunnel excavation,cylindrical specimens with a diameter of 50 mm were fabricated.These included pure sandstone,pure concrete,and composite specimens with sandstone layer thickness ratios of 25%,50%,and 75%,respectively.Thereafter,constant-amplitude cyclic impact compression tests of the F-T-damaged concrete-sandstone combination were conducted using a splitting Hopkinson pressure bar system with a diameter of 50 mm.The effects of the sandstone layer thickness ratio and number of F-T cycles(0,5,10,20,and 40)on the pore size distribution,stress wave characteristics(i.e.,incident,reflected,and transmitted stress waves),dynamic peak stress,anti-cyclic impact times,failure mode,and micromorphology of concrete-sandstone composite specimens were investigated by integrating nuclear magnetic resonance(NMR)and scanning electron microscopy.A composite damage variable,which could consider the effects of the F-T cycle and cyclic impact,was defined based on the variation of t he longitudinal wave velocity in the present study.Experimental results showed that with increasing number of F-T cycles,a notable divergence emerged in the cyclic impact resistance among the different specimen types.Specifically,the C-0%specimens maintained their original impact resistance even after 40 cycles,while the anti-cyclic impact times of both the composite and sandstone specimens exhibited varying degrees of degradation with increasing number of F-T cycles.As the sandstone layer thickness ratio increased,the number of micropores in the composite specimens decreased,while the number of mesopores increased significantly,accompanied by an increase in the dynamic peak stress.Conversely,the dynamic peak stress of specimens decreased with increasing number of F-T cycles.For the C-R-25%and C-R-75%specimens,the dynamic peak stress values without F-T treatment were 25.82 MPa and 27.87 MPa,respectively.After 40 F-T cycles,these values decreased to 19.04 MPa and 19.79 MPa,representing reductions of 26.26%and 28.99%,respectively.With the increase in the cyclic impact times,both the dynamic peak stress and dynamic secant modulus of the composite specimens decreased,while the dynamic peak strain increased.The amplitude of the reflected wave displayed an upward trend in concrete,sandstone,and concrete-sandstone composite specimens with increasing cyclic impact times,while the amplitude of the transmitted wave decreased and the occurrence of the peak value was delayed.Following cyclic impact loading,the concrete,sandstone,and composite specimens primarily exhibited two failure modes:tensile splitting and edge shear failure.The quantity and distribution of primary and secondary cracks were closely related to the sandstone layer thickness ratio within the composite specimens and number of F-T cycles.As the number of F-T cycles increased,the failure mode of the composite specimens evolved from a single main crack to multiple cracks,and the internal damage intensified within both the sandstone and concrete layers.Furthermore,distinct intergranular and transgranular cracks were observed within the sandstone layer,although no obvious damage was detected at the interface between the two layers.The"damage accumulation threshold"of concrete,sandstone,and concrete-sandstone composite specimens gradually increased with the number of F-T cycles.The findings of this study provide experimental evidence and references for analyzing the stability and durability of tunnel lining structures in cold regions.
