Carbon fibers are a key strategic material whose production must be independently guaranteed for national security reasons. Their fabrication into composites results in high⁃performance materials with added value. In this review， the development history as well as domestic and international research progress in carbon fiber reinforced polymer composites are briefly described from the viewpoints of the carbon fibers， the resin matrix and the interphase between fiber and matrix. Progress in the development of high⁃performance carbon fiber reinforced polymer composites in the Advanced Composites Center （ACC） at Beijing University of Chemical Technology （BUCT） is introduced， including the construction of new interphases and mechanisms of interfacial compatibility， key preparation technology for high⁃performance resin matrixes， and innovation and application of reinforced and toughened composites. The fabrication of “carbon fiber” admission certificates for new BUCT undergraduates is discussed in detail， including the synthesis and preparation of the epoxy matrix， preparation of the carbon fiber prepreg， lay⁃up design of the prepreg layer， composite sheet molding and surface printing. Finally， future development directions for carbon fiber⁃reinforced polymer composites are discussed.

Skin injury repair is a complex biological process， and research aimed at enhancing the self⁃repairing capacity of chronic or refractory skin wounds has significant clinical relevance. Exosomes， a subset of extracellular vesicles secreted by cells， have emerged as critical mediators of intercellular communication. Among these， stem cell‑derived exosomes exhibit multifaceted biological functions， including the modulation of immune and inflammatory responses， promotion of angiogenesis at wound sites， acceleration of skin cell proliferation and re‑epithelialization， and inhibition of excessive scar formation. These properties position stem cell‑derived exosomes as a promising therapeutic strategy in the field of skin injury repair. This review comprehensively examines the regulatory roles and molecular mechanisms underlying the actions of stem cell‑derived exosomes during skin injury repair. It further summarizes their emerging clinical applications in the treatment of chronic or refractory skin wounds， while exploring potential future directions for research and development in this rapidly evolving field.

The Rectisol process is widely used for removing acidic gases and purifying hydrogen. However， this process has problems such as high energy consumption and operating costs， which is particularly prominent in the deacidification gas absorbent recovery system of Rectisol devices.Therefore， taking the absorbent recovery system of deacidification gas as the research object， Aspen Plus was used to model the process. Under the feed condition of 80% coke+20% coal， the relative errors between the simulation results and the factory’s actual data for key parameters were less than 5%， which verified the reliability of the model.The mass reflux ratio of the thermal regeneration tower， the top output of the thermal regeneration tower， and the top output of the methanol-water separation tower were selected as the optimization variables， and the annual total utility cost and the H₂S content in the circulating methanol were selected as the optimization objectives. A multi⁃objective optimization framework based on a combination of Python and Aspen Plus was established， and the non‑dominated sorting genetic algorithm Ⅱ （NSGA-Ⅱ） was used to optimize the deacidification gas absorbent recovery system.The method was used to optimize the working conditions for three feed ratios （100% coke， 80% coke+20% coal， 100% coal）. The results show that as the proportion of coke in the feed decreases， the H₂S content in the raw material decreases， the mass reflux ratio of the thermal regeneration tower increases， the top output of the thermal regeneration tower decreases， and the top output of the methanol-water separation tower initially increases and then decreases. The total annual utility cost and the H₂S content in the circulating methanol are reduced.The results can provide a basis for changing the operating parameters of the deacidification gas absorbent recovery system when changing the feed ratio.

The separation of dimethyl carbonate/methanol azeotropes using traditional pressure⁃swing distillation suffers from the problem of high energy consumption. Aspen Plus software was used to simulate the conventional heat integration process of the pressure⁃swing distillation process， the coupling process of intermediate reboiler heat pump distillation and conventional heat integration， conventional heat pump distillation， and the coupling process of conventional heat pump distillation and intermediate reboiler. The improvements resulting from different processes were evaluated with the total annual cost （TAC） and carbon dioxide emissions as indicators.The results show that when the depreciation period of the equipment is three years， the heat⁃integrated coupling process with an intermediate reboiler added to the 16th plate affords the most economic benefits， and the TAC is 22.73×10⁶ yuan/a， which is 43.06% lower than that of the original pressure⁃swing distillation process.When the depreciation period of the equipment is five years， the coupling of two⁃stage compression conventional heat pump distillation and an intermediate reboiler has the best economic benefit. The TAC is 21.02×10⁶ yuan/a， which is 47.34% lower than that of the original pressure⁃swing distillation process. The carbon dioxide emission is 3.01 t/h， which is 76.13% lower than that of the original pressure‑swing distillation process.When the equipment depreciation period is eight years， the TAC of the coupling of two‑stage compression conventional heat pump distillation and an intermediate reboiler is the smallest （17.50×10⁶ yuan/a）， which is 56.16% lower than that of the original pressure‑swing distillation process.

