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[Objective] In semiconductor manufacturing, the remaining useful life(RUL) of equipment must be accurately predicted to ensure production efficiency and minimize economic losses. However, this task is fraught with substantial challenges. The heterogeneity of multisource sensor data, which encompass various signal types and measurement scales, poses a complex data integration problem. Meanwhile, the scarcity of key failure samples makes it arduous to train reliable prediction models. Traditional prediction methods, which are based on single-dimensional modeling, struggle to capture the intricate physical coupling relationships among different components of the equipment. Moreover, they cannot adequately reproduce the evolution laws of cross-temporal and spatial states during equipment degradation. Hence, these methods have limited prediction accuracy, lack interpretability, and cannot meet the high demands of modern semiconductor manufacturing processes. This study addresses these issues by developing a spatiotemporal joint modeling method that integrates a temporal convolutional network(TCN) with a graph convolutional network(GCN). The joint modeling aims to achieve an in-depth multiscale analysis of the dynamic degradation laws of equipment. [Methods] First, a learnable GCN is constructed. Based on the physical topology of sensors installed on the equipment, the GCN is designed to model the spatial relationships among different sensor nodes. Through a multi-order neighborhood information aggregation mechanism, the GCN effectively extracts the hierarchical spatial correlation features of the equipment. This process allows the model to understand the interactions among different components and their influences on each other in the spatial domain. Next, the TCN with a hierarchical dilated convolution architecture plays a vital role in handling time-series data. The dilated convolution layers capture the long-term trend features of equipment degradation without sacrificing the ability to detect local fluctuation patterns. By employing stacks of multiple layers with different dilation rates, the TCN analyzes the time-series data at various time scales to reveal hidden patterns and trends in equipment degradation. Finally, a spatiotemporal interaction module deeply fuses the spatial and temporal feature data extracted by the GCN and TCN. This integration enables the model to comprehensively examine the spatial and temporal aspects of the state of the equipment, facilitating accurate regression prediction of the RUL. [Results] The performance of the proposed model was evaluated via rigorous experiments on the dataset of an industrial ion etching system. The results demonstrated that the proposed model performed remarkably better than traditional models. Specifically, in terms of the root mean square error(RMSE) prediction index, the proposed model achieved a reduction of 20.6% compared to the long short-term memory(LSTM) model. The LSTM model, which is widely used in time-series prediction, often struggles in capturing complex spatial relationships. In contrast, the proposed model integrating the GCN effectively overcomes this limitation. Compared to that of a convolutional neural network(CNN), the RMSE of the proposed model was lower by 23.3%. CNNs are mainly designed for extracting spatial features in images, and their application in equipment RUL prediction without considering the unique spatiotemporal characteristics of equipment data leads to suboptimal performance. The specialized spatiotemporal architecture of the proposed model provides a more suitable solution. Compared to that of the multilayer perceptron(MLP), the RMSE of the proposed model was lower by 29.9%. The simple fully connected structure of the MLP is incapable of effectively modeling spatial and temporal dependencies, highlighting the advantage of the architecture of the proposed model. Additionally, when compared with those of the classical TCN and the Transformer network, the RMSE of the proposed model was lower by 11.0% and 13.7%, respectively, further validating the effectiveness of the joint spatiotemporal modeling approach. [Conclusions] This study not only provides an innovative and effective method for the predictive maintenance of industrial equipment but also has far-reaching implications for the semiconductor manufacturing industry. Enabling highly accurate RUL predictions, it helps enterprises optimize their maintenance strategies. Such optimization can substantially reduce unplanned downtime losses during wafer manufacturing, improving production efficiency and reducing costs. Furthermore, it paves the way for shifting the paradigm of intelligent manufacturing from a reactive post-failure response approach to a proactive pre-failure intervention strategy. This transition can enhance the overall reliability and competitiveness of the semiconductor manufacturing industry, driving it toward a more intelligent and sustainable future.