In nature,magnesite is frequently intergrown with dolomite and other carbonate minerals,forming complex ores that require efficient separation.The flotation separation of magnesite from dolomite remains a significant challenge in mineral processing owing to their similar crystal structures and solution chemistry,which result in comparable surface hydration properties and flotation behavior.Precise regulation of surface wettability is central to achieving selectivity.Previous research has predominantly focused on developing novel flotation reagents,particularly collectors and depressants,with emphasis on their interaction mechanisms at mineral surfaces.Recent studies employing ab initio molecular dynamics(MD)have provided fundamental insights,indicating that although both minerals favor molecular water adsorption,magnesite exhibits a slightly stronger affinity for water than dolomite,thereby affecting subsequent interfacial processes.Molecular simulation can effectively elucidate such intricate microscopic interactions at mineral-water-collector interfaces.To elucidate the atomic-scale mechanism governing the selective adsorption of collectors,this study integrates density functional theory and MD simulations using Materials Studio software.Structurally optimized surface models of m agnesite(104)and dolomite(104),together with a typical anionic collector CP(e.g.,a phosphoric acid ester),are constructed to systematically investigate frontier molecular orbital energetics,surface wettability dynamics,and interfacial adsorption energies.Frontier molecular orbital analysis reveals a narrower energy gap between CP and magnesite(2.643 eV)than in the dolomite system(2.670 eV),suggesting that electron transfer from the highest occupied molecular orbital of CP to the LUMO of magnesite is thermodynamically more favorable,thereby promoting selective adsorption on magnesite surfaces.MD simulations of wettability behavior indicate that dolomite exhibits a lower water contact angle(7.5°)than magnesite(9.2°),reflecting its intrinsically stronger hydrophilicity.After CP adsorption,the diffusion coefficient of water molecules on the magnesite surface increases significantly and exceeds that on dolomite,demonstrating that CP adsorption effectively disrupts the hydration layer and enhances the hydrophobicity of magnesite.This enhanced hydrophobicity is expected to facilitate bubble attachment more effectively on magnesite than on dolomite.Interfacial interaction energy analysis further confirms that CP exhibits significantly stronger adsorption energy(-406.8 kJ·mol-1)on magnesite than on dolomite(-95.6 kJ·mol-1),along with higher deformation energy(166.7 kJ·mol-1 vs.161.9 kJ·mol-1),indicating superior adsorption stability and interfacial compatibility on the magnesite surface.This study elucidates the multidimensional selective mechanism of the CP collector toward magnesite from electronic,dynamic,and energetic perspectives,providing a theoretical foundation for reagent design.To translate these molecular-level insights into practical separation strategies and address the complexity of industrial flotation systems,several aspects warrant further investigation.Future studies should focus on(1)investigating the combined effects of surface pretreatment(e.g.,acid etching to modulate roughness and active sites)and CP adsorption;(2)examining the influence of solution chemistry(e.g.,dissolved ionic species and pH)on the established adsorption model;and(3)extending simulations to the gas-liquid interface to evaluate the potential role of CP in bubble surface modification and its effect on overall flotation kinetics.These investigations will help bridge the gap between molecular-scale insights and the optimization of macroscopic flotation performance.
To solve the trajectory planning problem of unmanned aerial vehicles in three-dimensional (3D) environments, a dynamic adjustment strategy based on Levy flight is proposed to improve the spherical vector-based particle swarm optimization (SPSO) algorithm, thus forming the SPSO algorithm based on Levy flight dynamic adjustment strategy (LDS-PSO). This algorithm balances the global and local search capabilities, and alleviates the problems of slow convergence speed and being trapped in local optima. First, the trajectory planning problem is transformed into a multi-constraint objective optimization problem, and a fitness objective function is constructed based on trajectory length, obstacle threat, smoothness, and height changes. Second, the PSO algorithm is analyzed based on spherical vectors. In the initial stage of the search, the algorithm needs to expand the search range to ensure population diversity. In the later stage, it needs to achieve rapid convergence and improve accuracy for inter-group collaboration. Therefore, a strategy for dynamically adjusting learning cognitive factors based on power exponent inertia weight factors and quadratic functions is proposed. Compared with the fixed learning cognitive factors of traditional PSO and SPSO algorithms, this strategy not only balances global and local search capabilities, but also improves the convergence speed. Third, when the difference between the fitness values of particles in adjacent iterations is smaller than a preset small constant, the particles are considered to be trapped in a local optimum. The Levy flight disturbance factor is introduced to prompt particles to quickly escape from local optima. Finally, the effectiveness and convergence speed of the algorithm in a 3D environment are evaluated through simulation testing. Based on the flight environment of drones, four 3D elevation environment models with different obstacle densities were established, and simulation experiments of 3D trajectory planning using the proposed algorithm were carried out in each environment. The simulation results