沈阳科技学院

Epoxy resins (EP) are widely used in various fields due to their excellent mechanical properties, electrical insulation, and chemical resistance. However, their high flammability limits their application in fire-sensitive environments. Traditional halogenated flame retardants are being gradually phased out owing to environmental and health concerns, a trend that has driven the development of halogen-free alternatives. In this study, a novel phosphorus-, nitrogen-, and sulfur-containing ionic liquid, [PImam]Ps, was successfully synthesized using pentaerythritol phosphate (PEPA) and 1-(3-aminopropyl)imidazole, and subsequently introduced into an epoxy resin system. The structure of [PImam]Ps was confirmed by FTIR, NMR, and MS analyses. The curing behavior and thermal stability of the [PImam]Ps/DDM/EP composites were systematically investigated. The results demonstrated that the incorporation of [PImam]Ps significantly enhanced the flame retardancy and mechanical properties of the material. EP-5 exhibits the highest limiting oxygen index (LOI) of 30.1

To improve the defect detection performance on silicon carbide wafers, this study proposes an optimized YOLOv8 algorithm, named ESN-YOLOv8. This method achieves a synergistic optimization of detection accuracy and model efficiency by introducing an EMA attention module, a Slim-Neck lightweight neck network based on GSConv, and a WIoU bounding box loss function. The research results show that the model training process is stable and does not exhibit overfitting or underfitting. While maintaining a high recall rate, the detection accuracy and average precision (mAP50) are effectively improved. Ablation experiments verify the effectiveness and synergy of each improvement module. Compared to YOLOv8 and other classic algorithms, this algorithm shows significant improvements in multiple metrics: precision increased by 2.9%, recall rate increased by 0.5%, mAP50 increased by 4.6%, while the computational complexity (FLOPs) decreased by 14.8%, the number of parameters (Params) decreased by 20%, and the model weight (Weights) decreased by 15.9%. In addition, the ESN-YOLOv8 algorithm can more accurately focus on defect areas and significantly enhance the ability to capture micro-defect features. While achieving high-precision detection, the ESN-YOLOv8 algorithm significantly reduces model complexity, achieving an effective balance between accuracy and lightweight, and has good generalization ability and potential for practical applications.

Rotating machinery is the core equipment of the manufacturing industry, and its stability directly determines the operation of industrial systems. As a key component of rotating machinery, the accuracy of bearing fault diagnosis is particularly critical. Recently, deep learning (DL) has achieved remarkable results in mechanical fault diagnosis. However, traditional convolutional neural networks (CNNs) still have obvious limitations, which are difficult to effectively capture high-order nonlinearity features in the time-frequency domain and also show insufficient ability to decouple fault features in strong noise environments, which seriously restricts the improvement of diagnostic accuracy and model interpretability. To address these problems, a hybrid time-frequency spectral feature enhancement and attention fusion network (TFSAF-Net) is proposed. First, a time-frequency spectral feature enhancement module (TFSFEM) is designed. The TFSFEM employs learnable weight parameters to perform quadratic convolution nonlinear transformation on the wavelet time-frequency map of the signal in order to enhance its ability to extract higher-order fault features. Simultaneously, by integrating physical-driven feature decoupling module to extract envelope-related features and to utilize adaptive norm ratio-based feature metrics to improve the distinction between fault features and noise. Moreover, a convolutional multi-scale attention fusion module (CMSAFM) is developed. The CMSAFM introduces efficient multi-scale attention, which achieves precise focusing on key features through grouped feature interactions and adaptive weight allocation. Further, it realizes the effective integration of local details and global time-frequency distribution information through parallel extraction and weighted fusion of multi-scale features. Finally, a self-built engineering application datasets and PU dataset are implemented to validate the effectiveness and superiority of the TFSAF-Net. The experimental results demonstrate that our proposed method effectively captures high-order nonlinear features in the time-frequency domain and achieves efficient separation of fault characteristics from noise in highly noisy environments, and thereby reaches a higher diagnostic accuracy rate. Meanwhile, in terms of TFSAF-Net interpretability and generalization, it provides valuable insights for exploring similar problems within the field.

17α-Ethynylestradiol (EE2) is a ubiquitous synthetic estrogen of global concern. Due to its recalcitrance to biodegradation, EE2 is often insufficiently removed during wastewater treatment and consequently released with effluents, posing significant ecological risks and potential threats to human health. In this study, Rhodococcus equi DSSKP-R-001 (R. equi-001) was identified as one of the most potent EE2-degrading bacterium, achieving complete degradation of EE2 at low concentrations. Transcriptomic analysis revealed substantial upregulation and high functional similarity of genes encoding short-chain dehydrogenase/reductase (SDR) during EE2 degradation. Among these candidates, sdrR was functionally validated as the most catalytically efficient, achieving 97.88 % EE2 removal. Molecular docking analyses demonstrated that the sdrR-mediated conversion of EE2 to estrone (E1) is facilitated by multiple key amino acid residues through substrate binding, hydrogen bonding, and hydrophobic interactions. Furthermore, the sdrR-harboring genetically engineered bacterium (GEB) exhibited superior biodegradation potential, achieving up to 96.48 % EE2 removal individually and enabling complete elimination when applied for bioaugmentation. Concurrently, bioaugmentation with GEBs reshaped the sludge microbial community by optimizing interspecies interactions and increasing the abundance of denitrifying and phosphorus-accumulating microorganisms, thereby improving overall pollutant removal performance. Overall, this study expands the microbial and enzymatic repertoire for EE2 degradation and demonstrate the potential of genetic engineering strategies for improving the removal of recalcitrant micropollutants in biological wastewater treatment.

The rapid development of lithium-ion battery-powered electric vehicles has triggered an unprecedented demand for lithium resources. Spinel-type manganese-based lithium ion-sieves (LMO) offer high Li+ selectivity but suffer from severe manganese dissolution during acid elution, limiting sustainable recovery. This study modifies LMO using acrylic acid (AA) hydrogel granulation to enhance both adsorption and stability. Characterizations (XRD, FT-IR, SEM) confirm successful synthesis of granular GEL-LMO. AA gel not only acts as a binder but may also function as a structure-director and surface modifier: its three-dimensional network regulates the material's pore structure, facilitating mass transfer; its abundant carboxyl groups may introduce additional ion-exchange sites, potentially synergizing with LMO's intrinsic sites. In real brine (high Mg2+/Li+, multiple ions), GEL-LMO achieved a Li+ adsorption capacity of 23.59 mg center dot g-1 and a low Mn dissolution loss of 2.79% (pH = 12, S/L = 10 g center dot L-1, T = 25 degrees C). The process follows pseudo-second-order kinetics and the Langmuir isotherm, indicating monolayer chemisorption, and is spontaneous and endothermic. Compared to unmodified LMO, GEL-LMO shows superior capacity, cycling stability, ion selectivity (alpha Li/Mg = 417.93), and significantly reduced Mn loss, demonstrating its potential for sustainable Li+ recovery from complex salt-lake brines. This work provides a potential reference for material design toward the sustainable lithium recovery from the Baqiancuo salt-lake region.