Advances in Engineering Research : Possibilities and Challenges
Advances in Engineering Research : Possibilities and Challenges ISSN Print: 3079-5192 ISSN Online: 3079-5206
Multivariate Data Fusion and Health Analysis of Coal Processing Facilities
This paper presents a comprehensive health diagnosis and predictive maintenance management system based on multi - source data fusion. Firstly, the research constructs a data acquisition framework covering multi - source sensors such as vibration, temperature, and pressure. Wavelet transform and empirical mode decomposition are employed for signal processing, and multi - dimensional features in the time domain, frequency domain, and time - frequency domain are extracted. Subsequently, the local preserving projection algorithm is introduced to achieve effective dimensionality reduction and visualization of high - dimensional features. At the diagnostic level, a fuzzy clustering - fuzzy integral fusion diagnostic algorithm based on fuzzy measure theory is innovatively proposed to address the challenge of composite fault identification. At the predictive level, by constructing a degradation feature evaluation system encompassing correlation, monotonicity, and robustness, the self - organizing map network is used to fuse multiple features into a comprehensive health index, and the long - short - term memory network model is utilized to accurately predict the evolution trend of the equipment's health status. Finally, based on the health status assessment and remaining useful life prediction, a full - life - cycle predictive maintenance strategy is formulated, and an intelligent maintenance decision - support system is designed.
Copyright (c) 2025 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by/4.0/
Data-Driven Inverse Design of THz Metamaterials: A Deep Learning Approach for Small-Data Regimes
Inverse design based on deep learning offers a revolutionary paradigm for ac-celerating the development of novel terahertz (THz) metamaterials. However, its application is often constrained by the high cost of acquiring large-scale sim-ulation datasets. This work focuses on the high-accuracy inverse design of THz electromagnetically induced transparency (EIT) metamaterials under small da-taset conditions, systematically evaluating the effectiveness of different deep learning architectures. To achieve precise inversion from a target spectrum to its physical structure, we constructed a representative dataset through parametric electromagnetic simu-lation. On this foundation, we built, trained, and compared three neural network models: a Multi-Layer Perceptron (MLP) as the baseline, a residual fully con-nected network (FC-ResNet) for enhanced deep network training, and a one-dimensional convolutional neural network (1D-CNN) designed for sequen-tial data. The results reveal a key finding: for this inverse design task, the FC-ResNet demonstrated superior predictive performance, achieving a coefficient of de-termination (R²) of 0.9794 on an independent test set. This significantly out-performed the baseline MLP (0.9438) and surprisingly surpassed the theoreti-cally more suitable 1D-CNN. Further analysis suggests that for the EIT inverse problem investigated here, the prediction relies more on the global morphology and correlation features of the spectrum than on localized characteristics. The FC-ResNet, with its deep architecture and effective residual learning mecha-nism, successfully captured this complex, non-local mapping relationship. The core contribution of this work is demonstrating that for complex physical prob-lems, a deep, general-purpose model with sufficient expressive power and stable trainability can outperform a more specialized architecture that is prone to in-formation loss. This finding provides a crucial guideline for model selection in the intelligent design of physical devices, particularly in resource-constrained scenarios
Retraction: Research Progress on Novel Water-Soluble Lubricant Additives
At request of the authors, this article has been retracted by Erytis Publishing Limited in light of clear evidence that the results and conclusions are no longer valid. We thank the authors for notifying us so that the publication record can be amended accordingly. Retraction published: November 10, 2025.
Research on Welding Methods of Monel Alloy
Monel 400 alloy boasts excellent mechanical and processing properties, along with good corrosion resistance in high-temperature acid-base and strongly alkaline media. It is widely applied in high-pressure gas supply systems across aerospace, shipbuilding, chemical, pharmaceutical, and other industries. This article primarily analyzes and studies the welding issues of Monel 400 alloy pipelines, as well as the determination of welding process parameters, aiming to improve the welding process of Monel 400 alloy and ensure welding quality.
AKCD: An Attention-Driven Framework with Kernel Conditional Independence Testing for Time Series Causal Discovery
Causal discovery is crucial for understanding complex system dynamics, espe-cially with non-stationary time series data. This paper presents An Atten-tion-Driven Framework with Kernel Conditional Independence Testing (AKCD), a novel two-stage framework to enhance time series causal discovery accuracy and robustness. In the first stage, AKCD uses a Transformer-based Multi-Scale Temporal Self-Attention Network (MS-TSAN) to capture complex temporal de-pendencies in time series, generating attention matrices and hidden representa-tions for subsequent causal inference. In the second stage, it applies ker-nel-based conditional independence (KCI) tests on the hidden representations to generate a conditional independence (CI) matrix, which is used as prior knowledge to guide causal graph optimization via regularization constraints. This hybrid approach combines deep learning’s feature extraction advantages with statistical tests’ causal inference rigor, making AKCD particularly suitable for non-stationary time series data.
