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。     

Prediction of Flooding in Packed Liquid-Liquid and High-Pressure Extraction Columns Using a Gaussian Process

Reliable prediction of flooding conditions is needed for sizing and operating packed extraction columns. Due to the complex interplay of physicochemical properties, operational parameters and the packing-specific properties, it is challenging to develop accurate semi-empirical or rigorous models with a high validity range. State of the art models may therefore fail to predict flooding accurately. To overcome this problem, a data-driven model based on Gaussian processes is developed to predict flooding for packed liquid-liquid and high-pressure extraction columns. The optimized Gaussian process for the liquid-liquid extraction column results in an average absolute relative error (AARE) of 15.23 %, whereas the algorithm for the high-pressure extraction column results in an AARE of 13.68 %. Both algorithms can predict flooding curves for different packing geometries and chemical systems precisely.     

基于面元素重排的三维网格模型数据隐藏算法

目的 为了解决目前三维数据隐藏算法不能兼顾无失真和盲提取的问题，提出一种新的完全无失真的三维网格模型数据隐藏盲算法。方法 首先使用混沌逻辑映射选择嵌入与提取模式，保证数据的安全性。然后利用面元素重排，完全不会造成三维模型失真的性质，通过不同嵌入模式规则对三角面元素进行重排，以嵌入秘密数据。接收端则可根据相应的提取模式规则提取秘密数据。结果 仿真结果与分析表明，该算法不会对三维模型造成任何失真，嵌入容量为每顶点2比特，且能抵抗仿射变换攻击、噪声攻击和平滑攻击等。结论 这种三维数据隐藏盲算法无失真，容量大、安全性高、鲁棒性强，适用于三维载体不容修改的情形，如军事、医学、秘密通信和版权保护等。     

Evaluation of anticorrosion and contact resistance behavior of poly(orthophenylenediamine)- coated 316L SS bipolar plate for proton exchange membrane fuel cell

The present work was focused on the corrosion properties and contact resistance behavior of poly(orthophenlyenediamine) (PoPD) coating on 316L SS bipolar plates. To reduce the corrosion rate and increase the interfacial conductivity of 316L SS bipolar plates, PoPD coating was deposited using an electropolymerization technique by the various monomer concentration of orthophenlyenediamine (oPD) on its surface. The presence of 1, 2, 4, 5- tetra substituted benzene nuclei of phenazine units in the polymer coating was confirmed by infrared spectroscopy. X-ray photoelectron spectroscopy analysis has confirmed the (%) of chemical composition in PoPD coating. The results of scanning electron microscopy analysis revealed that the uniform and compact coating with complete cover on 316L SS. The corrosion properties were investigated in 0.5 M H₂SO₄ and 2 ppm HF solution at 80 °C. The polarization test results showed that the PoPD coating reduced the corrosion current density both in the PEMFC anode and cathode environments. The charge transfer resistance values were in the order of 316L SS ? 0.02 M PoPD ? 0.06 M PoPD ? 0.04 M PoPD. A very low interfacial contact resistance and good adhesion strength was observed for 0.04 M PoPD coating. The higher contact angle of 0.04 M PoPD coating explained the hydrophobic property and more benefit of water management in the PEMFC environment. The results of the analysis of total metal ion releases clearly explained that the low level of metal ions released for 0.04 M PoPD coating. The overall studies revealed the PoPD coating with optimized 0.04 M oPD concentration showed best performance and provided more anodic protection to 316L SS bipolar plates.     

High H2 selectivity with low coke formation for methanol steam reforming over Cu/Y1.5Ce0.84Ru0.04O4 catalyst in a microchannel plate reactor

The synthesized novel metal oxides Y_xCe_yRu_zO₄ (x = 1.5, y = 0.84, z = 0.04) which was produced by the sol-gel method was used as a support for Cu active metal on the surface of a microchannel plate reactor in the methanol steam reforming (MSR) process. The prepared catalysts were characterized by X-ray powder diffraction (XRD), BET surface area analysis (S_BET), energy-dispersive X-ray analysis (EDX), field-emission scanning electron microscopy (FE-SEM), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), temperature-programmed desorption (NH₃-TPD), and temperature-programmed reduction (H₂-TPR). High methanol conversion (99.5%) and H₂ selectivity (98.7%) and low CO selectivity (1.4%) were achieved for Cu/Y_xCe_yRu_zO₄ coated microchannel reactor at 250 °C. FE-SEM images and TGA curve of the spent catalyst displayed no coke formation on the surface of the catalyst after 32 h on stream at 300 °C. The low reduction temperature of Cu, high BET surface area, and high pore volume of the catalyst are considered imperative factors that cause a better dispersion of copper on the Y_1.5Ce_0.84Ru_0.04O₄ support.     