validate the performance of the improved algorithm. The trajectory generated by this algorithm has a lower cost and higher quality while ensuring obstacle avoidance safety, and it exhibits better optimization accuracy and faster convergence speed. In scenarios with sparse obstacles, the performance of this algorithm is consistent with that of the SPSO algorithm. In scenarios with dense obstacles, there are differences in the fitness and convergence speed between this algorithm and the SPSO algorithm. The LDS-PSO algorithm has a lower fitness value, faster convergence speed, and smoother path compared with the SPSO algorithm. In dense obstacle scenarios, various algorithms were run repeatedly 20 times to reduce random errors. Compared with the traditional PSO, linearly decreasing inertia weight IPSO (Improved particle swarm optimization algorithm), and SPSO algorithms, the LDS-PSO algorithm has the best stability and the lowest trajectory cost. In the 20 tests, the average best fitness value of the LDS-PSO algorithm decreased by 22.92%, 0.51%, and 0.34% compared with the above three algorithms respectively; the average fitness value decreased by 31.18%, 6.33%, and 4.54% respectively; and the average worst fitness value decreased by 36.45%, 16.73%, and 14.77% respectively. The research results indicate that the LDS-PSO algorithm has faster convergence speed, better stability, and higher reliability in 3D trajectory planning.
Greenization and intelligentization are focus areas for the high-quality development of manufacturing industry in the new era,and they are critical for China to address global climate change,achieve"dual carbon"goals,and enhance core industrial competitiveness.Empowered by policies such as dual control over the amount and intensity of carbon emissions and the special initiative of"Artificial Intelligence+Manufacturing,"the in-depth integration of green manufacturing and intelligent manufacturing is not only an inherent requirement for building a circular and sustainable industrial system,but is also important to drive the transformation and upgrading of the manufacturing industry from extensive development to refined and low-carbon development.This is of great practical significance for realizing the coordinated development of ecological and economic benefits.Based on current opportunities and challenges impacting the development of the manufacturing industry,this study first performs a systematic review of the evolution process and development status of green manufacturing and intelligent manufacturing.The development of green manufacturing has u ndergone an evolutionary process from passive compliance to active management.Relying on new-generation information technologies such as big data,artificial intelligence and the Internet of Things,intelligent manufacturing has realized the intelligent transformation of production processes and greatly improved production efficiency and product quality.On this basis,the study analyzes the internal logic of their integrated development,and highlights that green manufacturing provides development orientation for intelligent manufacturing,while intelligent manufacturing offers technical support for green manufacturing.The synergies between the two approaches are mutually beneficial,and their integrated development is important for the high-quality development of the manufacturing industry.Secondly,to solve the current problems such as inadequate integration and poor coordination between the two,this paper proposes the concept of endogenous carbon variables(ECV),embedding carbon factors into the whole process of intelligent manufacturing and breaking the traditional model of"production first,emission reduction later."To achieve this,the study constructs an integrated architecture of"Sensing-connecting-cognition-control-carbon."In this architecture,"Sensing"accurately collects carbon data throughout the production process;"Connecting"realizes the interconnection and intercommunication of various sets of data;"Cognition"achieves the precise accounting and optimization of carbon footprints relying on algorithmic models;and"Control"realizes the real-time regulation of carbon emissions in production processes.As the ultimate goal and core of the architecture,"Carbon"is integrated into various levels and serves as the ultimate goal of the entire framework.This paper systematically discusses the core technical system of ECV intelligent manufacturing,covering intelligent perception and digital twin technology,data circulation and industrial Internet technology,data-driven and AI optimization technology,as well as closed-loop control and edge computing technology,which provides robust technical support for the implementation of the integrated model.Finally,combined with the actual development of China's manufacturing industry,it systematically analyzes practical challenges in the implementation of the ECV intelligent manufacturing model from three dimensions,namely technology,ecology,and management.From the technological perspective,green and low-carbon intelligent manufacturing systems are limited with respect to carbon data acquisition,multi-source data fusion,and intelligent optimization and control.From an ecological perspective,there is a lack of sound coordination mechanisms and industrial chain support.From a management perspective,imperfect corporate carbon management systems and data governance mechanisms,as well as human capacity deficiencies all restrict the green transformation of manufacturing enterprises.This paper aims to provide solid theoretical support for the coordinated green and intelligent development of the manufacturing industry,help China's manufacturing industry accelerate the construction of a low-carbon industrial system,and elevate the development of the manufacturing industry to a new level.
