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.     

基于计算机视觉技术的茶叶品质随机森林感官评价方法研究

为弥补茶叶品质感官审评存在的缺陷，利用计算机视觉技术对茶叶品质进行快速无损评价研究。以碧螺春绿茶为对象，依据专家感官审评结果，将茶样分成4个等级；采用中值滤波及拉普拉斯算子对茶样图像进行预处理，并提取预处理后的茶样图像的颜色特征和纹理特征以表征茶叶图像的外形特征，利用随机森林算法对茶叶外形特征属性进行重要性排序；筛选出重要性较大的特征及随机森林算法中最优的决策树棵数建立感官评价模型，并与建立的支持向量机(SVM)模型性能相比较。结果表明：色调均值、色调标准差、绿体均值、平均灰度级、饱和度均值、红体均值、饱和度标准差、亮度均值、一致性等9个特征属性的重要性较大，且与感官审评特征描述结果相一致；当采用优选出的9个重要性较大的特征及决策数棵数为500时，建立的模型性能最优，模型总体判别率为95.75%，Kappa系数为0.933，OOB误差为5%，较SVM模型分别提高了3.5%，0.066，优选的9个重要性较大的图像特征与感官审评特征描述相一致。研究表明：利用随机森林方法筛选出对茶叶外形特征属性贡献最大的少数几个特征建立模型，模型性能就能达到很好的识别效果，模型得到简化，同时模型精度和稳定性都高于其他方法。     

基于LIBS技术的水泥粉末在线成分分析

在工业现场生产水泥的过程中，各种成分的含量直接影响着水泥的质量，因此如何快速准确地监测水泥中各个成分的含量意义重大。采用的实验方式为，将不经过任何预处理的水泥粉末直接放入位于二维移动平台上的物料盒中，通过激光诱导击穿光谱(LIBS)直接对水泥粉末表面的不同位置进行激发检测，对得到的光谱数据首先进行归一化和主成分分析等预处理操作，然后针对水泥中Ca, Si, Al, Fe, Mg五种元素，分别建立偏最小二乘(PLS)和支持向量回归(SVR)两种定量分析模型进行方法比较。此外，对比了粉末状水泥与压片式水泥两种测量方式的结果。实验结果表明，采用粉末状水泥直接测量的方式下，针对水泥样品元素浓度与所得到的光谱中特征线强度的关系，SVR方法比PLS方法更具优势，粉末状水泥直接测量的精度接近压片式测量的精度，说明LIBS技术对水泥粉末状样品直接在线测量具有可行性。     

Rapid and label-free classification of pathogens based on light scattering,reduced power spectral features and support vector machine

The rapid identification of pathogens is crucial in controlling the food quality and safety. The proposed system for the rapid and label-free identification of pathogens is based on the principle of laser scattering from the bacterial microbes. The clinical prototype consists of three parts: the laser beam, photodetectors, and the data acquisition system. The bacterial testing sample was mixed with 10 mL distilled water and placed inside the machine chamber. When the bacterial microbes pass by the laser beam, the scattering of light occurs due to variation in size, shape, and morphology. Due to this reason, different types of pathogens show their unique light scattering patterns. The photo-detectors were arranged at the surroundings of the sample at different angles to collect the scattered light. The photodetectors convert the scattered light intensity into a voltage waveform. The waveform features were acquired by using the power spectral characteristics, and the dimensionality of extracted features was reduced by applying minimal-redundancy-maximal-relevance criterion (mRMR). A support vector machine (SVM) classifier was developed by training the selected power spectral features for the classification of three different bacterial microbes. The resulting average identification accuracies of E. faecalis, E. coli and S. aureus were 99%, 87%, and 94%, respectively. The overall experimental results yield a higher accuracy of 93.6%, indicating that the proposed device has the potential for label-free identification of pathogens with simplicity, rapidity, and cost-effectiveness.     

