多组件学习器实现有机分子沸点的精准预测

沸点(BP)是有机分子液体的基本物理化学量, 也是化学工业生产中的重要参数. 有机分子的沸点由分子结构决定, 呈现复杂的结构-沸点关系, 函数法(Function Method)、基团贡献法(Group Contribution Method)等传统方法无法应对复杂多样有机分子结构的预测, 应用范围狭窄, 预测精度低. 本研究中, 我们利用基于人工神经网络(ANN)和支持向量机(SVM)的多组件学习器实现有机分子沸点的精准预测. 我们构建了基于可解释性描述符的ANN、基于相关性描述符的ANN及基于复合分子指纹的SVM三个异质模型, 并通过包含4550个各种类别的有机分子沸点的数据集进行训练得到了三个异质性学习器, 最后集成三个学习器对有机分子沸点进行预测. 相比于传统方法和此前的定量结构性质关系(QSPR)模型, 多组件模型结合了三种模型的优点, 展现出很好的预测精度和泛化能力以及低的过拟合, 实现了对多种类型有机分子的沸点的有效预测.     

Optimizing the Photovoltaic Performance of Organic Solar Cells for Indoor Light Harvesting

Organic solar cells (OSCs) harvesting indoor light are highly promising for emerging technologies, such as internet of things. Herein, the photovoltaic performance of PTB7-Th:PC₇₁BM solar cells constructed using “optimized (with 1,8-diiodooctane (DIO))” and “non-optimized (without DIO)” processing conditions are compared for indoor and outdoor applications. We find that in comparison to the “optimized” solar cell, the “non-optimized” solar cell is less efficient under simulated solar light illumination (100 mW cm⁻², spectral range 350–1100 nm), owing to significant bimolecular charge carrier recombination losses. However, under simulated indoor illumination (3.28 mW cm⁻², spectral range 400–700 nm), bimolecular recombination losses are effective suppressed, thus the power conversion efficiency of the solar cell without DIO was increased to 14.7 %, higher than that of the solar cell with DIO (14.2 %). These results suggest that the common strategy used to optimize the OSCs could be undesired for indoor OSCs. We demonstrate that the efforts for realizing the desired “morphology” of the active layer for the outdoor OSCs may be unnecessary for indoor OSCs, allowing us to realize high-efficiency indoor OSCs using a non-halogenated solvent.     

Raney Ni催化生物基糠醛还原胺化合成糠胺

生物质衍生物糠醛还原胺化是合成糠胺的一种有效方法。本文以糠醛为原料、氨水为氨源,使用商业Raney Ni催化糠醛还原胺化合成糠胺,考察了氢压、温度、n(糠醛)/n(氨水)、溶剂、催化剂用量等条件对反应的影响。实验结果表明：在1,4-二氧六环溶剂中,0.03 g Raney Ni、n(糠醛)/n(氨水)=1/2、130 ℃、2.0 MPa H₂条件下反应3 h,糠醛转化率100%,糠胺选择性96.3%。与传统方法相比,该工艺实现了非贵金属催化剂作用下高选择性反应,具有操作简单、低成本、收率高等优点。      

Evaluation of Multi-Objective Optimization Algorithms for NMR Chemical Shift Assignment

An automated NMR chemical shift assignment algorithm was developed using multi-objective optimization techniques. The problem is modeled as a combinatorial optimization problem and its objective parameters are defined separately in different score functions. Some of the heuristic approaches of evolutionary optimization are employed in this problem model. Both, a conventional genetic algorithm and multi-objective methods, i.e., the non-dominated sorting genetic algorithms II and III (NSGA2 and NSGA3), are applied to the problem. The multi-objective approaches consider each objective parameter separately, whereas the genetic algorithm followed a conventional way, where all objectives are combined in one score function. Several improvement steps and repetitions on these algorithms are performed and their combinations are also created as a hyper-heuristic approach to the problem. Additionally, a hill-climbing algorithm is also applied after the evolutionary algorithm steps. The algorithms are tested on several different datasets with a set of 11 commonly used spectra. The test results showed that our algorithm could assign both sidechain and backbone atoms fully automatically without any manual interactions. Our approaches could provide around a 65% success rate and could assign some of the atoms that could not be assigned by other methods.     