The cooperative inspection of multiple unmanned aerial vehicles (UAVs) in confined spaces, such as industrial plants, utility tunnels, and warehouse parks, presents significant challenges. These environments often feature narrow passages, frequent occlusions, and complicated layouts, resulting in congestion and close-range interaction conflicts among UAVs. Additionally, the instability of localization systems and the limited computational power available on UAVs further hinder their effective deployment in such environments. These conditions require novel solutions for safe and efficient UAV operation. To address these challenges, in this study, a cooperative deconfliction method, which integrates virtual-tube-based geometric layering with learning-enhanced control mechanisms, is proposed. In the geometric planning layer, a virtual tube is constructed using a parameterized centerline and variable radius, enabling the creation of allocable sub-channels. Continuous reference sub-trajectories are generated within the Frenet frame to ensure macroscopic spatial separation of the UAVs. This approach ensures that UAVs are kept within safe operational zones and prevents collisions using geometry-based constraints. Furthermore, in the learning-based control layer, a conflict-aware multi-agent proximal policy optimization (MA-PPO) controller with shared parameters is developed. This controller utilizes low-dimensional structured observations, which include task progress, lateral deviation, safety margin, and neighboring UAV information. The decision-making process is constrained to outputting a tangential-speed intention, which reduces the complexity of the control problem and ensures easier training. A pure pursuit method provides lateral geometric correction. When the inter-agent distance falls below a safety threshold, a potential-field-based repulsive velocity term is added to suppress close-range conflicts. To validate the proposed method, simulation experiments were conducted using a five-UAV maze scenario on the AirSim simulation platform. The experiments demonstrate that the UAVs successfully complete the mission without any collisions, with the minimum inter-agent distance consistently remaining above 0.6 m. Additionally, the average lateral tracking error decreased from 0.571 m to 0.201 m, representing a reduction of 64.8% when compared to a distributed rule-based control method. These results showcase the efficacy of the proposed method in terms of trajectory tracking accuracy and cooperative safety in confined spaces. Moreover, the proposed method was tested in a complex, inconsistent scenario, which was not included in the training environment. The results show that the method achieved zero-sample transfer and maintained zero collisions, proving the robustness and generalization ability of the learned policy under unknown geometric constraints. This demonstrates that the method is not only effective in the training environment but also capable of adapting to new, unforeseen scenarios. Overall, the results indicate that the proposed approach can simultaneously ensure safe cooperative passage and accurate trajectory tracking under low-sensing constraints. The integration of geometric constraints with learning-based decision-making provides a promising solution for multi-UAV cooperative missions in confined spaces. By addressing the challenges of low sensing and limited computational power, this method paves the way for the practical deployment of UAVs in complex and restricted environments.