ZIF⁃8⁃71 hybrid ligand crystals have been prepared by partially replacing the ligand 2⁃methylimidazole （HmIM） used in the preparation of ZIF⁃8 with 4，5⁃dichloroimidazole （DcIM） and characterized by X⁃ray diffraction （XRD）， Fourier transform infrared spectroscopy （FT⁃IR） and scanning electron microscopy （SEM）.ZIF⁃8 membranes were prepared on the inner surface of an α⁃Al₂O₃ ceramic tube carrier by a secondary crystal growth method using ZIF⁃8⁃71 as crystal seeds. The effects of varying the concentration of the crystal seed solution and the flow rate of the membrane growth solution on the microstructure and gas separation performance of the ZIF⁃8 membranes were investigated.The results show that when the ligand substitution rate was 50%， the particle size distribution of ZIF⁃8⁃71 crystals was uniform and they could be coated on the surface of the macroporous carrier to repair the surface defects of the carrier and form a dense seed layer.The thickness of the ZIF⁃8 film increases with increasing concentration of the ZIF⁃8⁃71 seed solution. When the mass fraction of the seed solution was 0.1%， the film was uniform and dense， and the surface was smooth.It was difficult to form a continuous and smooth film when the flow rate of the membrane growth solution was too low or too high. The ZIF⁃8 film formed at a flow rate of 0.333 mL/min exhibits good compactness and continuity.Under the optimized conditions， the H₂ permeation amount of a ZIF⁃8 membrane prepared with ZIF⁃8⁃71 as crystal seeds was 4.73×10^-6 mol/（Pa·s·m²）， and the ideal selectivity coefficients of H₂/CO₂， H₂/N₂ and H₂/CH₄ were 6.32， 8.75 and 12.12， respectively. The gas separation performance of the ZIF⁃8 membrane prepared with ZIF⁃8⁃71 as crystal seeds was better than that of a ZIF⁃8 membrane prepared with ZIF⁃8 as crystal seeds.

The geometric structures， stabilities and properties of Mg₃B _n ^{0/ -} （n=1-8） clusters have been systematically investigated using the particle swarm optimization structure prediction program （CALYPSO） combined with density functional theory.The lowest energy structures were obtained at the single⁃point CCSD（T）/aug-cc-pvdz level based on the geometries optimized at the B3LYP/6-311+G（d） level. Mg₃B₃ ^- and Mg₃B₄ ^- were found to adopt 2D planar structures， whereas all the other clusters had 3D structures.The neutral and anionic clusters possess similar structures in the case of n=3， 6， and 8. Subsequent calculations of the average binding energies， second⁃order energy differences， and HOMO-LUMO gaps indicate that theMg₃B₆ and Mg₃B₇ ^- clusters have the highest stability. The electronic properties of the clusters were further analyzed by calculating charge transfer and Wiberg bond orders. Based on the Multiwfn program， the photoelectron spectra， infrared spectra and Raman spectra were computationally simulated to facilitate their spectral characterization. Investigation of the thermodynamic properties showed that the heat capacity and entropy increase with increasing temperature and that the relationships between C_v （S） and T are approximately quadratic.

Engineering quality directly affects and restricts the “safe， stable， long‑term， full and excellent” operation of large⁃scale petrochemical units. However， the accuracy of the existing evaluation technology is low， and it is difficult to ensure continuous and stable production and maximum benefit output.Therefore， in this work， we propose a quantitative method of evaluating petrochemical plant engineering quality based on Leaky⁃Bayesian inference.Firstly， the engineering quality indexes of each level of the evaluated device were screened， and a systematic analysis of the index layer and the criterion layer was carried out to obtain the corresponding index level.Secondly， the evaluation index system was transformed into a Bayesian network （BN） model， and the Leaky Noisy⁃or Gate （LNoG） theory was subsequently introduced to construct a Leaky⁃Bayesian inference model.Then， according to the index analysis and weight distribution results of the criterion layer， the nodes and conditional probabilities of the inference model were determined.Finally， the Bayesian formula was used to evaluate the quality of the petrochemical plant engineering quantitatively.The resulting Leaky⁃Bayesian inference model was verified for three real cases： a leakage explosion accident in a propylene oxide device， an explosion accident in an ethylene glycol device and an explosion accident in a raw oil buffer tank. The results using our model are consistent with the accident assessment results for these dangerous events.The comprehensive evaluation method， grey correlation degree evaluation method， traditional Bayesian inference model and our Leaky⁃Bayesian inference model were compared for the leakage and explosion accident of a propylene oxide device. Compared with the other methods， the engineering quality evaluation method based on Leaky⁃Bayesian inference has higher efficacy， rationality and accuracy.

