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[Objective] Hot dry rock is a type of renewable energy that is widely distributed in the deep layers of the Earth's crust. Its exploitation can play a significant role in promoting the diverse and sustainable development of China's energy sector. However, when underground hot rock mass comes into contact with low-temperature working fluid, the mineral particles will shrink sharply, resulting in thermal stress and changing the physical and mechanical properties of the reservoir rock, which affects the connectivity rate of the fracture network. In this paper, an acoustic emission experiment under uniaxial compression and particle flow code(PFC) numerical simulations are combined to build a teaching platform for rock mechanics experiment under temperature shock, which can not only reduce the experimental cost and shorten the research period but also help to cultivate students' innovative spirit and practical ability. [Methods] The uniaxial compression test of granite samples is carried out by an SHT4 series electrohydraulic servo universal testing machine, and the acoustic emission test is carried out by the SAEU3H system produced by Beijing Shenghua Technology Company. The variation of peak compressive strength, elastic modulus, and Poisson's ratio with temperature is obtained according to the stress–strain curves of granite treated at different temperatures. The damage variable is calculated by using the energy value of the acoustic emission signal, and the different crack development stages are divided according to the damage variable curve. The effect of temperature change on the particles is realized by changing the thermal expansion radius of the granite, using the high-temperature treatment module that comes with the PFC software. The peak stress of the granite sample and its corresponding strain value are selected as the calibration objectives, and the parameters of effective modulus, normal strength, and stiffness ratio in the simulation process are gradually adjusted to make them approach the optimal value. Combined with the crack simulation diagram and the particle slip diagram, the failure mode of granite under the influence of high temperature is analyzed. [Results] The research results show that the peak compressive strength of granite increases first and then decreases with the increase in temperature, the elastic modulus decreases with the increase in temperature, and the Poisson's ratio increases with the increase in temperature, which indicates that high-temperature treatment enhances the plasticity of granite. When crack propagation enters the stable phase, the damage variable curve begins to rise, and new cracks initiate. As cracks further extend and coalesce, the damage variable starts to accumulate at an accelerated rate, marking the transition into the intensified crack propagation stage. The PFC simulation results show that when the treatment temperature is less than or equal to 250 ℃, the cracks show a vertical trend, and the failure of rock samples is mainly in the form of splitting; when the treatment temperature is greater than or equal to 450 ℃, the crack direction is inclined, and the failure mode is mainly shear. [Conclusions] This paper takes hot dry rock mining in the field of green and low-carbon energy as the engineering background, takes the evolution mechanism of rock formation mechanical properties under the action of temperature shock as the technical background, and combines the needs of experimental teaching in engineering colleges and universities to establish a rock mechanics experimental teaching platform that combines physical experiment and numerical simulation. Physics experiments can improve students' practical operation ability and obtain the real mechanical parameters of rocks treated at different temperatures. Through the analysis of crack images in PFC numerical simulation, students can deepen their understanding of the physical mechanics principles behind it. This teaching platform is conducive to improving students' ability to comprehensively apply theoretical knowledge and experimental results to solve engineering problems.