With the rapid acceleration of global industrialization,soil contamination by heavy metals has become a critical environmental challenge,drawing significant attention from the scientific community.Chromium pollution is particularly pervasive,primarily stemming from industrial discharges such as leather processing,textile printing,and electroplating,as well as various agricultural activities.Cr predominantly exists in the trivalent[Cr(III)]and hexavalent[Cr(VI)]oxidation states.Although Cr(III)exhibits low mobility and is readily adsorbed onto soil particles,Cr(VI)is characterized by high solubility,mobility,and acute toxicity.Cr(VI)readily disperses through the vadose zone into the groundwater,posing a severe and persistent threat to ecological safety and human health.Electrokinetic(EK)remediation has emerged as a promising technology for treating low-permeability soils(such as clays)because of its unique electrically driven transport mechanism.However,given the complexity and heterogeneity of actual soil contamination,employing a single remediation technique often fails to achieve the desired pollutant removal efficiency.To overcome this technical bottleneck,an integrated electrokinetic-permeable reactive barrier(EK-PRB)system is designed and optimized in this study.The objective was to achieve in situ interception and removal of migrating pollutants by incorporating a chemically modified a ctivated carbon PRB layer.The synergistic mechanisms between various electrolytes and PRB materials was systematically investigated to optimize the system performance.Specifically,activated carbon was functionalized using hydrochloric acid and cetyltrimethylammonium bromide(a cationic surfactant),to precisely regulate the pore structure and surface charge characteristics of the material.Three electrolytes with distinct driving mechanisms were introduced:citric acid(a complexing agent),potassium chloride(a conductive salt),and sodium dodecylbenzenesulfonate(an anionic surfactant).The remediation efficacy was comprehensively evaluated by monitoring key parameters,including electrical current evolution,soil pH distribution,and spatiotemporal migration patterns of Cr.The experimental results indicate that the remediation efficiency is heavily dependent on the physicochemical compatibility between the electrolyte and the PRB material.The combination of a citric acid(CA)electrolyte with hydrochloric acid-modified activated carbon demonstrated optimal performance,achieving removal rates of 93.10%for Cr(VI)and 77.96%for total Cr.Mechanistically,this superior performance was attributed to the strong chelating action of CA,which effectively prevented precipitation and promoted the desorption of adsorbed Cr from soil particles.Simultaneously,the acid-modified PRB layer provided abundant active sites and favorable surface conditions for the precise interception of migrating chromium-citrate complexes.Comparative analysis confirmed that the anolyte Cr concentration in the EK-PRB remediated group was significantly lower than that in the conventional EK remediated group,verifying the efficacy of the PRB layer in mitigating anode enrichment.Furthermore,BCR(Bureau Communautaire de Référence)sequential extraction analysis revealed that the designed technology substantially altered the chemical speciation of the residual Cr.Post-remediation,the soil exhibited reductions exceeding 91.2%and 64.12%in the bioavailable weak acid extractable and reducible fractions,respectively,which are considered the most environmentally hazardous forms.In summary,compared to conventional EK remediation,the proposed EK-PRB technology not only achieves high-efficiency remediation but also promotes uniform removal and deep stabilization of Cr,offering a robust solution for the remediation of Cr-contaminated sites.
