一种植物油中苯并（a）芘含量测定的固相萃取前处理方法

为了建立一种准确性高、成本低的植物油中苯并(a)芘测定的样品前处理方法，基于GB 5009.7—2016以自制氧化铝柱为固相萃取柱，采用单因素试验对称样量、洗脱剂量、洗脱流速、吸附剂存放时间、吸附剂量这5个影响植物油中苯并（a）芘洗脱的因素进行研究，优化前处理条件，并对测定方法进行考察。结果表明：优化的前处理条件为称样量0.100 0 g、洗脱剂量120 mL、吸附剂（氧化铝）量22 g、洗脱流速1滴/2 s、吸附剂存放时间少于12周；方法检出限为0.2 μg/L，样品加标回收率为94.23%～100.00%，RSD为1.20%～7.36%；同一样品测定结果与SGS测定值接近，相对平均偏差为2.36%～3.50%。说明本试验方法测定结果准确，可应用于油脂企业植物油中痕量苯并（a）芘的测定。     

Thermal shock response behaviour of carbon fibre reinforced silicon carbide matrix composites prepared by chemical vapour infiltration at different heating rates

The present work investigated the effects of thermal cycles in air on the tensile properties of a two-dimensional carbon fibre reinforced silicon carbide composite (2D C/SiC) prepared by chemical vapour infiltration at different heating rates. The composite was exposed to different cycles of thermal shock between 20 °C and 1300 °C in air. The damage mechanisms were investigated by AE online monitoring and fractured morphology offline analysis. The tensile strength of 2D-C/SiC decreases with increasing thermal cycles. However, the modulus only decrease within 40 cycles. Due to oxidation, with the decrease in heating rate, the residual properties of the material decrease more obviously. Meanwhile, the results of AE online monitoring and fracture analysis show that the matrix damage is more serious at higher heating rate and that more delamination occours in tensile fractures. The above results indicate that for the thermal shock of 2D C/SiC composites in air, oxidative damage plays a key role in the residual properties.     

4-Methyl-2,4-bis(4-hydroxyphenyl)pent-1-ene,a Major Active Metabolite of Bisphenol A,Triggers Pancreatic β-Cell Death via a JNK/AMPKα Activation-Regulated Endoplasmic Reticulum Stress-Mediated Apoptotic Pathway

4-methyl-2,4-bis(4-hydroxyphenyl)pent-1-ene (MBP), a major active metabolite of bisphenol A (BPA), is generated in the mammalian liver. Some studies have suggested that MBP exerts greater toxicity than BPA. However, the mechanism underlying MBP-induced pancreatic β-cell cytotoxicity remains largely unclear. This study demonstrated the cytotoxicity of MBP in pancreatic β-cells and elucidated the cellular mechanism involved in MBP-induced β-cell death. Our results showed that MBP exposure significantly reduced cell viability, caused insulin secretion dysfunction, and induced apoptotic events including increased caspase-3 activity and the expression of active forms of caspase-3/-7/-9 and PARP protein. In addition, MBP triggered endoplasmic reticulum (ER) stress, as indicated by the upregulation of GRP 78, CHOP, and cleaved caspase-12 proteins. Pretreatment with 4-phenylbutyric acid (4-PBA; a pharmacological inhibitor of ER stress) markedly reversed MBP-induced ER stress and apoptosis-related signals. Furthermore, exposure to MBP significantly induced the protein phosphorylation of JNK and AMP-activated protein kinase (AMPK)α. Pretreatment of β-cells with pharmacological inhibitors for JNK (SP600125) and AMPK (compound C), respectively, effectively abrogated the MBP-induced apoptosis-related signals. Both JNK and AMPK inhibitors also suppressed the MBP-induced activation of JNK and AMPKα and of each other. In conclusion, these findings suggest that MBP exposure exerts cytotoxicity on β-cells via the interdependent activation of JNK and AMPKα, which regulates the downstream apoptotic signaling pathway.     

Predicting the mechanical properties of Cu–Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments

Micromechanics model, finite element (FE) simulation of microindentation and machine learning were deployed to predict the mechanical properties of Cu–Al₂O₃ nanocomposites. The micromechanical model was developed based on the rule of mixture and grain and grain boundary sizes evolution to predict the elastic modulus of the produced nanocomposites. Then, a FE model was developed to simulate the microindentation test. The input for the FE model was the elastic modulus that was computed using the micromechanics model and wide range of yield and tangent stresses values. Finally, the output load-displacement response from the FE model, the elastic modulus, the yield and tangent strengths used for the FE simulations, and the residual indentation depth were used to train the machine learning model (Random vector functional link network) for the prediction of the yield and tangent stresses of the produced nanocomposites. Cu–Al₂O₃ nanocomposites with different Al₂O₃ concentration were manufactured using insitu chemical method to validate the proposed model. After training the model, the microindentation experimental load-displacement curve for Cu–Al₂O₃ nanocomposites was fed to the machine learning model and the mechanical properties were obtained. The obtained mechanical properties were in very good agreement with the experimental ones achieving 0.99 coefficient of determination R2 for the yield strength.     