[Objective] The global environmental crisis is intensified by emerging pollutants including Bisphenol A(BPA), which leads to serious threats to the health of ecosystems and humans. To solve the above-mentioned problems, graduate students in environmental engineering must have innovative thinking and technical proficiency. Hence, it is vital to cultivate innovative graduate students through whole-process design of scientific experiments. This study focuses on enabling graduate students to comprehend the structure-performance relationships of bismuth oxyhalides(BiOBr and BiOI) for BPA removal. These practical experiences effectively cultivate critical thinking, technical proficiency and problem-solving skills of graduate students. [Methods] The experiment is structured into three phases: material synthesis, characterization, and performance evaluation. BiOBr and BiOI are synthesized by graduate students using microwave-assisted synthesis. For the preparation of BiOBr, bismuth nitrate pentahydrate is dissolved in a 10% glycol solution, followed by potassium bromide(KBr) was added to the solution and mixed. The mixture was microwave-heated at 160 ℃. By a similar method, potassium iodide(KI) was substituted for potassium bromide to prepare BiOI. After synthesis, materials were washed, dried, and characterized. Crystallinity and chemical composition are confirmed by XRD and FT-IR. Morphological differences revealed by SEM/TEM showed Bi OI nanosheets(～25 nm) and Bi OBr nanosheets(～62 nm). Optical and electrochemical analyses(UV-vis DRS, PL, Mott-Schottky) elucidated the bandgap structures and charge dynamics. Photocatalytic degradation of BPA(10 mg·L~(-1)) is conducted under a 500 W xenon lamp. Degradation efficiency is monitored via UV-vis spectrophotometry at 278 nm. Reactive species(·O_2~–, h~+, e~-) are identified using scavengers like tert-butanol. The graduate students conducted systematic correlation analysis to elucidate the relationship between material properties(e.g., band gap, morphology) and degradation performance. [Results] The results showed that BiOI achieved 88% BPA degradation efficiency within 120 min, which was significantly superior to that of BiOBr(28%). The corresponding conduction band potentials(ECB) of BiOBr and BiOI were 0.21 eV and 0.39 eV, respectively, while the valence band potentials(EVB) were 2.45 eV for BiOBr and 1.32 eV for BiOI, indicating that BiOI has a narrower bandgap compared to BiOBr. The superior degradation performance of BiOI over BiOBr was mainly due to the shorter band gap of BiOI, which facilitated the generation of a greater number of reactive oxygen species such as ·O_2~–, e~–, and h~+ during the photocatalytic process. [Conclusions] Implementing the whole-process design of scientific research experiments demonstrates that this closed-loop system integrates all stages, from design to hypothesis validation. This comprehensive approach includes the development and implementation of experimental plans, precise data collection and analysis, and validation of results through iterative feedback. By participating in this whole process, graduate students are able to develop key skills such as experimental design, problem solving, and technical proficiency. The closed-loop system not only promotes independent thinking and systematic research habits, but also encourages graduate students to critically evaluate their own hypotheses and optimize research methods based on empirical evidence. As a result, the process fosters students' abilities to think critically, innovate, and contribute substantively to their field of study.
[Objective] The battery management system plays a crucial role in the new energy vehicle industry and battery chemical processes. Predicting battery remaining useful life(RUL) significantly impacts the efficiency and accuracy of this system. However, batteries exhibit complex chemical mechanisms and physical changes, making it essential to study battery parameter characteristics deeply and develop effective life prediction models. While traditional prediction algorithms can estimate battery life, their accuracy is often insufficient. This paper designs a neural network prediction algorithm combining nonlinear dynamic adaptive inertia weight with twice particle swarm optimization(IPSO-BP) to significantly improve prediction accuracy. [Methods] This paper investigates data extraction and prediction algorithms for lithium battery RUL. First, the LAN BTS battery tester and electrochemical workstation were used to conduct charge-discharge tests and complete cycle accelerated aging tests on multiple lithium battery groups. The test data were preprocessed and saved. Then, the BP neural network was improved based on the particle swarm optimization algorithm(PSO). Health factors extracted from the battery's historical aging data served as input to the PSO-BP network to train its prediction capability. Multiple sets of battery aging data were used for prediction calculations, comparing traditional algorithms with the PSO-BP network to identify a higher-accuracy approach. Subsequently, to address the PSO algorithm's tendency to fall into local optima and premature convergence, a nonlinear dynamic adaptive inertia weight strategy was implemented, resulting in the improved IPSO-BP algorithm. Finally, new degradation datasets were measured using the battery tester, imported into the IPSO-BP model, and the RUL prediction curve was generated and compared against the actual test curve. [Results] Comparing the prediction results of different algorithms verified the superiority of the proposed approach. The same experimental data were predicted using SVM, RF, BP, and PSO-BP algorithms. The PSO-BP algorithm achieved the smallest mean square error(approximately 0.011), meeting RUL prediction requirements but not being optimal. The developed IPSO-BP model was then compared against PSO-BP. The IPSO-BP algorithm demonstrated significantly better prediction accuracy and a trend closer to the real values than PSO-BP. [Conclusions] This paper innovatively designs a combined virtual and physical experimental process for lithium battery life prediction and constructs an IPSO-BP prediction algorithm. Comparing battery accelerated aging test results with simulation calculations revealed that this algorithm not only outperforms traditional algorithms but also aligns more closely with real test values. This experimental design provides students with a robust platform, enhancing their practical abilities in data testing, processing, computational model development, and result prediction, while broadening their engineering problem-solving skills.