Simulation of Thermal Barrier Layer Effects on Cu/PI Structures for Flexible Film Antenna Fabrication
This study investigates the influence of a thermal barrier layer on the laser ablation behavior of a Cu/Ti/PI trilayer structure using finite element simulation in COMSOL Multiphysics. A 2D model was established to analyze the temperature distribution and material removal under nanosecond pulsed laser irradiation. The effects of single-pulse energy, thermal barrier layer thickness, pulse width, and scanning speed were systematically evaluated. Results indicate that a Ti thermal barrier layer with a thickness of 200–300 nm effectively delays the vaporization of the PI substrate while maintaining adequate copper removal. Optimal processing parameters were identified as a single-pulse energy of 450 μJ, pulse width of 120 ns, and scanning speed of 250 mm/s, which achieved complete copper ablation with minimal substrate damage.
Modeling of Urban Road Vehicle Exhaust Emission Laws and Comprehensive Analysis Under Accident Scenarios
With the rapid growth of motor vehicle ownership, vehicle exhaust has become a major source of urban air pollution. This study constructs a mathematical modeling system for carbon monoxide (CO) and hydrocarbon (HC) emissions based on real road observation data, focusing on speed-emission relationships, optimal driving speed, driving behavior impacts, and accident-induced idling emissions. Results show the "emission rate-speed" model outperforms "mileage emission-speed" (CO emission rate R²≈0.994); HC’s optimal speed is ~25 mile/h (practical), while CO’s optimal speed exceeds real limits; variable-speed driving increases instantaneous emission fluctuations; 200 vehicles idling 30 minutes emit ~1754g CO and ~189g HC. This study provides a scientific basis for traffic emission management.
Grating structures are fabricated inside glass based on femtosecond laser parallel processing technology
Femtosecond laser single-focus scanning has the problem of low efficiency when manufacturing large-area structures. This paper proposes a multi-focus parallel processing method based on the weighted Gerchberg-Saxton (GS) algorithm. A multi-focus hologram was designed and loaded onto a spatial light modulator to achieve uniform energy distribution. The effects of repetition frequency, processing times and scanning speed on the morphology of microchannels were systematically studied, and the uniformity was all above 90%. Using the optimized process parameters, a grating structure with a cross-sectional width of approximately 29.315 μm was fabricated inside the glass by the three-focal parallel process. Compared with single-focus scanning, this method triples the efficiency while maintaining high quality, providing a promising path for the manufacturing of scalable photonic devices.
Tribological properties of CF/PEEK paired with different metals
Under the condition of impurity water lubrication, the tribological properties of three friction pairs consisting of 45#, nickel-plated steel, 304L, and carbon fiber-reinforced PEEK composite (CF/PEEK) were investigated. Ball-on-block reciprocating tests were conducted using a self-made friction and wear tester. In combination with surface morphology characterization, the friction and wear mechanisms of the materials were deeply analyzed. The research results showed that with changes in load and rotational speed, the friction coefficient and wear volume of CF/PEEK-45# were consistently lower than those of CF/PEEK-NP and CF/PEEK-304L. Under the condition of impurity water lubrication, the compatibility of CF/PEEK with the three materials was ranked as follows: CF/PEEK-45# > CF/PEEK-NP > CF/PEEK-304L. This study provides key data support and theoretical guidance for the practical application of CF/PEEK composite materials in motion pair components such as water-lubricated bearings and guide rails.
Experimental Study on Dynamic Modulation Op-timization of Micro-Hole Morphology in Wa-ter-Assisted Laser Drilling
IN718 alloy, a typical nickel-based superalloy, is widely used in key components such as turbine blades and combustion chambers in aircraft engines due to its excellent high-temperature strength, creep resistance, and corrosion resistance. However, the material's high-temperature strength, poor thermal conductivity, and low plasticity lead to low efficiency in traditional machining and severe tool wear. Laser processing, with its high energy density, non-contact nature, and flexibility, is considered an effective method for machining micro-holes in high-temperature alloys. However, laser micro-hole processing in air still faces problems such as large hole expansion, rough hole walls, a large heat-affected zone, and thick oxide layers. To address these issues, this paper introduces a water-assisted environment and combines pulse delay technology to explore its effects and mechanisms in the micro-hole processing of IN718 alloy.