[Objective] With the ongoing advancement of emerging new engineering disciplines, undergraduate experimental teaching is progressively transitioning from the traditional model of “verification-oriented knowledge transmission” to a new paradigm emphasizing “comprehensive, innovative, and interdisciplinary capability development.” To address the demands of engineering education reform and cultivate high-quality engineering talent with interdisciplinary integration capabilities, this study designed and implemented a comprehensive experimental teaching project titled “Preparation of ZIF-67/Zn0.1Cd0.9S composite materials and their photocatalytic degradation of tetracycline hydrochloride(TC–HCl).” The experiment was structured around a complete practical process of “material synthesis—characterization analysis—performance investigation,” aiming to develop students' interdisciplinary knowledge integration and innovative thinking skills, in alignment with the talent development goals of emerging engineering education. [Methods] An S-scheme ZIF-67/Zn0.1Cd0.9S heterojunction photocatalyst was successfully fabricated via an in situ hydrothermal synthesis method. The regulatory mechanisms of solution pH on crystal growth kinetics and of the heterointerface structure on photocatalytic performance were systematically investigated. The phase composition, structural properties, and specific surface area of the composite materials were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, and N2 adsorption–desorption. Morphology was observed using scanning electron microscopy, while optical absorption characteristics and band structures were analyzed using ultraviolet–visible diffuse reflectance spectroscopy and electrochemical methods. [Results] Experimental results indicated that ZIF-67 exhibited a smooth, leaf-like morphology, whereas pure-phase Zn0.1Cd0.9S formed a microsphere structure via nanoparticle self-assembly. In the ZIF-67/Zn0.1Cd0.9S-7 composite material prepared at pH = 7, Zn0.1Cd0.9S nanoparticles were uniformly coated on the surface of ZIF-67 nanosheets, with the original microsphere structure of the pure phase disappearing completely. The mesoporous composite exhibited a broader spectral response range and a 59.8% increase in specific surface area compared with pure-phase Zn0.1Cd0.9S. Its photocatalytic degradation rate constant for TC–HCl(0.035 min~–1) was 4.07 times and 2.97 times higher than those of samples synthesized at p H = 5(ZIF-67/Zn0.1Cd0.9S-5) and p H = 9(ZIF-67/Zn0.1Cd0.9S-9), respectively. Radical trapping experiments and photoelectrochemical measurements revealed that the photocatalytic process primarily followed an S-scheme heterojunction mechanism. [Conclusions] This teaching project consisted of three interconnected teaching modules: experimental design and scheme optimization, material synthesis and characterization, and photocatalytic performance and mechanism investigation. By integrating multiple material characterization and photoelectrochemical testing methods, the experiment systematically illustrated the influence of synthesis conditions on material structure, optical properties, and photocatalytic activity, establishing a comprehensive teaching system covering the entire process from “scheme design—material preparation—performance characterization—mechanism investigation.” Teaching practice demonstrated that this experiment helped students master composite material synthesis and characterization techniques. More than 90% of the students independently completed material characterization and data analysis, and more than 80% could construct a systematic analytical framework for the photocatalytic reaction mechanism, thereby considerably enhancing their innovative thinking and complex engineering problem-solving skills. The comprehensive experiment developed in this study epitomizes the new philosophy of engineering talent cultivation: “solid foundation, strong interdisciplinary integration, and practice-oriented education.” By deeply integrating research content with experimental teaching, this work provides a valuable reference for advancing the development of high-quality experimental curricula that integrate research into teaching.
