Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.     

曲面相机阵列多分辨成像方法

针对现有成像系统因数据冗余而无法兼顾大视场、高分辨、高效性的问题,结合人眼视网膜变分辨成像和并列式复眼成像原理,设计一种多分辨率成像的复合仿生成像系统.该成像系统按照球面和平面兼顾的曲面布局方式,利用11个相机镜头构建相机阵列,组成了四个等级分辨率的子眼拍摄模块.通过物距100 m的远景实验和物距10 m的近景实验发现,该系统在实现高分辨成像的同时,获得总视场达150.8°×37.8°.多分辨率成像实验结果表明,该系统获取的图像的分辨率从中心视场到边缘视场逐渐降低,并且相较于中心清晰全视场成像,四级分辨率成像的拼接图像数据量减少了17.2倍的数据冗余.     

用于SCA ASIC测试的数字读出模块设计

高速开关电容阵列(SCA)具有高速采样、低功耗的特点,基于SCA的高速波形数字化是目前高精度时间测量的一个重要研究方向。为此,我们开展SCA芯片的研究,目前已设计完成原型ASIC设计,并正在进行后续版本的改进设计。为便于未来多版本ASIC的测试和评估,需设计具有一定通用性的数字读出模块,本论文工作主要介绍此模块的设计工作以及相应的数据读出软件。数字读出模块基于FPGA实现对待测ASIC的控制、配置及数据读出,采用DDR3片外存储芯片,使用USB3.0等接口进行数据传输;上位机软件基于Python3.7设计,实现了数据采集与波形绘制等功能。目前已使用设计完成的数字读出模块对第2版SCA ASIC进行了初步的测试,测试结果表明,此读出模块工作正常,且SCA芯片输出结果符合预期。     

Determination of sulfa antibiotic residues in river and particulate matter by field-amplified sample injection-capillary zone electrophoresis

In the present research, field-amplified sample injection–CZE (FASI–CZE) coupled with a diode array detector was established to determine trace level sulfa antibiotic. Sulfathiazole, sulfadiazine, sulfamethazine, sulfadimethoxine, sulfamethoxazole, and sulfisoxazole were selected as analytes for the experiments. The background electrolyte solution consisted of 70.0 mmol/L borax and 60.0 mmol/L boric acid (including 10% methanol, pH 9.1). The plug was 2.5 mmol/L borax, which was injected into the capillary at a pressure of 0.5 psi for 5 s. Then the sample was injected into the capillary at an injection voltage of –10 kV for 20 s. The electrophoretic separation was carried out under a voltage of +19 kV. The capillary temperature was maintained at 20˚C throughout the analysis, and six sulfonamides were completely separated within 35 min. Compared with pressure injection-CZE, the sensitivity of FASI-CZE was increased by 6.25–10.0 times, and the LODs were reduced from 0.2–0.5 to 0.02–0.05 μg/mL. The method was applied to the determination of sulfonamides in river water and particulate matter samples. The recoveries were 78.59–106.59%. The intraday and interday precisions were 2.89–7.35% and 2.77–7.09%, respectively. This provides a simpler and faster method for the analysis of sulfa antibiotic residues in environmental samples.     

Concurrent multiresponse non-linear screening: Robust profiling of webpage performance

Profiling engineered data with robust mining methods continues attracting attention in knowledge engineering systems. The purpose of this article is to propose a simple technique that deals with non-linear multi-factorial multi-characteristic screening suitable for knowledge discovery studies. The method is designed to proactively seek and quantify significant information content in engineered mini-datasets. This is achieved by deploying replicated fractional-factorial sampling schemes. Compiled multi-response data are converted to a single master-response effectuated by a series of distribution-free transformations and multi-compressed data fusions. The resulting amalgamated master response is deciphered by non-linear multi-factorial stealth stochastics intended for saturated schemes. The stealth properties of our method target processing datasets which might be overwhelmed by a lack of knowledge about the nature of reference distributions at play. Stealth features are triggered to overcome restrictions regarding the data normality conformance, the effect sparsity assumption and the inherent collapse of the ‘unexplainable error’ connotation in saturated arrays. The technique is showcased by profiling four ordinary controlling factors that influence webpage content performance by collecting data from a commercial browser monitoring service on a large scale web host. The examined effects are: (1) the number of Cascading Style Sheets files, (2) the number of JavaScript files, (3) the number of Image files, and (4) the Domain Name System Aliasing. The webpage performance level was screened against three popular characteristics: (1) the time to first visual, (2) the total loading time, and (3) the customer satisfaction. Our robust multi-response data mining technique is elucidated for a ten-replicate run study dictated by an L₉(3⁴) orthogonal array scheme where any uncontrolled noise embedded contribution has not been necessarily excluded.     