Synergistic laser drilling, pulse-delay modulation
Study on micro-hole morphology of Ni-based superalloy optimized by pulse-delay dynamic modulation of nanosecond laser
Ni-based superalloys are widely used in aerospace for their excellent high-temperature properties.. To address thermal damage and dimensional ac-curacy issues faced by IN718 in high-power nanosecond laser micro-drilling, this study proposes a pulse-time modulation strategy. By dynamically control-ling heat accumulation through precise pulse-delay adjustments, a segmented energy delivery mechanism enhances through-hole quality. Two pulse-delay op-timization strategies are proposed and verified in comparison with the conven-tional processing: the optimized hole size accuracy is improved by 70%, the av-erage roughness of the hole wall is reduced by 30%, and the taper of the through-hole is reduced by 54%.
From Grid-Following to Grid-Forming: A Review of Converter Technology Paradigm Shift and Its Applications in Renewable-Rich Power Systems
With the global penetration of renewable energy exceeding 30%, power systems are increasingly challenged by inertia deficiency and weak grid stability, exposing the limitations of conventional grid-following inverters (GFLI) which rely on external synchronization and may provoke sub-synchronous oscillations. This review analyzes the technological transition toward grid-forming inverters (GFMI)—capable of autonomously establishing and regulating voltage and frequency—and assesses their application in renewable energy systems. Through bibliometric analysis, case studies from major Chinese renewable bases, and hardware-in-the-loop simulations under weak-grid conditions (SCR as low as 1.0), we identify significant advancements in wide-bandgap semiconductor topologies, adaptive control strategies such as virtual synchronous generator (VSG) and self-synchronizing control (SSC), impedance reshaping, and fault ride-through capability. Real-world implementations demonstrate markedly improved performance: inertia response time reduced to 0.48s, suppression of sub-synchronous oscillations by 89%, reactive response within 25ms, and successful black-start within 180 seconds, alongside notable economic benefits including reduced wind curtailment and lower equipment costs. Nevertheless, widespread adoption faces standardization gaps particularly in thermal management of high-frequency devices, multi-timescale stability certification, and market mechanisms for ancillary services. We conclude that GFMI represents a foundational technology for future high-renewable power systems, though its full deployment requires integrated progress in adaptive intelligence, international standard alignment, and policy-supported market frameworks.
Evaluation and Suggestions on Logistics Level of Yangtze River Delta Urban Agglomeration Based on Factor Analysis and Cluster Analysis
As the "neural network" of economic integration, the development level of the logistics system determines the efficiency of cross-regional flow and allocation of factor resources. Currently, although the economic aggregate of the Yangtze River Delta region accounts for nearly one-fourth of the national total, the imbalance in logistics development has become a key bottleneck restricting in-depth regional collaboration. To comprehensively evaluate the logistics development level of the Yangtze River Delta urban agglomeration, this paper constructs an evaluation system for regional logistics level of the Yangtze River Delta urban agglomeration by selecting 9 indicators from 4 dimensions. The comprehensive scores of each city are calculated through factor analysis. The results show that Shanghai, Wuxi, and Nanjing have the highest logistics development level, followed by Jiaxing, Ningbo, and Nantong. Based on the scores of each common factor and the comprehensive scores obtained from the above factor analysis, a cluster analysis can be conducted on the regional logistics level of the cities in the Yangtze River Delta. According to logistics scale, openness, logistics network density, and industrial contribution, the 14 cities are divided into three categories. By analyzing and identifying the advantages and disadvantages of urban logistics development, revealing the spatial differentiation law of logistics development in the Yangtze River Delta, and proposing differentiated countermeasures, this paper puts forward policy suggestions for promoting the integration of the Yangtze River Delta region.
Wear Prediction for Helical Gears Used in EMU
By combining Hertz contact theory with the Archard wear formula, a wear model for the tooth surface of helical cylindrical gears used in high-speed trains was developed, and its wear characteristics were numerically simulate.The simulation findings show that, under ideal operating conditions, the tooth sur-face of the helical gear pair wears non-uniformly along the tooth width direc-tion, with the least wear near the pitch circle and the most wear at the root of the smaller gear. Based on this, the effects of various modules, tooth width, he-lix angle, and input torque on tooth surface wear are investigated further. The analysis shows that changes in module, input torque, and tooth width all have a considerable impact on the distribution of tooth surface wear, although varia-tions in helix angle have a modest impact.