Deep-sea hydrothermal vent polymetallic sulfide deposits exhibit significant enrichment in strategic metallic elements,i ncluding copper,zinc,gold,and silver,and critical rare-earth elements,such as cobalt,selenium,tellurium,and indium,which represents the most economically valuable mineral resources of the 21st century.These deposits,formed through complex hydrothermal processes involving mid-ocean ridge systems and back-arc spreading centers,demonstrate metal concentrations frequently exceeding conventional terrestrial ore deposits by several orders of magnitude,rendering them essential for future mineral resource security.Nevertheless,the harsh deep-sea environment characterized by water depths of 1500-5000 meters,extreme hydrostatic pressures up to 50 MPa,aggressive marine corrosion,and complex sulfide mineral assemblages presents formidable technical challenges throughout the complete"sampling—dissociation—extraction"processing chain for sustainable resource development.This study examines the current technological advances and identifies critical bottlenecks in this strategically significant research domain.Regarding sampling technologies,the evolution from conventional mechanical approaches to intelligent integrated systems is analyzed,encompassing multi-platform operations employing human-occupied vehicles(HOVs),remotely operated vehicles(ROVs),and autonomous underwater vehicles(AUVs).Critical sample preservation issues are identified as fundamental concerns,whereby recovered specimens experience severe temperature variations exceeding 300℃and pressure differentials of 20-50 MPa during ascent from seafloor to surface,inevitably causing mineral phase transitions,oxidation reactions,and microstructural alterations that compromise analytical precision.The proposed innovations include the development of multiparameter monitoring systems with real-time sensing capabilities for temperature,pressure,pH,and electrochemical potential(Eh)coupled with adaptive sample preservation systems utilizing conservation protocols,including pressure compensation chambers,thermal regulation units,and inert gas protection systems,to maintain mineralogical stability and specimen integrity.Concerning dissolution mechanisms,the oxidative leaching kinetics of primary sulfide phases—including pyrite(FeS2),chalcopyrite(CuFeS2),and sphalerite(ZnS)—under coupled pressure-temperature conditions are investigated,encompassing elementary processes such as surface metal-sulfur bond dissociation,electrochemical electron transfer reactions,and aqueous ionic transport phenomena.The galvanic coupling effects characterized by electrochemical potential differences ranging from 0.1-0.5 V,synergistic dissolution enhancement,and competitive inhibition mechanisms within polymetallic sulfide assemblages are elucidated through electrochemical characterization methods.The necessity for establishing multiscale theoretical frameworks spanning five hierarchical levels from atomic-scale quantum mechanics to macroscopic continuum behavior,complemented by kinetic databases integrating density functional theory(DFT)calculations,molecular dynamics(MD)simulations,and in situ spectroscopic characterization techniques,is emphasized for a fundamental mechanistic understanding.Regarding environmentally sustainable extraction methodologies,the advantages and limitations of various green processing technologies are assessed,including biohydrometallurgical processes(specific energy consumption:100-300 kW·h·t-1),electrochemical extraction techniques(current density:50-200 A·m-2),supercritical fluid extraction methods(operating parameters:150-200 ℃,15-25 MPa),and microwave-assisted leaching technologies.The strategic implementation of integrated multi-technology synergistic processes is advocated,incorporating processing schemes such as combined biochemical leaching,microwave-ultrasonic coupling,and electrochemical-ultrasonic enhancement,targeting improvements in metal recovery efficiency of 10-20 percentage points and energy consumption reduction of 20%-30%.Four strategic research directions for future breakthroughs are proposed:(1)development of intelligent in situ sample preservation systems,(2)fundamental investigation of coupled pressure-temperature dissolution kinetics,(3)construction of integrated sustainable extraction processes achieving specific energy consumption<1000 kW·h·t-1 with metal recovery rates>90%,and(4)establishment of digital twin control platforms with intelligent process optimization.These findings provide an essential theoretical foundation and practical technological guidance for deep-sea mineral resource development and represent critical contributions to national resource security and maritime technological advancement.