[Objective] A critical issue with red shale roadways has been identified in Guizhou phosphate mines. Over 70% of roadways pass through red shale, with a 34% roof collapse rate and 5 200 yuan/m support cost. The deep red shale surrounding rock is particularly susceptible to high ground stress and dynamic disturbances(excavation, blasting), which can worsen its instability. Existing studies confirm the bedding effect of red shale, however, its correlation with the angle-fracture mode remains ambiguous. Conventional methodologies prove ineffective in capturing real-time microfractures, and the application of the acoustic emission(AE) rise angle–average frequency(RA–AF) criterion remains underdeveloped. This study explores the tensile properties, fracture mechanisms, and AE characteristics of red shale under different bedding angles to optimize roadway support. [Methods] Red shale from Guizhou phosphate mines was processed into specimens measuring Φ50 mm × 30 mm and characterized by 0°, 30°, 45°, 60°, and 90° bedding angles. The specimens were dried at 115 ℃ for 24 h and subsequently sealed to avoid moisture. An MTS81 electro-hydraulic servo machine with a loading rate of 0.1 mm/min and a PAC PCI-2 AE system were utilized in the experimental setup. Two AE probes(AB-glued) were utilized to monitor signals, with key parameters including ringing count, energy, RA, and AF. The RA–AF criterion(RA = 3 μs·d B–1 threshold) was employed to distinguish between tensile and shear fractures, with stress–AE curve coupling analyzed. [Results] The tensile strength of red shale decreased significantly as the bedding angle increased, dropping from 6.20 MPa at 0° to 3.71 MPa at 30°, 3.43 MPa at 45°, 3.30 MPa at 60°, and finally to 3.17 MPa at 90°, representing a total reduction of 48.9%. The fracture mode of red shale also showed a clear transformation law. Specimens with 0° and 90° bedding angles(RA values of 1.2 μs·d B–1 and 2.1 μs·d B–1, respectively) exhibited primarily tensile failure. Specimens with 30°–60° bedding angles(RA values ranging from 18.5 μs·d B–1 to 32.7 μs·d B–1) exhibited an increasing proportion of shear failure, with 45° identified as the critical bedding angle at which shear failure accounted for 75%. The AE signals of red shale exhibited three stages that were highly coupled with the stress curve: stable accumulation(linear stress increase and weak AE activity), sudden release(peak stress and sharp surge of AE signals), and dissipation(low stress level and stable AE signals). Furthermore, specimens with 30°–60° bedding angles demonstrated nonlinear stress fluctuations before reaching the peak stress, accompanied by multi-peak variations in AE signals. Additionally, an RA threshold of >8 μs·d B–1 was proposed to predict shear slip 20 min in advance. Industrial tests showed that this scheme reduced the roof collapse rate of roadways to 9% and decreased the per-meter support cost by 1 200–1 800 yuan. [Conclusions] The bedding angle exerts a predominant influence on the anisotropy and fracture mode of red shale. The three-stage AE and RA–AF criterion effectively characterizes failure. A differentiated support scheme is proposed: roadways at 0° are reinforced with Φ22 mm high-strength anchor cables(≥150 kN); those at 30°–60° are anchored with epoxy grouting(≥5 MPa) + U-steel(45° with 0.8 m grouting spacing and AE monitoring); and those at 90° are reinforced with anchor cables + 120 mm C25 shotcrete. This approach is instrumental in ensuring the safety and efficiency of mining operations in Guizhou phosphate mines.
[Significance] Autonomous vehicles have been widely applied in transportation, logistics, and other fields due to their efficiency, convenience, and intelligence. These vehicles usually rely on a variety of heterogeneous sensors to measure environmental information and then execute real-time decisions accordingly. However, owing to the complexity of multi-sensor system structures and factors such as component aging, vehicle sensors are inevitably prone to faults. Minor faults can cause state monitoring errors, distorting environmental perception information and interfering with the decision-making of intelligent vehicle control systems. Sensor fault diagnosis technology is a key approach to ensuring the safety and reliability of autonomous vehicles, and related research has made significant progress. Nevertheless, this field still faces various challenges regarding practical feasibility and improving diagnostic performance. Therefore, summarizing existing research results, reasonably selecting fault diagnosis schemes, and anticipating future development directions are of great significance. [Progress] Diagnostic methods can be divided into knowledge-driven and data-driven approaches according to whether they rely on prior models and domain knowledge. Knowledge-driven methods comprehensively utilize system models, expert experience, or fuzzy logic to detect and locate faults. Among these, model-based methods use analytical models to design robust residual generators and construct evaluation criteria to achieve fault diagnosis. These approaches are characterized by clear physical meaning and strong interpretability but are difficult to apply to vehicles lacking accurate models. Diagnostic approaches based on expert systems and fuzzy logic transform domain knowledge into