A Comparative Study on the Development of Engineering Ethics in China and the West and Strategies for Enhancement
Engineering ethics plays an increasingly important role in modern society, not only influencing the quality and safety of engineering practices but also relating to social development and human well-being. This paper compares the development of engineering ethics in China and the West, sorts out the similarities and differences between the two, analyzes the differences in engineering ethics concepts, practices, systems, and education, and on this basis, puts forward relevant suggestions for improving engineering ethics practices to promote the further development of engineering ethics on a global scale.
Integrating ResNet-50 and Vision Transformer Architectures for Robust and Efficient Tomato Fruit Ripeness Classification
This article introduces a new hybrid deep learning framework that combines ResNet-50 and Vision Transformer (ViT) models to classify tomato fruits by ripeness and quality. The hybrid model takes advantage of the ResNet-50's capability to extract features in local spatial regions. Additionally, it combines ResNet-50 with ViT's ability to establish a global contextual relationship between elements with self-attention. The framework overcomes the limitations of utilizing a single model. The framework was trained and evaluated, using a balanced and diverse dataset. It consists of four tomato classes, ripe, unripe, unevenly ripened, and damaged, collected under controlled conditions in the field. The experiments conducted aggressively in the study demonstrated a high classification accuracy of 98% with a hybrid framework compared to individual ResNet-50 and ViT models. The results in addition to the general performance were validated further using precision, recall, the area under the curve (AUC) and the F1-score. The study also included a computational efficiency test to look for accuracy versus multiple time and spatial resource costs. The research successfully addresses the challenges with dataset diversity, computational costs, and real-time feature deployments through the conceptualization of strategies (data augmentation, transfer learning) to improve generalization. The hybrid framework is expected to provide similar design principles and measures for use with other agricultural products. It can help in real-time sorting applications in variable real-world scenarios in agricultural operations. The research provides a pathway for developing smarter, scalable, and sustainable solutions in food quality and safety systems within precision agriculture. Future work will incorporate deployment and optimize the framework for edge devices in a real-world variable farm environment.
Research on the Amplitude-Frequency Characteristics of the Lateral Vibration of the BTA Deep Hole Machining System
The lateral vibration of the boring bar in the precision BTA deep-hole boring pro-cess cannot be ignored. This chapter mainly studies a type of vibration situation in which the boring bar has lateral vibration caused by the elastic bending defor-mation of the tool when the tool system rotates and cuts into the workpiece during the boring process. Analyze the BTA deep-hole boring machining system, use the multiple scale method to obtain the amplitude-frequency response equation of the vibration model; use numerical simulation techniques to obtain phase diagrams and amplitude-frequency diagrams to analyze the amplitude-frequency character-istics, and obtain the dynamic behavior of the BTA deep-hole boring system con-sidering the elastic deformation of the tool under the influence of fluid with the change of various parameters, revealing the evolution mechanism of the nonlinear dynamic behavior of the BTA deep-hole machining system with the change of process parameters.
Similar simulation study on overlying strata movement law of high stress and large mining length working face
Aiming at the problem of overlying strata movement law of large mining length working face under high stress environment in deep mining, this paper system-atically studies the movement law and failure form of overlying strata on large mining length working face by combining physical similarity simulation with three-dimensional photogrammetry. The results show that after the completion of the whole excavation process, the cumulative maximum subsidence of the surface reaches 5.4mm, the maximum subsidence value appears at 73cm from the left side of the model frame, the minimum subsidence value of the surface is 4.7mm, and the minimum subsidence value appears at 50mm from the left side of the model frame. In the process of advancing the whole working face, the maximum roof subsidence reached 15.9mm, the maximum roof subsidence ap-peared at 75cm from the left boundary of the model frame, the minimum roof subsidence was 5.4mm, and the minimum roof subsidence appeared at 44cm from the left boundary of the model frame.