Repeated low-velocity impact responses of SiC particle reinforced Al metal-matrix composites

This study aimed to investigate experimentally the repeated low-velocity impact behaviors of SiC reinforced aluminum 6061 metal-matrix composites for different volume fractions and energy levels. In addition, the hardness variations were measured by the Vickers hardness tests from the impacted and impact-free cross-sections of the particle reinforced metal-matrix composites. Low-velocity impact tests were applied to composite samples manufactured by powder metallurgy (in 10, 20, and 30% volume fractions) at two total energy levels (15 and 60 J as single) and in repetitions equal to the sum of these energy levels (5 + 5 + 5 and 20 + 20 + 20 J as repeated). As a result, in increasing the impact number for all volume fractions, the total contact time was shortened and the peak contact force increased, whereas both the permanent central deflection and the absorbed energies reduced. Hence, these variations obtained under repeated impacts (5 + 5 + 5 and 20 + 20 + 20 J) revealed that metal-matrix composites showed a tougher behavior with an increase in the impact numbers from 1st to 3rd, particularly because of the strain hardening effect. Furthermore, an increase in volume fraction from 10 to 30% resulted in an increase in the impact strength under all repeated and single impacts despite changing deformation and damage mechanisms due to increasing the strain hardening effect and particle fractures. The hardness was affected by the volume fraction and increased as the volume fraction increased in both the impacted and impact-free zones. The repeated impact increased the impacted zone hardness more than the single impact for all volume fractions. Additionally, the hardness of the impacted zone under 20 + 20 + 20 J repeated impact was measured as the highest value in the 30% volume fraction. Therefore, metal-matrix composites can behave harder with the strain hardening effect under repeated impacts.     

Effect of the thicknesses of the Al2SiO5 ion conductor on the opto-electrical properties for all-solid electrochromic devices

In this study, an all-solid-state electrochromic device (ECD) with the structure of ITO/WO₃/Al₂SiO₅/NiO_x/ITO was prepared, and the effect of the Al₂SiO₅ solid electrolyte thicknesses on the opto-electrical performance was investigated. The microstructure and surface morphology were characterized using XRD, SEM and AFM, and the surface morphology and degree of surface looseness demonstrate a significant influence on the opto-electrical properties of ECDs. The charge transfer dynamics at the solid-solid interface were characterized using EIS to obtain an ionic conductivity of 4.637 × 10^-8 S/cm. CV, CA and UV–Visible spectra were employed to record the in situ electrochemical and optical properties. The results revealed that the highest optical modulation was 44.58%, the coloring and bleaching times were 14.8 s and 3.7 s, and the highest coloring efficiency was 98.17 cm²/C, which indicates that excellent opto-electrical properties were obtained. When the thickness increases, the degree of surface dense morphology transforms, and the loose morphology is more favorable for ion conductivity, which improves the opto-electrical properties. The results in this study provide insights into the understanding of Al³⁺-based all-solid-state ECDs, which promote the exploration of new types of Al³⁺ ionic conductors for all-solid-state ECDs.     

Piperine Targets Different Drug Resistance Mechanisms in Human Ovarian Cancer Cell Lines Leading to Increased Sensitivity to Cytotoxic Drugs

Our goal was to examine the anticancer effects of piperine against the resistant human ovarian cancer cells and to explore the molecular mechanisms responsible for its anticancer effects. Our study used drug-sensitive ovarian cancer cell line W1 and its sublines resistant to paclitaxel (PAC) and topotecan (TOP). We analyzed the cytotoxic effect of piperine and cytostatic drugs using an MTT assay. The impact of piperine on protein expression was determined by immunofluorescence and Western blot. We also examined its effect on cell proliferation and migration. We noticed a different level of piperine resistance between cell lines. Piperine increases the cytotoxic effect of PAC and TOP in drug-resistant cells. We observed an increase in PTPRK expression correlated with decreased pTYR level after piperine treatment and downregulation of P-gp and BCRP expression. We also noted a decrease in COL3A1 and TGFBI expression in investigated cell lines and increased COL3A1 expression in media from W1PR2 cells. The expression of Ki67 protein and cell proliferation rate decreased after piperine treatment. Piperine markedly inhibited W1TR cell migration. Piperine can be considered a potential anticancer agent that can increase chemotherapy effectiveness in cancer patients.     

Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named “Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.