diagnostic logic to extract correlated fault information from multiple sensor sources; however, their performance is limited when the rule base is incomplete. Data-driven fault diagnosis does not require explicit models or prior knowledge; instead, it directly mines potential patterns and correlated features from historical data to construct diagnostic systems. Cluster analysis–based methods rely on learning mechanisms to analyze data distribution characteristics and can classify fault modes without requiring large numbers of labeled samples; however, their performance degrades when the number of clusters is improperly selected or when data quality is poor. Support vector machine–based methods achieve fault classification and recognition by constructing a maximum-margin hyperplane but are sensitive to kernel function selection, class imbalance, and complex hyperparameter tuning. Random forest–based methods construct multiple decision trees and perform voting, offering strong classification performance for nonlinear data but exhibiting high model complexity and limited interpretability. Deep learning–based diagnostic methods perform unsupervised pretraining through multiple hidden layers, enabling automatic extraction of fault features from raw data to higher-level representations. These approaches demonstrate excellent performance in handling high-dimensional, nonstationary, and heterogeneous multisource data; However, they face challenges such as scarce fault samples, poor interpretability, and high computational complexity. In addition, an evaluation framework for fault diagnosis methods is integrated, providing a quantitative basis for selecting and optimizing diagnostic techniques. [Conclusions and Prospects] Sensor fault diagnosis techniques have been widely applied in autonomous vehicles to enhance system safety and reliability. Future research directions include hybrid diagnostic systems driven by both knowledge and data, improvements in the generalization and interpretability of data-driven methods, optimization of edge intelligence deployment, and exploration of meta-learning and modular design.
[Objective] Compressible Aerodynamics is a core course in the undergraduate curriculum for Aircraft Design and Engineering. The course focuses on aerodynamic phenomena and governing principles under high-speed flow conditions and serves as a critical foundation for the education and training of future aircraft designers. However, instruction in compressible aerodynamics currently suffers from a severe shortage of experimental components, which limits students' ability to connect theoretical concepts with physical flow behavior. To address this deficiency, this study develops an experimental teaching platform for compressible aerodynamics based on a newly constructed supersonic Ludwieg tube tunnel. By systematically integrating theoretical instruction with hands-on experimentation, the platform establishes a solid experimental foundation for cultivating high-level talent in Aircraft Design and Engineering. [Methods] Using Mach number measurement in a hypersonic wind tunnel as a representative example, this paper presents the fundamental operating principles of the Ludwieg tube and the theoretical basis and formula derivations for determining the incoming-flow Mach number using Pitot probes. A dedicated hypersonic wind tunnel experiment was designed to guide students through the measurement process, enabling them to develop a deeper understanding of normal shock wave theory and the correct application of isentropic relations in compressible aerodynamics through experimental design and practice. [Results] High-speed schlieren visualization techniques were employed to observe the formation and evolution of detached shock waves ahead of Pitot probes, thereby rendering otherwise invisible aerodynamic phenomena directly observable. This visualization intuitively demonstrates the complete process of flow establishment within a hypersonic wind tunnel and significantly enhances students' conceptual understanding of key topics in compressible aerodynamics. Using Pitot probes in combination with pressure sensors, total pressure measurements were obtained upstream and downstream of shock waves at different incoming flow Reynolds numbers. Based on the Pitot–Rayleigh relationship derived from normal shock theory, the free-stream Mach number distribution in the test section was calculated for each case. The experimental results indicate that increasing the incoming flow Reynolds number leads to a thinner boundary layer at the nozzle exit, an increased effective area ratio between the nozzle exit and throat, and consequently a higher Mach number in the wind tunnel test section. [Conclusions] The Ludwieg tube hypersonic wind tunnel experimental teaching platform has been successfully implemented in undergraduate education at our institution and has since been widely used in both undergraduate and graduate experimental teaching. By overcoming the inherent limitations of conventional hypersonic wind tunnels—namely, prohibitive construction costs, high operational expenses, and limited accessibility—this platform provides a practical model for experimental instruction in compressible aerodynamics and offers a viable approach for training students in hypersonic experimental aerodynamics in China.