[Objective] The digital logic experiment course, essential for computer and electrical engineering undergraduates, has long emphasized foundational hardware concepts like logic gates, combinational circuits, and sequential circuits. However, the omission of emerging technologies integrating artificial intelligence(AI) and hardware from course materials has resulted in experimental content that lacks foresight, innovation, and interdisciplinary depth. This gap limits students' ability to connect theoretical knowledge with cutting-edge industry practices, particularly in AI-driven hardware acceleration. To address this, we propose a systolic array platform using FPGAs to accelerate YOLO neural network inference. The platform modernizes the curriculum by embedding AI-hardware co-design principles, enhancing students' ability to tackle real-world engineering problems through hands-on experimentation. [Methods] The platform employs a co-design approach integrating hardware and software components. Hardware utilizes an FPGA-based systolic array to accelerate inference for the YOLO neural network(widely adopted for real-time object detection). The systolic array architecture is selected for its inherent parallelism and efficiency in matrix multiplication/addition operations fundamental to neural networks. Software implements control and data transmission interfaces using Verilog HDL, enabling communication between the host system and FPGA accelerator. The platform supports structured experimental cases covering serial communication, state machine design, and Verilog-based circuit implementation. These guide students through designing, synthesizing, and debugging hardware circuits while introducing model quantization, pruning, and distillation for hardware-aware neural network optimization. [Results] The platform was successfully implemented and tested, demonstrating practicality and effectiveness for enhancing the digital logic course. Students gain hands-on experience in hardware circuit design/optimization for AI applications, specifically enabling them to: 1) Deepen understanding of hardware concepts(e.g., adders, multipliers, systolic arrays) by implementing them in neural network acceleration contexts; 2) Engage with mainstream AI technologies(e.g., YOLO) and learn hardware-oriented model optimization; 3) Develop proficiency in Verilog HDL and EDA tools(circuit design, synthesis, place-and-route, timing analysis, on-board debugging). The platform's "one platform, multiple objectives" model supports diverse topic exploration within a unified framework. [Conclusions] This study bridges traditional digital logic education with modern AI-hardware integration. Embedding YOLO acceleration into FPGA experiments enriches the curriculum and cultivates students' interdisciplinary problem-solving skills. Key innovations include the systolic array's adaptive data reshaping mechanism and integrating industry-standard AXI protocols into pedagogical tools. Future work will extend the platform to distributed FPGA clusters for large-scale model training and develop modules for emerging technologies like neuromorphic computing. These efforts position the digital logic course as a cornerstone of AI-driven hardware education, equipping students with skills critical for evolving technological landscapes.
[Objective] This study aims to systematically investigate the effects of three oxide electrodes—SrRuO_3(SRO), LaNiO_3(LNO), and LaSr_(0.5)Co_(0.5)O_3(LSCO)—on the structural and physical properties of Pb(Zr_(0.4)Ti_(0.6))O_3(PZT) epitaxial thin films through a comprehensive experimental framework. By integrating advanced ferroelectric capacitor fabrication techniques, high-precision characterization methods, and hands-on training, the experiment focuses on elucidating the regulatory mechanisms of electrode materials on ferroelectric performance. It establishes a holistic workflow encompassing “material design, process optimization, and performance analysis” to deepen students' understanding of the relationship between interface engineering and device performance in ferroelectric heterojunctions. The experiment also provides scientific insights for optimizing the ferroelectric device performance. [Methods] This experiment combines magnetron sputtering and sol–gel techniques to fabricate PZT heterostructures with different electrodes on SrTiO_3(STO) substrates. A Ti_3Al buffer layer and bottom electrodes(SRO, LNO, LSCO) are sequentially deposited via magnetron sputtering. Subsequently, a 120-nm PZT epitaxial film is synthesized using sol–gel spin-coating followed by annealing. The top electrodes are patterned using a shadow mask. Structural characterization included X-ray diffraction(XRD) to analyze lattice orientation and crystallinity, atomic force microscopy(AFM) to evaluate surface morphology and roughness, and Raman spectroscopy to quantify residual stress. Ferroelectric properties(e.g., polarization hysteresis loops, ΔP–V response, pulse width dependence, and fatigue resistance) are measured using a ferroelectric tester, whereas dielectric constants and leakage current densities are assessed via an LCR meter and a Keithley source meter, respectively. Mechanisms underlying electrode-material-induced interface effects are systematically explored through lattice mismatch calculations, dislocation density analysis, and stress-performance correlation models. [Results](1) Structural Properties: XRD analysis revealed that the SRO electrode system exhibited superior epitaxial quality, with the narrowest full width at half maximum of 0.735° for the PZT(002) rocking curve and the lowest dislocation density(2.306×10~(10)/cm~2), indicating optimal lattice matching. Raman spectroscopy further confirmed that SRO electrodes minimized residual stress(2.41 GPa) because of the smallest lattice mismatch with PZT, compared with LNO(3.12 GPa) and LSCO(3.56 GPa), effectively suppressing the interface defect formation.