Energy Efficiency Study of Waste Heat Recovery-Based Chemical Absorption Decarbonization and Liquefaction for Natural Gas
This study presents an integrated approach for biogas purification and liquefaction, combining methyldiethanolamine (MDEA)-piperazine (PZ) solvent absorption with waste heat recovery from gas turbine exhaust. Through Aspen HYSYS simulation of a high-CO₂ (10%) feed gas system, we demonstrate a cascading energy utilization scheme that achieves both effective decarbonization (reducing CO₂ to <0.5%) and enhanced energy efficiency. Key findings reveal optimal absorption pressure at 1.5 MPa, where specific power consumption reaches 0.584 kW·h/kg LNG. The waste heat recovery system delivers 84.9% utilization efficiency, fully meeting the regeneration heat demand (828.99 kJ/m³) without external energy input. This configuration shows superior performance compared to conventional biogas treatment methods, particularly for CO₂-rich (≥10%) feedstocks, offering significant energy savings for LNG production facilities.
Effect of Fiber Orientation on Grinding Characteristics and Removal Mechanisms of Cf/SiC
Carbon fiber-reinforced silicon carbide ceramic matrix composites (Cf/SiC) ex-hibit advantages such as high-temperature resistance, low density, and high spe-cific strength, making them widely applicable in aerospace, new energy vehicles, and other fields. However, their heterogeneous nature and hard-brittle fracture mechanisms lead to poor machined surface quality. To clarify the influence of fiber orientation on grinding characteristics and material removal mechanisms, single-grit scratching experiments were conducted, and a finite element model (FEM) of Cf/SiC composites was established. Two machining directions, MA (transverse direction) and MB (longitudinal direction), were defined based on fiber orientation, and the effects on normal force (Fn) and surface morphology were investigated. A finite element numerical model of Cf/SiC composites was developed, incorporating the mechanical properties of fibers, matrix, and inter-faces. Simulation results revealed that Fn increases with cutting depth, and the average Fn magnitude followed the order MB > MA. Cracks propagated along the fiber axis: transverse fibers were removed via wear, fracture, and peeling, with some fibers exposed due to brittle matrix fracture; longitudinal fibers were re-moved via wear and debonding, primarily influenced by the contact area be-tween fibers and abrasive grits.
A Review on the Anti-Aging Properties of Modified Asphalt
Asphalt pavement is widely used in road engineering due to its advantages such as short construction cycles and excellent road performance. However, asphalt is prone to brittleness and hardening during production and service under the com-bined effects of heat, oxygen, and ultraviolet (UV) radiation, leading to degraded pavement performance. To address this, modifiers are commonly incorporated to enhance the anti-aging properties of asphalt.
Preparation and physicochemical characterization of cracking oil from waste plastics
The aim of this paper is to systematically explore the preparation process of waste plastic cracking oil from municipal solid waste and its physicochemical properties, and to study the composition and properties of waste plastic crack-ing oil in depth through characterization techniques such as organic elemental analysis (EA), capillary viscometer, flash point and ignition temperature test, so as to provide theoretical basis and technical support for the resource utilization of low value waste plastics in municipal solid waste.
Detection method of tubing coupling based on magnetic field induction
The inspection of tubing coupling is a key part to ensure the safety of snub-bing operation, To realize the accurate identification of tubing and coupling, Studied a magnetic detection method, Accurate identification of coupling by capturing the difference in magnetic field strength between the surface of the oil pipe and the coupling, This paper uses COMSOL software to establish a finite element simulation model of the pipe coupling detection device. Through simu-lation calculations, the effects of the spacing between magnetic materials, per-manent magnet strength, pipe eccentricity, and coupling defects on the detec-tion performance are studied. The influence patterns of different factors on the detection results are obtained, and finally, experiments are conducted to verify the feasibility of the method.
Study on the optimization of boom performance and oil interception strategy in the emergency response to oil spill in rapids-type rivers
This paper investigates the performance optimisation and oil interception strategies of booms in emergency response to oil spills in rapids-type rivers. The limitations and failure mechanisms of traditional booms under torrent conditions are analysed, including oil film submergence and structural failure. The structural optimisation direction of the new type of boom is proposed to improve the anti-slumping and interception efficiency by adding gradient counterweights and integrating anti-splash components. Combined with the example of Lancang River, a scientific and reasonable selection of oil spill interception points, boom deployment angles and methods, and multi-stage deployment strategies are proposed. The research results are of practical significance in guiding the selection, deployment and optimisation of oil containment booms in the emergency response to oil spills in rapids-type rivers.