[Objective] This study undertakes a comprehensive investigation of the phase modulation properties of subwavelength dielectric gratings(SWDGs), emphasizing the role of key structural parameters—namely, grating period, duty cycle, substrate refractive index, and grating height—in determining the resulting phase retardation. This research aims to demonstrate that SWDGs function as highly tunable optical elements that enable precise two-dimensional light-field manipulation, thereby offering distinct advantages over conventional birefringent waveplates in terms of angular stability, broadband performance, and integration flexibility. Ultimately, this study seeks to establish theoretical and experimental frameworks to support the implementation of SWDGs in advanced photonic platforms, including metasurfaces, polarization control devices, and integrated optical systems. [Methods] A dual approach was employed, integrating numerical simulations and experimental measurements. The finite element method(FEM) was employed in COMSOL Multiphysics to develop a rigorous electromagnetic model of the SWDG, enabling the simulation of phase retardation under variable structural parameters and incident conditions. The behavior of TE-and TM-polarized waves, their transmission efficiency, and the resulting phase difference were the specific focus of the simulations. In the experimental phase, a quartz-based subwavelength grating with a period of approximately 826 nm and a height of 1 280 nm was fabricated via ultraviolet nanoimprint lithography. A bespoke optical configuration was engineered to assess the phase retardation characteristics at 1 550 nm under normal incidence. The system employed Mueller matrix polarimetry, using a laser source, linear polarizers, and a rotating analyzer, in conjunction with a power meter. The resulting intensity profiles as a function of analyzer angle were recorded and fitted using MATLAB to extract the exact phase delay and optical axis orientation. [Results] The phase retardation is principally dictated by the grating height and the substrate's refractive index. An approximate linear relationship with height is observed, with a monotonic increase at higher refractive indices. The impact of variations in grating period on phase delay was found to be minimal, whereas the duty cycle showed a nonlinear effect, with an optimal value of approximately 0.4. The experimental measurements corroborated the numerical predictions, yielding a phase retardation of 0.44 rad, which closely matched the simulated value of 0.43 rad, resulting in a minor error of only 2.3%. The optical axis orientation was approximately 1.71 rad. The minor discrepancies observed between the simulation and the experiment were attributed to three factors: fabrication imperfections, slight misalignments in the optical path, and non-ideal polarization elements. The SWDG exhibited high transmission and consistent performance across a range of incident angles, thereby underscoring its robustness and suitability for practical applications. [Conclusions] This study successfully illustrates that SWDGs can be designed and fabricated to achieve tailored phase retardation. This offers a versatile and efficient alternative to conventional waveplates. The strong correlation between simulation and experimental results validates the use of FEM-based modeling for the design and optimization of SWDGs. Notable advantages of these gratings include broad angular acceptance, wavelength flexibility, and compatibility with standard nanofabrication processes. These characteristics render them highly promising for applications in metasurfaces, adaptive optics, optical sensing, and on-chip photonic systems. Subsequent research endeavors should explore dynamic and reconfigurable grating designs, as well as their integration with other functional optical elements to further expand their utility in next-generation optical technologies.