Ultrathin Mn Doped Ni-MOF Nanosheet Array for Highly Capacitive and Stable Asymmetric Supercapacitor

In this study, we demonstrate that an Mn-doped ultrathin Ni-MOF nanosheet array on nickel foam (Mn_0.1-Ni-MOF/NF) serves as a highly capacitive and stable supercapacitor positive electrode. The Mn_0.1-Ni-MOF/NF shows an areal capacity of 6.48 C cm⁻² (specific capacity C: 1178 C g⁻¹) at 2 mA cm⁻² in 6.0 m KOH, outperforming most reported MOF-based materials. More importantly, it possesses excellent cycle stability to maintain 80.6 % capacity after 5000 cycles. An asymmetric supercapacitor device utilizing Mn_0.1-Ni-MOF/NF as the positive electrode and activated carbon as the negative electrode attains a high energy density of 39.6 Wh kg⁻¹ at 143.8 Wkg⁻¹ power density with a capacitance retention of 83.6 % after 5000 cycles.     

Electrochemical Monitoring of Methotrexate Anticancer Drug in Human Blood Serum by Using in situ Solvothermal Synthesized Fe3O4/ITO Electrode

Here, we reported on a one‐step fabrication of magnetite Fe₃O₄ nanoparticles/indium tin oxide (ITO) electrode based on the direct growing of Fe₃O₄ nanoparticles on the ITO surface by using a solvothermal process. The modified electrode was used as electrochemical methotrexate (MTX) biosensor with high sensitivity based on cyclic voltammetry and square wave voltammetry techniques. The results demonstrated a linear relationship between the MTX concentration and its oxidation current peak over a wide range from 10^?5 to 10^?14 mole/L with a limit of detection of 0.4×10^?15 M based on the square wave voltammetry (SWV) technique. In addition, Fe₃O₄/ITO electrode showed a good capability for measuring very low concentrations of MTX drug dissolved in human serum solution. Also, Fe₃O₄/ITO electrode was used for detecting MTX in blood serum samples collected from patients after their treatment with MTX. The prepared electrode showed the higher sensitivity that higher than the Viva‐E instrument, which opens the door for developing a cheap, simple and higher sensitive MTX sensor.     

导电聚苯胺线膜修饰ZnO纳米棒阵列的可控制备及其紫外探测性能

基于p-n结的光生伏特效应可构筑性能优异的UV探测器,本文采用水热法可控制备竖直排列的氧化锌纳米棒阵列(n型ZnO-NRs),利用原位聚合法在ZnO-NRs表面上修饰p型聚苯胺线膜(PANI-NWs),再组装成ZnO-NRs与ZnO-NRs/PANI-NWs紫外探测器。通过扫描电镜(SEM)、X射线衍射仪(XRD)、紫外可见漫反射(UV-Vis)光谱表征样品的形貌、结构与光学性质。并通过电化学工作站测定电流-时间(I-t)和电流电压(I-V)曲线,表征其光响应性能。结果表明,制备的ZnO-NRs/PANI-NWs材料阵列排列整齐,界面接触良好,孔隙均匀。ZnO-NRs/PANI-NWs探测器在检测365 nm紫外光时,光电流为2.73×10^-4 A;检测254 nm紫外光时,光电流为1.44×10^-4 A。其光电流为ZnO-NRs探测器的4~10倍,ZnO-NRs和PANI-NWs之间形成的p-n结增强了光电导。用ZnO-NRs/PANI-NWs材料组装成的UV探测器体现出稳定性好,响应速度快,恢复时间短,电流增益高等优点,为开发高性能紫外光电探测器提供数据支撑。