(2) Ferroelectric Performance: Ferroelectric testing demonstrated that the SRO/PZT heterostructure achieved the highest remnant polarization(Pr= 108.50 μC/cm~2), highlighting efficient polarization switching. In addition, its pulse-width-dependent stability(0.01–10 ms) and fatigue resistance(no degradation after 10~9 switching cycles) underscored the enhanced domain dynamics owing to the reduced interfacial stress.(3) Dielectric and Leakage Characteristics: The SRO system displayed the highest dielectric constant(ε_r) with superior stability, while its leakage current density(J) was one and two orders of magnitude lower than those of LNO and LSCO systems, respectively, validating the optimized charge transport at the interface.(4) Surface Morphology: AFM characterization showed that the SRO-based PZT film exhibited the lowest root-mean-square roughness(RMS=1.18 nm), substantially lower than LNO(1.23 nm) and LSCO(2.75 nm), thereby emphasizing the critical role of lattice compatibility in achieving smooth surfaces. [Conclusions] This study systematically unravels the influence of electrode materials on the performance of PZT epitaxial films by integrating fabrication, characterization, and testing methodologies. The SRO electrode, owing to its exceptional lattice compatibility with PZT, considerably reduces the interfacial stress and dislocation density, thereby endowing the heterostructure with optimal comprehensive properties: highest remnant polarization, lowest leakage current, superior dielectric stability, and robust fatigue resistance. These findings not only provide theoretical guidance for electrode optimization in ferroelectric capacitors but also establish an integrated “fabrication-characterization-analysis” experimental framework that bridges theoretical knowledge and practical training. Through hands-on participation in thin-film deposition, instrument operation, and data analysis, students gain mastery over core characterization techniques and a profound understanding of the impact of interface engineering on device performance, effectively enhancing their research capabilities and innovative thinking in functional material science.
[Objective] As 3D printing emerges as a transformative force in manufacturing and biomedical fields, China emphasizes its integration into undergraduate education to nurture interdisciplinary innovators. However, existing curricula often lack system-level, hands-on training bridging hardware design, process optimization, and advanced printing techniques. This study addresses this gap by developing a comprehensive teaching experiment centered on Digital Light Processing(DLP) 3D printing. The dual objectives are to(1) enable students to independently construct and operate a DLP printer, enhancing their understanding of photopolymerization and system integration, and(2) investigate how grayscale printing modulates material properties and precision, fostering analytical and research skills. [Methods] Students assemble a DLP printer using a DLP4710 projector(405 nm wavelength, 2W optical output) with a 1 920×1 080 Digital Micromirror Device(DMD) chip. A Raspberry Pi 4B serves as the control core, coordinating projection timing and platform movement via UART communication protocols. A Nema23 stepper motor, coupled with a 5 mm lead screw, enables precise vertical layer control(10 μm resolution). Critical steps involve optical alignment to ensure uniform illumination across the 136×76.5 mm build area and mechanically stabilizing the resin vat to minimize layer misalignment. Using ANYCUBIC ABS-LIKE photopolymer resin, students systematically correlate exposure time(1–30 s) with cured layer thickness. Students project a 10×10 mm square pattern and measure layer thickness using a vernier caliper. Concurrently, they quantify light intensity across grayscale levels(0–255) with an LP100 power meter to establish grayscale-light relationships. Dog-bone tensile specimens are designed in SolidWorks, sliced into layers using CHITUBOX, and printed with grayscale gradients(0–255). Post-printing, students evaluate dimensional accuracy using digital microscopy and assess mechanical performance via tensile testing to quantify strength-precision trade-offs. [Results] The experimental outcomes demonstrate both technical and pedagogical successes: 1) Students independently constructed a functional DLP printer, achieving a lateral resolution of 70 μm. Light intensity measurements confirmed a nonlinear relationship between