Inverse colloidal crystal membranes for hydrophobic interaction membrane chromatography

A collaborative study on the robustness and portability of a capillary electrophoresis‐mass spectrometry method for peptide mapping was performed by an international team, consisting of 13 independent laboratories from academia and industry. All participants used the same batch of samples, reagents and coated capillaries to run their assays, whereas they utilized the capillary electrophoresis‐mass spectrometry equipment available in their laboratories. The equipment used varied in model, type and instrument manufacturer. Furthermore, different types of sheath‐flow capillary electrophoresis–mass spectrometry interfaces were used. Migration time, peak height and peak area of ten representative target peptides of trypsin‐digested bovine serum albumin were determined by every laboratory on two consecutive days. The data were critically evaluated to identify outliers and final values for means, repeatability (precision within a laboratory) and reproducibility (precision between laboratories) were established. For relative migration time the repeatability was between 0.05 and 0.18% RSD and the reproducibility between 0.14 and 1.3% RSD. For relative peak area repeatability and reproducibility values obtained were 3–12 and 9–29% RSD, respectively. These results demonstrate that capillary electrophoresis‐mass spectrometry is robust enough to allow a method transfer across multiple laboratories and should promote a more widespread use of peptide mapping and other capillary electrophoresis‐mass spectrometry applications in biopharmaceutical analysis and related fields.     

Study on convergence and error of a numerical method for solving systems of nonlinear Fredholm–Volterra integral equations of Hammerstein type

The aim of this paper is to present an efficient analytical and numerical procedure for solving systems of nonlinear Fredholm–Volterra integral equations of the Hammerstein type with the aid of fixed point techniques and the usual Schauder basis in an adequate Banach space.     

Lithium Bis(oxalate)borate Additive for Self-repairing Zincophilic Solid Electrolyte Interphases towards Ultrahigh-rate and Ultra-stable Zinc Anodes

The artificial solid electrolyte interphase (SEI) plays a pivotal role in Zn anode stabilization but its long-term effectiveness at high rates is still challenged. Herein, to achieve superior long-life and high-rate Zn anode, an exquisite electrolyte additive, lithium bis(oxalate)borate (LiBOB), is proposed to in situ derive a highly Zn²⁺-conductive SEI and to dynamically patrol its cycling-initiated defects. Profiting from the as-constructed real-time, automatic SEI repairing mechanism, the Zn anode can be cycled with distinct reversibility over 1800 h at an ultrahigh current density of 50 mA cm⁻², presenting a record-high cumulative capacity up to 45 Ah cm⁻². The superiority of the formulated electrolyte is further demonstrated in the Zn||MnO₂ and Zn||NaV₃O₈ full batteries, even when tested under harsh conditions (limited Zn supply (N/P≈3), 2500 cycles). This work brings inspiration for developing fast-charging Zn batteries toward grid-scale storage of renewable energy sources.     

Host–Guest Doping in Flexible Organic Crystals for Room-Temperature Phosphorescence

Organic single crystals (OSCs) with excellent flexibility and unique optical properties are of great importance due to their broad applicability in optical/optoelectronic devices and sensors. Nevertheless, fabricating flexible OSCs with room-temperature phosphorescence (RTP) remains a great challenge. Herein, we propose a host–guest doping strategy to achieve both RTP and flexibility of OSCs. The single-stranded crystal is highly bendable upon external force application and can immediately return to its original straight shape after removal of the stress, impressively emitting bright deep-red phosphorescence. The theoretical and experimental results demonstrate that the bright RTP arises from Förster resonance energy transfer (FRET) from the triphenylene molecules to the dopants. This strategy is both conceptually and synthetically simple and offers a universal approach for the preparation of flexible OSCs with RTP.     

Oxide Nanofibers as Catalysts Toward Energy Conversion and Environmental Protection

Ultrathin oxide nanofibers are widely used in an array of catalytic applications toward energy conversion and environmental protection. Remarkable progress has been made with regard to the development of engineering oxide nanofibers into unique structures to suit or enable various functions. We aim to provide a comprehensive overview of oxide nanofibers, including the structure engineering, derivates, assemblies and their applications. We begin with a brief introduction to the production of nanofibers with diversified compositions, structures and properties, followed by discussions of the wet-chemistry derivates. Afterward, we discuss the applications of catalytic oxide nanofibers, including electrocata-lysis, photocatalysis and thermal-catalysis. Then we highlight the most significant role of oxide nanofibers as catalyst support for the immobilization of metal nanoparticles. Moreover, we showcase the advanced assemblies based on oxide nanofibers, including their use as multi-functional membranes and foams. In the end, we offer perspectives on the challenges, opportunities and new directions for future development.