Millable polyurethane （MPU） has emerged as an exceptional damping material due to its tailorable molecular structure， modulus stability across wide temperature ranges， and tunable damping properties. However， research to date has predominantly focused on regulating the types and ratios of soft/hard segments， while studies on damping performance optimization through sulfur vulcanization remain insufficient. In this work， a 3MPTMG-MPU raw rubber was synthesized using 3⁃methyltetrahydrofuran/tetrahydrofuran copolymer polyol （3MPTMG）， diphenylmethanediisocyanate （MDI） and trimethylolpropanemonoallyl ether （TME）. Vulcanized 3MPTMG-MPU damping materials were prepared with varying sulfur contents （0.5-2.5 phr）. The material properties were characterized using Fourier transform infrared spectroscopy （FT-IR）， thermogravimetric analysis （TGA）， differential scanning calorimetry （DSC）， dynamic mechanical analysis （DMA）， and tensile tests. The experimental results demonstrated that reducing the sulfur content from 2.5 phr to 0.5 phr decreased the glass transition temperature （T _g） from -39 ℃ to -51 ℃ and increased the tan δ _Max from 0.82 to 1.02， representing a 24.4% enhancement. This study provides a novel strategy for designing new high⁃performance MPU damping materials.

The reactive extrusion modification of polyethylene terephthalate （PET） was carried out by coupling 1，3⁃phenylene⁃bis⁃oxazoline （PBO） and pyromelliticdianhydride （PMDA）， and the effects of the chain expansion modification of the PET were analyzed for varying ratios of the two chain expanders.The changes in torque were monitored with a torque rheometer during the chain expansion reaction supported by dynamic rheological tests， crystallization property tests and mechanical property tests. The effects of chain expansion modification on the molecular chain structure and mechanical properties of PET were analyzed. The results show that the maximum impact strength of a PBO/PMDA⁃modified PET spline is 73.42 kJ/m²， which is 103.1% higher than that of PMDA⁃modified PET.The results provide a valuable reference for the preparation of new industrialized foams by extrusion modification of PET.

Corrosion problems can easily lead to safety hazards and even serious safety accidents in refining and chemical equipment， so it is of great significance to develop rapid and accurate corrosion state prediction technology for refining and chemical equipment. Most previous studies have made corrosion prediction based on ideal experimental data and virtual generated data， but have ignored the actual situation. This paper collected 621 sets of corrosion detection data for acid water vapor extraction equipment and adopted the synthetic minority over⁃sampling technique with edited nearest neighbors （SMOTEENN） algorithm to solve imbalances of the original data. A prediction model for predicting the equipment corrosion state （including corrosion type and corrosion degree） based on a genetic algorithm⁃based K⁃nearest neighbors （GA⁃KNN） algorithm was established. The results show that in terms of dataset balancing， the SMOTEENN algorithm can effectively balance the dataset and improve the model’s ability to recognize the corrosion state of the equipment. In terms of equipment corrosion state prediction， the KNN model optimized by a genetic algorithm has better prediction effect，with the prediction accuracy of equipment corrosion type and corrosion degree reaching 0.993 3 and 0.981 2， respectively. The model can realize a comprehensive diagnosis of equipment corrosion， and provides theoretical guidance for corrosion monitoring and the maintenance of acid water vapor extraction equipment.