grayscale values and light output, saturating beyond grayscale 200; 2) Cured thickness followed a logarithmic growth trend with exposure time. Notably, a 2-second exposure produced a 200 μm cured layer, highlighting the resin's rapid photopolymerization kinetics. However, prolonged exposure induced over-curing, leading to dimensional instability; 3) Higher grayscale values enhanced light intensity, promoting denser polymer networks and increasing tensile strength. However, this mechanical improvement coincided with reduced lateral precision. Significantly, minimal precision loss occurred at 30% grayscale, underscoring the necessity of balancing light intensity and exposure parameters for optimal performance. [Conclusions] This experiment establishes a replicable pedagogical model for 3D printing education, immersing students in the full process of device development, material characterization, and advanced manufacturing. By engaging in hands-on printer assembly, resin curing analysis, and grayscale optimization, learners gain practical insights into the synergies between optical engineering, material science, and digital control systems. The observed trade-offs between mechanical strength and geometric fidelity emphasize the importance of parameter optimization in real-world applications—a skill rarely addressed in traditional coursework. Future iterations could explore multi-material printing(e.g., elastomer-rigid polymer composites) or biocompatible resins for medical applications, further aligning academic training with industrial and biomedical challenges. Ultimately, this framework equips students with the interdisciplinary agility, technical proficiency, and critical thinking required to pioneer innovations in additive manufacturing.
[Objective] Distance-resolved differential absorption lidar(DR-DIAL) is an advanced active remote sensing technology that provides detailed information on gas distribution and emission patterns by measuring the absorption differences of lasers at different wavelengths by gas molecules. DR-DIAL operating in the 2 μm band features high-precision retrieval of the CO_2 concentration profile and plays an important role in greenhouse gas detection, benefiting from abundant CO_2 absorption lines in this band. However, the stringent experimental requirements of DR-DIAL systems pose challenges for traditional laboratory-based teaching. To address this, a teaching simulation platform was developed using the MATLAB graphical user interface(GUI). This platform not only provides a virtual experimental environment but also enhances student understanding of DR-DIAL system working principles and applications through intuitive visualization and interaction. [Methods] This paper provides a comprehensive overview of DR-DIAL working principles, including laser emission, signal transmission, reception, and data acquisition and processing. Based on these principles, the modelling process for DR-DIAL is elaborated, including the gas model, atmospheric model, and system model. The MATLAB App Designer was used to implement these DR-DIAL models, leading to the development of a MATLAB GUI-based simulation platform for 2 μm band CO_2 DR-DIAL teaching. The platform enables students to independently adjust experimental parameters and interactively observe simulated lidar echo signals and the retrieval of gas concentration profiles. Through this interactive environment, students can gain a comprehensive understanding of the entire workflow, from signal generation to concentration retrieval, fostering a deeper grasp of the underlying principles. [Results] The simulation platform demonstrates 2 μm band laser detection of CO_2 as a case study to showcase the practical applications of DR-DIAL. It allows students to customize various model parameters for specific experiments, enabling them to observe key processes such as absorption cross-section selection and calibration, lidar echo signal calculation and simulation, and gas concentration profile retrieval. Furthermore, the platform provides data storage capabilities, allowing students to conduct multiple experiments and compare their results for further analysis. The entire workflow is demonstrated through a predefined experimental scenario. Experimental results show that retrieved gas concentration values exhibit minimal deviation from the predefined values, validating the platform's effectiveness as a teaching and experimental tool. [Conclusions] The simulation platform is user-friendly, easy to debug, and supports autonomous learning and extended development. These features facilitate students' rapid comprehension of CO_2 DR-DIAL systems and enable a progressively deeper understanding of both operational principles and practical applications by providing practical experience in a virtual environment, thereby facilitating personalized learning and stimulating interest in DR-DIAL technology. Beyond its educational value, the platform serves as a reference for researchers developing DR-DIAL simulation platforms for various wavelengths and gases. It contributes to the broader dissemination and advancement of DR-DIAL technology, offering significant educational benefits and promising application prospects.