Simultaneous fractionation of multiple classes of polyphenols from honeybush tea using solid-phase extraction

The structural diversity of polyphenols and the inherent limitations of current extraction techniques pose a challenge to extract polyphenols using a simple and green method. Hence, in this study, a method was developed to simultaneously fractionate multiple classes of polyphenols by only varying ethanol-water solutions. Honeybush tea, which is rich in polyphenols, was selected as a model for this study. Solvent extraction followed by solid-phase extraction (SPE) was developed to obtain a polyphenol-rich fraction from six honeybush samples. Based on a gradient elution programme (10%, 30%, 50%, 70% and 90% (v/v) ethanol-water solution) of SPE, the Strata X cartridge showed a better recovery of most targeted polyphenols under 0.9 mL of the drying volume and 1 mL min⁻¹ of the dispensing speed. The elution programme for fractionating most polyphenols was as follows: single elution with 50% ethanol, followed by twice elution with 70% ethanol. The antioxidant capacity was used to analyse the differences among the polyphenol-rich fractions from six honeybush samples. Principal component analysis (PCA) revealed that unfermented C. genistoides (GG) has the greatest antioxidant capacity among the honeybush species studied. Additionally, mangiferin, isomangiferin and vicenin-2 were the main contributors to the antioxidant capacity in six honeybush fractions according to the correlation study.     

Effect of metal oxide nanofluids on the performance of passive solar still single slope for two different absorbent plates

This study aims to improve the performances of a solar still single slope using metal oxide nanofluid (Al₂O₃–water, Cu₂O–water, and TiO₂–water). The numerical study was carried out for the climatic conditions of Agadir, Morocco, with different concentrations of nanofluids inside a basin equipped with an absorber plate with two different absorptivities. The numerical study is based on thermal balance equations applied on different solar system components and solved using the Runge Kutta method. The numerical model is validated by comparing our results with the literature available data. A comparison study of the effect of these nanofluids on solar still productivity is done. The results show that the productivity of the solar still using nanoparticles Cu₂O, TiO₂, and Al₂O₃ are 7.38, 7.1, and 7.064 kg m⁻² day⁻¹, respectively. It is obtained that the maximum efficiency of the solar still is found to be 55.27% by using cuprous oxide nanoparticles. Furthermore, an enhancement in solar still productivity of 6.36%, 19.54%, and 33.25% is obtained by dispersing 1%, 3%, and 5% volume fraction of Cu₂O nanoparticles in pure water, respectively compared to the conventional solar. Moreover, the impact of the absorptivity of the absorber plate on the solar still effectiveness is investigated. Two types of coatings are considered to change the absorber plate absorptivity. The results indicate that the efficiencies of the solar system are 58.81% and 51.77% using an absorber plate with 0.95 and 0.85 of absorptivity, respectively.     

A Feature Extraction Method for scRNA-seq Processing and Its Application on COVID-19 Data Analysis

Single-cell RNA-sequencing (scRNA-seq) is a rapidly increasing research area in biomedical signal processing. However, the high complexity of single-cell data makes efficient and accurate analysis difficult. To improve the performance of single-cell RNA data processing, two single-cell features calculation method and corresponding dual-input neural network structures are proposed. In this feature extraction and fusion scheme, the features at the cluster level are extracted by hierarchical clustering and differential gene analysis, and the features at the cell level are extracted by the calculation of gene frequency and cross cell frequency. Our experiments on COVID-19 data demonstrate that the combined use of these two feature achieves great results and high robustness for classification tasks.     

基于SVD和SSA-VMD降噪的轴承故障特征提取

针对强噪声背景下轴承故障特征提取困难的问题，提出一种基于奇异值分解和参数优化变分模态分解联合降噪的轴承故障特征提取方法(SSVMD)：首先，对原始信号进行奇异值分解(Singular Value Decomposition，SVD)处理，运用奇异值差分谱法选取有效奇异值并将原始信号重构得到初步降噪信号；其次，为防止故障信息丢失，将残余信号进行麻雀算法(Sparrow Search Algorithm，SSA)优化的变分模态分解(Variational Mode Decomposition，VMD）算法处理，得到最佳的模态个数K和惩罚参数α，选取峭度值最大、包络熵最小的IMF分量与初步降噪信号叠加得到最终降噪信号，并对信号进行包络分析；最后，通过仿真和试验数据分析得出，该方法能在信噪比很低的情况下降低噪声含量并提取轴承故障特征，为设备的状态监测和故障诊断提供理论依据。