Because large language models show good understanding and generation capabilities， various industries have begun to study automated intelligent assistants based on these models， calling upon tools to help people solve complex problems. Large language models are prone to hallucination problems when generating responses due to the closed and complex data in the training phase. When facing complex problem planning， large language models often find it difficult to accurately generate function names and parameters when calling multiple different tools， and cannot coordinate the invocation of multiple tools to find answers. To improve the model’s tool call accuracy when planning tasks for complex problems， this paper proposes a prompt method that employs a chain-of-thought approach， Reasoning to Annotation and Coding （ReACo）. This method fully utilizes pre-trained data and enhances the language model’s ability to understand complex tasks through a task planning prompt method that combines code and annotations. Based on this， a new framework for large language model thinking prompts， named ReACoGPT， is proposed. The language model using ReACoGPT prompts can accurately call multiple plug-ins and provide logical task planning capabilities based on facts， so that， while maintaining the logic of task planning， the actual requirements data can also be accurately utilized. Experimental results show that compared with existing methods， ReACoGPT has achieved improvements in multiple indicators on the RestBench dataset， confirming that the ReACo prompt method can enhance the planning and reasoning capabilities of large language models， effectively utilize a large amount of training data to effectively plan tasks， and promote the further development of large language models in tool learning.

Reasonable mode partitioning for batch processes can enhance the accuracy of the prediction model for multimodal quality variables， which is crucial for ensuring product quality. Considering the influence of nonlinearity， temporality， and dynamic characteristics of process data on the rationality of mode partitioning results， a mode partitioning method of batch processes based on an undecimated lifting scheme packet-instantaneous frequency response function （ULSP-IFRF） is proposed in this work. The data for batch processes is first decomposed in the time-frequency domain using an undecimated lifting scheme packet transform， and the discriminant rule of the optimal decomposition layer number is constructed by combining the information entropy and the evaluation index of mode partitioning. The transient frequency response function of the wavelet domain is then introduced to characterize the dynamic time-dependent characteristics of the batch process， and then the fuzzy C-means clustering algorithm is used to perform unsupervised clustering on the IFRF value in the wavelet domain， achieving mode partitioning of the batch process. Finally， the rationality of the mode partitioning results obtained using the proposed method is verified through experiments on the penicillin fermentation process.

Traditional fault diagnosis methods usually involve complex data processing as well as pattern definition， long diagnosis and analysis times， and poor interpretability， which are not applicable to real-time production control. In this paper， we propose an equipment operation fault diagnosis method based on a Monte Carlo tree search and large language modeling. By modeling the equipment operation fault problem as a search space， representing the possible operation states of the equipment and the different causes of faults as states， and defining the operation behaviors and decision-making behaviors as the transfer of states， and then using the Monte Carlo tree search to simulate the operation logic tree of the equipment in a specific state， the operation of the equipment， the faults detection， and the execution of the operations can be simulated. By using the prior knowledge as well as the reasoning capabilities in the large language model， the operation of the device in the current state is simulated， and the results of the simulated operation including possible faults and other critical information are generated. Finally， the results of the simulation are evaluated by assigning an appropriate score to each state to indicate the value in solving the problem. By combining the production capabilities of the language modeling and outputting the natural language diagnostic explanations and suggestions， the interpretability as well as the real-time nature of the diagnostic method are effectively improved， and the inspection efficiency as well as the detection accuracy are enhanced. The proposed method was employed in the fault diagnosis for large-scale coal mining equipment. Compared with the traditional Monte Carlo tree search method， the fine-tuned Llama2 language model inference method and the Llama2 direct inference method， the accuracy of our new method is improved by 5.9%， 12.2% and 23.3%， and the task success rate is improved by 9.3%， 21% and 28%， respectively.

For small sample experimental observation data， the severe multicollinearity between indicator variables and the asymmetry of model error distribution can lead to the inability to construct a suitable accurate statistical model. In order to overcome the impact of inaccurate estimates of the shape parameters and skewness parameters of the error distribution on the statistical inference of linear regression models， this paper proposes a method using sensitivity analysis based on small sample data， which can more accurately estimate the shape parameters and skewness parameters of the model error distribution assuming that the error follows a skewed normal distribution. After obtaining parameter estimates of the error distribution， effective statistical inference can be made for linear regression models with severe multicollinearity. Firstly， Bayesian regression combined with the Markov Chain Monte Carlo （MCMC） method was used to estimate model coefficients， and then a posterior interval estimation was used to screen indicator variables. The simulation results showed that the method can effectively establish the final model. By constructing skewed simulation data with small samples， it was verified that our method provides a valuable alternative for statistical inference of linear regression models with multicollinearity for skewed data with small samples. Finally， a quantitative structure-activity relationship model between the quantitative parameters of crown ether molecules and the copper isotope fractionation coefficient {∆}^{\mathrm{65}} \mathrm{C} \mathrm{u} ( \% ) was constructed using the method proposed in this paper.