[Objective] Traditional experimental teaching in civil engineering materials often relies on fixed-proportion verification experiments using cement-based systems. While these experiments help students understand the basic principles of hydration and strength development, they tend to be repetitive and lack flexibility. This limits students' exposure to emerging materials and innovative design concepts. Moreover, current teaching models seldom incorporate sustainability, carbon neutrality, or lifecycle thinking. As green construction and low-carbon development become core strategies in infrastructure planning, it is increasingly important for civil engineering education to reflect these trends. There is a pressing need to reform traditional teaching approaches to align with environmental priorities and better prepare students for modern engineering challenges. Reforming such curricula can help bridge the gap between theoretical knowledge and practical skills, enabling future engineers to address global environmental concerns through material innovation and optimized design practices. [Methods] To address these limitations, this reform proposes replacing conventional cement with alkali-activated red mud-based cementitious materials, introducing students to the concepts of industrial waste recycling and sustainable materials. Red mud, a byproduct of alumina production, is highly alkaline and poses environmental challenges when stockpiled. However, when activated with alkaline solutions, it can serve as a viable binder with promising mechanical performance. This reform also incorporates Response Surface Methodology(RSM) into experimental teaching to help students optimize mix design scientifically. RSM allows for a structured analysis of variables such as red mud content, activator dosage, and calcination temperature, offering students practical experience in experimental design, statistical modeling, and material performance evaluation. By involving students in variable selection, model construction, and regression analysis, the approach encourages hands-on learning and deeper engagement with scientific principles. This method shifts students from passive learning to active problem-solving. [Results] Under optimized conditions—a calcination temperature of 850 ℃, an alkali activator dosage of 16%, and a red mud content of 10%—the material's flexural and compressive strengths improved by 32.18% and 42.21%, respectively, compared to unoptimized mixes. These results highlight the effectiveness of both material substitution and design optimization. Moreover, the optimized system has been successfully applied in a real-world engineering case: the expansion joint repair on the Bailu Bridge. The material enhanced mechanical performance by 11.3% and reduced project costs by 10.8%, confirming its technical and economic viability in infrastructure repair. The successful translation from classroom research to engineering practice validates the pedagogical effectiveness of this reform. [Conclusions] This reform bridges experimental learning with real-world application, offering students a deeper understanding of sustainable materials and modern design tools. It strengthens skills in data analysis, teamwork, innovation, and problem-solving, while promoting awareness of environmental responsibility. The use of industrial waste aligns with circular economy principles and national carbon reduction goals. Furthermore, the integration of RSM improves students' research capability and decision-making in engineering practice. The reform not only expands students' technical horizons but also fosters a mindset of lifelong learning and adaptability, essential traits for engineers in an evolving professional landscape. Overall, this reform provides a model for future civil engineering education—one that supports both academic rigor and societal relevance.