With the rapid development of the new energy industry, lithium-ion batteries (LIBs) have been extensively applied in various fields, such as electric vehicles and energy storage devices. However, constrained by lagging recycling modes, the global annual output of spent LIBs exceeds one million tons. Spent LIBs contain scarce metals, such as cobalt, nickel, and lithium, as well as toxic components, including electrolytes and binders. The improper disposal of spent batteries is prone to resource waste and pollution of soil and water bodies. Therefore, the efficient recycling and reusing of spent LIBs have become a strategic priority for safeguarding resource security and promoting the achievement of the “dual carbon” goals. As a new type of green solvent developed by mixing hydrogen-bond donors and acceptors in a specific ratio, deep eutectic solvents (DESs) have gradually replaced traditional toxic and volatile solvents. Endowed with prominent advantages such as excellent environmental compatibility, strong recyclability, tunable physicochemical properties, and outstanding dissolution capacity for cathode metal oxides, DESs have demonstrated significant practical value and economic feasibility for the recycling of spent LIBs. This study systematically reviews the research status of spent LIBs recycling using DESs in recent years, focusing on the extraction mechanisms and core principles of valuable metals from battery cathode materials. It analyzes the separation principles of different DESs systems and clarifies the differences in their core leaching mechanisms. When exploring DES-based recycling technologies, the separation efficiency and leaching mechanism of each system are closely related to its composition and structure. For example, hydrogen bond-based DESs mainly rely on hydrogen bond interactions to promote the dissolution of metal oxides, whereas metal-based DESs may enhance the leaching efficiency through synergistic effects between metal ions and target metals. Clarifying these differences in mechanisms can provide a theoretical basis for targeted design and optimization of DESs systems. On this basis, this study further discusses the bottleneck problems faced by current DESs recycling technologies. Although DESs have shown great potential in laboratory research, many challenges remain in their practical application. For instance, the preparation cost of some high-performance DESs is relatively high, which reduces their large-scale application, and the viscosity of some DESs is too high at room temperature, which affects the mass-transfer efficiency of the leaching process and reduces the overall recycling efficiency. The separation and purification technology for the target metals in DESs leaching solutions must be further improved to realize the efficient recovery of valuable metals. Finally, this paper outlines the future technical pathways for the efficient, green, and sustainable recycling of spent LIBs using DESs. Future research should focus on the development of low-cost DESs systems, explore the use of cheap raw materials, such as biomass derivatives, to reduce preparation costs, and optimize the composition ratio of DESs to adjust their physicochemical properties and improve their leaching performance. At the same time, it is necessary to strengthen research on multi-metal synergistic separation technology to realize the selective recovery of various valuable metals in spent LIBs. This review is expected to provide important references for subsequent research and engineering applications in the field of spent LIBs recycling, and contribute to the sustainable development of the new energy industry.
The growing demand for deep and ultra-deep drilling operations presents dual challenges in terms of efficiency and safety for the intelligent operation and maintenance of drilling equipment under complex working conditions.Conventional methodologies,whether relying solely on physical mechanisms or purely driven by data analytics,often fall short in delivering the dynamic adaptability and closed-loop optimization required in modern drilling contexts.Consequently,the integration of virtual and physical realms has emerged as an essential and enabling pathway toward achieving truly intelligent operation and maintenance.This study begins by systematically analyzing the multifaceted challenges encountered in the operation and maintenance of drilling equipment in deep and ultra-deep environments.These challenges span five critical and interconnected levels:at the equipment level,unclear failure propagation mechanisms in highly coupled systems;at the environmental level,extreme pressures,temperatures,and restricted sensing conditions;at the data level,heterogeneity,noise,and low utilization of multisource information;at the model level,the paradigm divide between physics-based and data-driven approaches;at the decision-making level,reliance on static rules and delayed human i ntervention.This analysis underscores the necessity of virtual-physical fusion technology that significantly enhances the potential for mining multi-source data,deepens the integration of mechanistic principles with data-driven insights,and elevates the overall intelligence of operational decisions.A digital twin-driven framework for virtual-physical fusion in intelligent operation and maintenance is proposed for drilling equipment.The framework is designed around four cohesive layers:intelligent perception,twin model,virtual-physical mapping,and operation and maintenance service.The intelligent perception layer employs advanced multidimensional data acquisition technologies,including physical sensors and virtual sensing techniques,to provide a real-time,comprehensive stream of status information from the formation,downhole tools,and surface equipment,thereby forming a reliable and holistic data foundation for the entire system.As the core of the framework,the twin model layer constructs high-fidelity multidomain digital twins that integrate mechanical,electrical,hydraulic,and control subsystems.These models achieve an accurate mapping of the state,behavior,and dynamic responses of the physical equipment,enabling precise simulation,fault propagation analysis,and forward-looking predictive assessment under diverse operational scenarios.The virtual-physical mapping layer ensures dynamic consistency and supports closed-loop optimization by maintaining a continuous bidirectional interaction and real-time state synchronization between the physical entity and its virtual counterpart.Furthermore,this layer enables model calibration and iterative refinement based on the streaming data,guaranteeing that the digital twin remains a faithful representation throughout the equipment lifecycle.Finally,the operation and maintenance service layer aims to reduce the drilling risk,enhance equipment reliability,and improve operational safety.The practical value of this virtual-physical fusion operation and maintenance framework is demonstrated through its application in several key areas:performance evaluation of blowout preventers under diverse shear conditions,intelligent monitoring and early fault detection for hoisting systems using digital twin-driven anomaly diagnosis,and data-driven optimization for drilling pumps through virtual-real data fusion and feature selection.These implementations validate the effectiveness of the framework in enhancing operational awareness,reducing unplanned downtime,and optimizing maintenance resources.Furthermore,they provide a valuable reference for the broader adoption and continued development of intelligent virtual-physical fusion solutions in the oil and gas industry.