Optimization of Vacuum-Microwave-Assisted Extraction of Natural Polyphenols and Flavonoids from Raw Solid Waste of the Orange Juice Producing Industry at Industrial Scale

Orange pomace (OP) is a solid waste produced in bulk as a byproduct of the orange juice industry and accounts for approximately 50% of the quantity of the fruits processed into juice. In numerous literature references there is information about diverse uses of orange pomace for the production of high-added-value products including production of natural antioxidant and antimicrobial extracts rich in polyphenols and flavonoids which can substitute the hazardous chemical antioxidants/antimicrobials used in agro-food and cosmetics sectors. In this work and for the first time, according to our knowledge, the eco-friendly aqueous vacuum microwave assisted extraction of orange pomace was investigated and optimized at real industrial scale in order to produce aqueous antioxidant/antimicrobial extracts. A Response Surface Optimization methodology with a multipoint historical data experimental design was employed to obtain the optimal values of the process parameters in order to achieve the maximum rates of extraction of OP total polyphenols and/or total flavonoids for economically optimum production at industrial scale. The three factors used for the optimization were: (a) microwave power (b) water to raw pomace ratio and (c) extraction time. Moreover, the effectiveness and statistical soundness of the derived cubic polynomial predictive models were verified by ANOVA.     

Comparative Studies of Selected Criteria Enabling Optimization of the Extraction of Polar Biologically Active Compounds from Alfalfa with Supercritical Carbon Dioxide

The aim of this research was to provide crucial and useful data about the selection of the optimization criteria of supercritical carbon dioxide extraction of alfalfa at a quarter-technical plant. The correlation between more general output, including total phenolics and flavonoids content, and a more specified composition of polar constituents was extensively studied. In all alfalfa extracts, polar bioactive constituents were analyzed by both spectrometric (general output) and chromatographic (detailed output) analyses. Eight specific phenolic acids and nine flavonoids were determined. The most dominant were salicylic acid (221.41 µg g⁻¹), ferulic acid (119.73 µg g⁻¹), quercetin (2.23 µg g⁻¹), and apigenin (2.60 µg g⁻¹). For all seventeen analyzed compounds, response surface methodology and analysis of variance were used to provide the optimal conditions of supercritical fluid extraction for each individual constituent. The obtained data have shown that eight of those compounds have a similar range of optimal process parameters, being significantly analogous for optimization based on total flavonoid content.     

Discrimination of Beers by Cyclic Voltammetry Using a Single Carbon Screen-printed Electrode

A fast, simple and costless methodology without sample pre-treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen-printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are submitted to partial least squares discriminant analysis (PLS−DA) and support vector machine discriminant analysis (SVM−DA), which allow a reasonable classification of the beers. Also, CV data from beers can be used to predict their alcoholic degree by partial least squares (PLS) and artificial neural networks (ANN). In general, non-linear methods provide better results than linear ones.     

基于(火积)理论的“+”形高导热构形通道实验研究

基于构形理论和■理论,对"+"形高导热通道的方形构造体开展导热实验研究,并对不同优化目标和不同高导热通道布置形式下的构造体导热性能进行比较.结果表明:对于"+"形高导热通道的方形构造体,实验和数值计算所得到的构造体最高温度点均位于"+"形高导热通道两分支之间,实验和数值计算所得到的构造体平均温差和■耗散率的误差均在可接受范围内,这从定性和定量的角度证明了导热构形优化结果的正确性.与"H"形高导热通道的方形构造体相比,构造体内高导热通道采用一级"+"形布置使得其导热■耗散率得到降低.■耗散率最小的一级"+"形高导热通道构造体最优构形与最大温差最小的构造体最优构形相比,前者的导热■耗散率降低了5.98%,但最大温差提高了3.57%.最大温差最小目标有助于提高构造体的热安全性,■耗散率最小目标有助于提高构造体的整体导热性能.在保证热安全性能的前提下,实际微电子器件设计中可采用■耗散率最小的构造体最优构形以提高其整体导热性能.