[Objective] Under the dual pressures of energy conservation and environmental protection, environmentally friendly refrigerants must exhibit excellent thermal performance, environmental compatibility, safety, and economy, along with adequate lubricant oil solubility. The solubility of refrigerant in oil alters the working viscosity, anti-friction, and anti-wear properties of the lubricant while ensuring miscibility, ultimately affecting compressor performance and lifespan. This solubility represents a key phase equilibrium property of mixtures and a critical parameter for evaluating refrigeration system performance.[Methods] A solubility apparatus was developed using the isochoric saturation method, comprising a gas chamber system, equilibrium cell system, temperature/pressure measurement systems, magnetic stirrer, and thermostat. The system measures gas solute solubility in liquid solvents at 243–373 K and pressures ≤5 MPa. Based on error propagation theory, the combined uncertainty at a 95% confidence level(k=2) was 2.8% for pressures ≤5 MPa. Carbon dioxide and n-decane served as reference fluids for validation. Using this experimental teaching platform, R1234ze(E) refrigerant solubility in ISO VG 68 polyol ester(POE) base oil was studied as a demonstration case. Experimental solubility data were compared with published values. [Results] Deviations between experimental and literature data for reference fluids fell within the apparatus uncertainty, confirming reliability. R1234ze(E) solubility in POE base oil was measured at 283–353 K and≤1.2 MPa(56 data points). Solubility increased with pressure and decreased with temperature. The Peng-Robinson(PR) equation of state coupled with the Wilson activity coefficient model(PR-Wilson) correlated the data well, with deviations ≤3.0%. For R1234ze(E) in POE oil, the absolute average and maximum absolute deviations between experimental and calculated pressures were 1.56% and 2.71%, respectively, demonstrating the model's effectiveness. Solubilities in POE oils of identical viscosity grades were comparable, while higher oil viscosity reduced solubility at fixed pressure/temperature conditions. [Conclusions] The experiments enhanced teaching quality by helping students understand gas-liquid dissolution characteristics, boosting learning engagement, and improving "Refrigeration Technology" course outcomes. Students gained proficiency in Origin and MATLAB, advancing their research literacy. Innovative teaching methods proved effective for talent development. Further refinements will optimize the experiment's feasibility for undergraduate teaching.
[Objective] As an important instrument for imaging research, the laser scanning confocal microscope has been gradually applied in the field of polymer science, such as polymer multicomponent systems, polymer particles, polymer films, self-assembly of block copolymers, controlled release of drugs, and the characterization of hydrogel. Interfacial polymerization is a common approach for synthesizing high-performance polymer materials. However, owing to the limitations of in-situ observation and characterization methods, the kinetics of polymerization and the evolution of the polymer structure during interfacial polymerization are difficult to explore in depth; hence, accurate control of the polymer structure by adjusting the polymerization conditions is difficult. [Methods] To overcome this problem, the aggregation-induced emission effect is used to realize in-situ and real-time observation of the entire interfacial polymerization process. Based on the environmental sensitivity of the fluorescent aggregation-induced emission moiety, clear images of the polymer are obtained by laser scanning microscope, and its structural evolution is characterized. In this experiment, tetrakis(4-aminophenyl) ethylene and toluene diisocyanate are selected as monomers for interfacial polymerization to produce polyurea in the alkane and ionic liquid solvent system. By dissolving tetrakis(4-aminophenyl) ethylene in ionic liquid and toluene diisocyanate in alkanes, the interfacial polymerization takes place at the stable alkane–ionic liquid interface and high fluorescent polymers are generated in a homemade device. A collection of images of the polymerization system colored by fluorescence intensity is obtained by the laser scanning confocal microscope. The effects of reaction time and monomer concentration on polymer growth are studied. The thickness and intensity distribution of the fluorescent region are analyzed by the affiliated software, such that the variation of thickness and density of the generated polymer could be clearly illustrated. [Results] The results showed that the fluorescent polymer continuously extends from the alkane solution to the ionic liquid phase, indicating that toluene diisocyanate has a strong tendency to diffusion, and the growth of a polymer layer occurs in the ionic liquid. With the further extension of polymerization time, the formed polymer hindered the diffusion of toluene diisocyanate; therefore, the reaction rate decreased slowly in the later stage. In addition, the experimental results showed that different monomer feed ratios can produce polymers with different microstructures. At the lower concentration of tetrakis(4-aminophenyl) ethylene, the fluorescence intensity of polymers grows slowly and is weaker than that at high concentrations, indicating that the overall polymer is less dense because of the lack of tetrakis(4-aminophenyl) ethylene. While under the same polymerization time, the higher concentration of tetrakis(4-aminophenyl) ethylene is accompanied by a faster polymerization rate and the polymer with higher fluorescence intensity is produced in a short time. By controlling the concentration and proportion of the two monomers, the structure of the polymer obtained by interfacial polymerization can be designed to achieve precise control of the polymer structure and performance. [Conclusions] Based on the high-resolution fluorescence imaging by laser scanning confocal microscope and the aggregation-induced emission effect, this study established a new method for the in-situ and real-time visualization of the interfacial polymerization reaction process, which can provide useful guidance for the regulation of interfacial polymerization systems or provide reference for the design and preparation of functional polymer materials.