The dynamic characteristics of the pantograph-catenary system(PCS)directly affect the current collection quality and operational safety of high-speed trains.While traditional finite element methods(FEM)that utilize nonlinear cable-truss equivalent models accurately characterize the strong nonlinearity and time-varying mechanical behaviors of the PCS,they suffer from prohibitive computational complexity that hinders real-time prediction and digital twin deployment.To address these computational bottlenecks,data-driven surrogate models have emerged.However,standard Fourier neural operators(FNO)rely on fixed-frequency band truncation,which effectively captures low-frequency principal modes but systematically discards critical high-frequency transient details,such as l ocalized contact force mutations and wave reflections.Purely data-driven models also lack explicit physical constraints,leading to severe error accumulation during long-term dynamic simulations.To overcome these multiscale modeling challenges,this paper proposes adaptive Fourier neural operator diffusion model(AFNODM),a novel physics-informed framework that synergistically integrates an adaptive Fourier neural operator(AFNO)with a conditional diffusion model(CDM)to establish a time-frequency collaborative generation paradigm.In the first stage,the AFNO acts as a global physical skeleton generator to capture dominant vibration modes(0-20 Hz).Crucially,we introduce a velocity-based frequency modulation mechanism equipped with deformable convolution kernels,allowing the model to adaptively adjust its spectral receptive field in response to real-time train speeds and neutralize Doppler effects.In the second stage,a CDM-driven post-processing architecture is deployed.Conditioned on the AFNO's output,the diffusion model executes a progressive reverse denoising strategy in the latent space to seamlessly reconstruct the missing high-frequency residual details(above 20 Hz),while kinematic constraint losses are embedded via automatic differentiation to ensure absolute derivative consistency across spatial-temporal fields.Extensive evaluations on a high-fidelity PCS dataset(200-380 km·h-1)demonstrate that AFNODM efficiently solves complex dynamic equations with an unprecedented balance of speed and precision.At 350 km·h-1,the root mean square errors(RMSE)for the displacement,velocity,and acceleration fields are remarkably low at 0.0673,0.1603,and 0.8503,respectively,representing an error reduction of over 50%compared with mainstream baselines such as deep operator network(DeepONet)and physics-informed enhanced Fourier neural operator(PI-EFNO).Frequency-domain analysis confirmed that CDM integration significantly suppressed the high-frequency relative spectral error(RSE)from 16.80%(using pure AFNO)to 6.55%.Cross-line robustness tests across three distinct high-speed railway configurations(Beijing—Shanghai,Guangzhou—Shenzhen,and Beijing—Tianjin)validated the exceptional generalization capabilities of the model under varying structural parameter perturbations.Ultimately,the proposed AFNODM framework provides a highly accurate,resolution-independent,and real-time capable computational engine,paving the way for next-generation digital twins and intelligent predictive maintenance in modern electrified railways.