[Objective] The morphology of large-scale wildfire fronts is a crucial factor in assessing wildfire spread and determining safe distances for transmission corridors. Understanding the dynamic evolution of these fronts is essential for effective real-time wildfire monitoring and prevention. Most existing research focuses on small-scale experimental scenarios that struggle to replicate the complex interactions among multiple factors, such as fuel types and meteorological parameters, during actual wildfires. Few studies have examined fire front morphology in large-scale scenarios that incorporate meteorological conditions, and current research often falls short of the accuracy needed for effective wildfire prevention and control. This study aims to investigate the effects of different fuel types and meteorological conditions on wildfire front morphology through large-scale experiments, providing experimental data and theoretical support for the development of fire front spread models applicable to actual wildfire situations. [Methods] A 50 m×40 m full-scale wildfire combustion experimental platform was constructed for this study, integrating UAV thermal infrared imaging, tower-based visual monitoring, and multi-source meteorological sensing systems. We conducted large-scale, systematic experiments on wildfire spread using two common surface fuels(wheat straw and pine needle litter) under varying meteorological conditions. The analysis focused on the effects of fuel type, wind direction, and wind speed on key parameters, including fire front morphology, temporal variation in stable fire front length, and fire front propagation angle. We systematically compared surface fire-front spread under various working conditions. [Results] The results revealed the following: 1) Fire front morphology is significantly affected by fuel type, where wheat straw produces a smooth arc-shaped front, and pine needles result in a sharp, multi-branched, and irregular morphology. The fire front angle increases continuously during combustion, with the temperature decay rate in the burned area of pine needles being significantly faster than that of wheat straw. Wind direction dictates the overall spread direction of the front, whereas wind speed primarily affects the size of the front angle. 2) The variation trend and fluctuation amplitude of stable fire front length are jointly influenced by fuel and meteorological conditions. The fire front length of wheat straw decreases steadily over time, whereas that of pine needles exhibits significant short-term oscillations. Greater differences in maximum and minimum wind directions lead to more intense fluctuations in fire front length. Under identical wind directions, higher average wind speeds correspond to greater extreme values of fire front length. 3) The fire front propagation angle gradually decreases during the spread process. The wheat straw fire front is generally smooth with minor fluctuations, whereas the pine needle fire front displays significant local curvature and irregular trajectories. Greater stability in wind direction and higher average wind speeds result in a smaller average fire front propagation angle, causing the front to approach a straighter line. [Conclusions] Through large-scale surface fire spread experiments, this study elucidates the influence of fuel type and meteorological conditions on key parameters such as fire front morphology, temporal variation of stable fire front length, and fire front propagation angle. It reveals the comprehensive influence mechanism between meteorological conditions and fuel properties regarding fire front morphology, offering a large-scale experimental basis and critical parameter support for developing wildfire spread prediction models and improving wildfire prevention and control strategies in transmission corridors. Future research will expand these large-scale wildfire experiments to include more complex scenarios, thereby enhancing our understanding of real wildfire behavior.
[Objective] In the context of the “double carbon” goal, hydrogen energy has gradually been promoted as a renewable energy source with the advantages of being green, flexible, having good combustion performance, and possessing high energy density. Hydrogen-doped natural gas pipelines are an effective way to enable large-scale, long-distance, safe, and efficient hydrogen transportation. However, the unique physical properties of hydrogen may affect the performance of pipeline sealing materials and pose a threat to transportation safety. Hydrogen blending in natural gas pipelines may not only degrade the performance of non-metallic sealing materials but also increase pipeline leakage. Because hydrogen is easier to ignite and explode than natural gas, the risk of pipeline transportation is greatly increased. The purpose of this study is to explore the performance evolution of non-metallic sealing materials in a hydrogen environment through systematic experimental teaching, to provide a basis for the optimization design and safety standard formulation of pipeline sealing systems, and to cultivate students' engineering practice ability and scientific research literacy. [Methods] This study designs and constructs an experimental teaching system for evaluating the performance of non-metallic sealing materials in a hydrogen environment, enabling students to gain a deep understanding of the performance degradation behavior of non-metallic materials under hydrogen exposure. The experimental teaching system combines experimental research and numerical calculation. The experimental research component consists mainly of two parts: aging experiments and permeation experiments, which are used to study the aging behavior and permeation characteristics of non-metallic materials in a hydrogen-doped environment. During the experiments, students' participation in the entire process of instrument operation, data recording, and phenomenon analysis was emphasized. In combination with numerical simulation methods, a simulation model of permeation and sealing performance of non-metallic sealing materials in a hydrogen environment was established. [Results] The experimental results show that the volume swelling rates of ethylene propylene rubber and low-nitrile nitrile rubber after hydrogen-induced aging were less than 15%, and the mechanical property retention rates were greater than 85%, indicating the best anti-aging performance, while the performance of fluorine rubber was also favorable. In the permeation experiments, the permeability coefficients of tetrafluoro-propylene rubber and nitrile butadiene rubber were low, demonstrating excellent barrier properties. Among the eight non-metallic materials tested, fluorine rubber exhibited the best comprehensive performance in a hydrogen environment. [Conclusions] By constructing an experimental teaching system for evaluating the performance of non-metallic sealing materials in a hydrogen environment, this study clarified the aging and permeation behaviors of typical non-metallic materials under hydrogen exposure, providing an experimental basis for the selection and safety evaluation of sealing materials for hydrogen-doped natural gas pipelines. At the same time, the experimental system integrates frontier scientific research problems into practical teaching, effectively improving students' comprehensive abilities in high-pressure gas environment material testing, data modeling, and analysis for solving complex engineering problems, and provides an important teaching platform for cultivating engineering and technical talents to meet the needs of energy transformation.