Research on Corrosion Rate Prediction Model for Long-Distance Pipelines Based on Gravimetric-CART Fusion
As a classical method for corrosion rate determination, the gravimetric method provides fundamental data for corrosion analysis by measuring material mass loss. However, its reliance on laboratory environments and inability to perform real-time prediction limit its engineering applications. This paper proposes a corrosion rate prediction model based on the CART (Classification and Regression Tree) algorithm, which integrates historical gravimetric data with multi-source environmental parameters to achieve dynamic corrosion rate prediction under complex working conditions. Experiments show that the CART model significantly outperforms the traditional gravimetric method in prediction accuracy (MAE=0.05 mm/a) and variable interaction analysis capability (MAE=0.12 mm/a). The study reveals how data-driven methods inherit and extend the core value of the traditional gravimetric method, providing an efficient and low-cost solution for corrosion management in long-distance pipelines.
Study on failure mode of oil interception fence in turbulent river
To reduce environmental pollution caused by oil spills in turbulent river conditions, the key to researching oil spill recovery technology for turbulent rivers is to identify the critical factors leading to the failure of oil booms. This paper establishes a two-dimensional three-phase flow model based on FLUENT technology to simulate the transient changes and distribution patterns of oil and water under the influence of oil booms; it uses ANSYS to build a mechanical model of the oil boom, calculating the deformation degree and oil interception efficiency under turbulent conditions, and investigates areas with higher probabilities of structural failure. Additionally, based on similarity principles, a laboratory small-scale oil spill tank experiment is established to provide a basis for optimizing new oil boom structures and deployment methods.
Robot precise positioning and interconnection system based on machine vision
With the continuous advancement of intelligent manufacturing in our country, factories have increasingly higher requirements for intelligent devices. Traditional production technologies can no longer meet the growing needs of enterprises. In order to improve the quality of product production, adapt to complex production scenarios, and achieve intelligent interconnection and flexible production, enterprises combine equipment IoT (Internet of Things) and visual systems to conduct real-time detection and positioning of products. They utilize industrial robots with high precision for rapid sorting, thereby replacing manual labor to complete high-intensity and high-precision work. The integration of this technology is an important driving force for the construction of digital factories and the development of intelligent manufacturing. This article introduces EPSON robots and machine vision systems, and develops a monitoring software APP, realizing the application of an interconnected system for precise positioning and grasping.
Review on the Development of Lithium-Ion Batteries Electrolytes
This paper systematically reviews the technological evolution of Lithium-Ion Batteries battery (LIB) electrolytes, from traditional liquid systems to innova-tive solid/gel composite systems, with a focus on advances in high-voltage, wide-temperature-range, and flame-retardant electrolytes. By analyzing the synergistic optimization mechanisms of solvents, lithium salts, and additives, the study elucidates the critical role of electrolytes in determining battery en-ergy density, safety, and environmental adaptability. Integrating recent break-throughs in solid-state electrolytes (e.g., sulfide/oxide electrolytes) and green manufacturing trends, it outlines key challenges and future directions for elec-trolytes in pairing with advanced materials such as high-nickel cathodes and silicon-based anodes, providing theoretical support for next-generation LIB de-sign.
Research progress on lithium dendrite growth
This article reviews the progress in the growth mechanisms, suppression strate-gies, and monitoring techniques of lithium dendrites. The high - capacity poten-tial of lithium - metal anodes is restricted by cycle decay, short - circuits, and thermal runaway caused by dendrites. Their growth originates from electro-chemical imbalance and interfacial heterogeneity. Multi - physics models reveal the regulation rules of pore geometry and crystal orientation on morphology evolution. Suppression strategies center on constructing high - conductivity LiF interfaces, designing 3D porous anodes, and pressure control. In - situ charac-terization technologies (such as X - ray tomography and fiber - optic sensing) realize dynamic monitoring of the coupled mechanisms of dendrite growth and SEI reconstruction. Current challenges involve cross - scale model verification and real - time monitoring technology development. In the future, it's necessary to combine machine learning with intelligent sensor networks to promote the practical application of high - safety lithium - metal batteries.
Advances in the Synthesis and Applications of α-Hydroxy Phosphates
α-Hydroxy phosphates are a class of organophosphorus compounds characterized by the presence of a hydroxyl group adjacent to the phosphate moiety. These compounds exhibit significant biological and chemical properties, making them valuable in pharmaceuticals, catalysis, and materials science. Due to their multifunctional nature, α-HPs have been extensively studied for their potential roles in metabolic pathways, enzymatic reactions, and as intermediates in drug design. This review summarizes recent advances in the synthesis, reactivity, and applications of α-HPs, highlighting innovative methodologies, green synthetic approaches, and emerging applications in various fields.