[Objective] Efficient enrichment of phosphopeptides is crucial for phosphoproteomics research, enabling the identification of disease biomarkers and advancing early clinical diagnostics. Two-dimensional transition metal carbides(MXenes), particularly Ti_3C_2Tx, have attracted considerable attention for protein separation due to their large specific surface area and abundant surface functional groups. However, conventional methods for preparing TiO2/Ti_3C_2Tx composites suffer from weak interfacial bonding and insufficient control over the TiO2 crystal phase, both of which critically influence phosphopeptide enrichment performance. To address these limitations, this study developed a green and controllable solvent-engineering strategy for the in situ preparation of TiO2/Ti_3C_2Tx heterostructures with tailored crystal phases to achieve efficient phosphopeptide enrichment. [Methods] Few-layer Ti_3C_2Tx MXene was first synthesized from Ti3 AlC2 using the minimally intensive layer delamination method with LiF/HCl etching. Thereafter, TiO2/Ti_3C_2Tx heterostructures with controlled crystal phases were prepared via solvothermal treatment of the Ti_3C_2Tx precursor in different solvents(ethylene glycol, ethanol, and deionized water), denoted as TiO2/Ti_3C_2Tx-G, Ti O2/Ti_3C_2Tx-E, and TiO2/Ti_3C_2Tx-H, respectively. The resulting composites were systematically characterized using SEM, TEM, XRD, and XPS, and their phosphopeptide enrichment performance was evaluated using model proteins analyzed by MALDI–TOF mass spectrometry. [Results] SEM, TEM, and XRD characterizations revealed that TiO2/Ti_3C_2Tx-G maintained the MXene sheet structure with sparse, small anatase TiO2 nanoparticles; TiO2/Ti_3C_2Tx-E exhibited uniformly distributed anatase TiO2 nanoparticles with diameters of 10–30 nm; and TiO2/Ti_3C_2Tx-H possessed a unique bi-modal size distribution of TiO2 nanoparticles and polyhedra with a mixed phase of anatase and rutile. XPS analysis further demonstrated a progressive increase in Ti(Ⅳ) content from the ethylene glycol to the water system. These results confirmed the successful formation of TiO2/Ti_3C_2Tx heterostructures with solvent-dependent morphologies and crystal phases. The solvent-dependent properties of TiO2/Ti_3C_2Tx heterostructures were attributed to the different oxidation kinetics dictated by solvent oxidizability. In the enrichment experiments, direct analysis of the protein digest identified only a few phosphopeptides. Treatment with all Ti O2/Ti_3C_2Tx heterostructures significantly improved phosphopeptide enrichment and showed distinct performance. In particular, TiO2/Ti_3C_2Tx-G, Ti O2/Ti_3C_2Tx-E, and TiO2/Ti_3C_2Tx-H captured 16, 31, and 35 distinct phosphopeptides, respectively. The mixed-phase TiO2/Ti_3C_2Tx-H composite exhibited the highest enrichment efficiency. This superior performance was attributed to the synergistic effect of the high concentration of Ti(Ⅳ) sites in well-crystallized TiO2, which enabled specific Ti—O—P coordination, and the residual MXene substrate, whose surface functional groups facilitated interactions with phosphopeptides via hydrogen bonding. [Conclusions] This study successfully developed a green and efficient solvent-engineering strategy for the controllable preparation of TiO2/Ti_3C_2Tx heterostructures with tailored crystal phases and morphologies without requiring external titanium sources. The results demonstrated that the crystal phase and surface chemistry of these heterostructures could be precisely modulated by the solvothermal solvent, determining their phosphopeptide enrichment performance. The mixed-phase anatase/rutile TiO2 grown in water exhibited the highest efficiency, capturing the most phosphopeptides. This work provides a novel and sustainable pathway for fabricating MXene-based functional heterostructures and establishes their considerable potential as high-performance platforms for phosphoproteomics analysis. The insights into the structure–property relationship offer a valuable design principle for developing advanced separation materials, with potential applications in energy storage and catalysis.
[Objective] Traditional organic synthesis reactions are usually conducted in flasks or reactors, where the detection of reaction intermediates relies on time-consuming offline analyses, making real-time characterization difficult in conventional laboratory courses. Recent studies have shown that organic reactions can be dramatically accelerated in electrospray droplets, with some proceeding to completion instantaneously during ionization under ambient conditions. Compared with traditional synthesis, this greatly shortens reaction time. Thus, the electrospray ion source serves as both an ionization tool and a microreactor, enabling “on-source synthesis” and the online capture of transient intermediates. When coupled with tandem mass spectrometry(MS/MS), the captured intermediates can be structurally validated. This study aims to develop a simple and efficient online analysis platform for capturing reaction intermediates and to integrate it into experimental teaching. [Method] Carbon fiber paper spray and electrospray ionization were innovatively employed as reaction inlets. The platform supports rapid on-source reactions, in which two reactants are instantaneously mixed and reacted within paper-spray or needle-spray droplets. It simultaneously enables in situ, real-time capture and structural verification of intermediates. The ion source offers several advantages, including simple assembly, reusable carbon fiber substrates, low cost, and high reproducibility and sensitivity. These features make it particularly well suited for investigating on-source organic reactions and mechanisms in undergraduate laboratory teaching. The device was applied to the synthesis of aryl phenols from arylboronic acids and hydrogen peroxide. Hydrogen peroxide solution was introduced through the electrospray needle, with water as a blank control. [Results] Based on mass spectrometry results, the reaction between arylboronic acid and H_2O2 is inferred to proceed through two mechanisms. The first is an ionic pathway, where the lone electron pair of the peracidate anion attacks the boron atom of the boronic acid to form a Lewis acid–base complex. This complex undergoes aryl migration and eliminates a hydroxide ion to generate a boronic acid aryl ester. Under basic conditions, the boronic acid aryl ester is hydrolyzed, ultimately producing a phenoxide anion and boronic acid. The key experimental evidence supporting this pathway is the detection of characteristic intermediate signals at [M + 31]⁻(Lewis complex) and its dehydration product at [M + 15]⁻(corresponding to the boronic acid aryl ester or a related species) in the mass spectra of all tested substrates. These assignments are further confirmed by MS/MS fragmentation patterns. The second mechanism is a radical pathway, where H_2O2 initially generates a peroxy radical anion that coordinates with arylboronic acid to form a radical adduct, corresponding to the characteristic intermediate signal at [M + 32]-·. This radical intermediate subsequently undergoes single-electron transfer, rearrangement, and hydrolysis, ultimately yielding the phenoxide anion and boronic acid as well. [Conclusions] This study demonstrates the successful on-source synthesis of aryl phenols and the capture of key intermediates using electrospray-assisted carbon fiber paper spray ionization mass spectrometry, systematically revealing the coexistence of ionic and radical pathways. The method enables millisecond-level mixing and reaction in droplets, reducing the traditional reaction time of ～2 h to less than 2 min, and allows online structural validation of intermediates without the delays associated with offline analysis. Students not only acquire essential skills in mass spectrometer operation through ion source assembly and parameter optimization but also develop mechanistic reasoning by analyzing intermediate signals obtained from different substrates. The high instructional efficiency of this experiment transforms organic synthesis and mechanistic studies into a feasible, comprehensive teaching module that addresses the challenge of capturing short-lived intermediates while providing multidimensional training in experimental design, real-time monitoring, and mechanistic validation.