Pipette‐tip solid‐phase extraction based on deep eutectic solvent modified graphene for the determination of sulfamerazine in river water

A green and novel deep eutectic solvent modified graphene was prepared and used as a neutral adsorbent for the rapid determination of sulfamerazine in a river water sample by pipette‐tip solid‐phase extraction. Compared with conventional graphene, deep eutectic solvent modified graphene can change the surface of graphene with wrinkled structure and higher selective extraction ability. The properties of deep eutectic solvent modified graphene and graphene were characterized by scanning electron microscopy, Fourier transform infrared spectroscopy, and thermogravimetric analysis. Static adsorption showed deep eutectic solvent modified graphene had a higher adsorption ability (18.62 mg/g) than graphene. Under the optimum conditions, factors such as kinds of washing solvents and elution solvents and volume of elution solvent were evaluated. The limits of detection and quantification were 0.01 and 0.03 μg/mL, respectively. The method recoveries of sulfamerazine were in the range of 91.01–96.82% with associated intraday relative standard deviations ranging from 1.63 to 3.46% and interday relative standard deviations ranging from 0.68 to 3.84%. Deep eutectic solvent modified graphene showed satisfactory results (recovery was 95.38%) and potential for rapid purification of sulfamerazine in river water sample in combination with the pipette‐tip solid‐phase extraction method.     

Ternary choline chloride/caffeic acid/ethylene glycol deep eutectic solvent as both a monomer and template in a molecularly imprinted polymer

A molecularly imprinted polymer based on a ternary deep eutectic solvent comprised of choline chloride/caffeic acid/ethylene glycol was prepared. The caffeic acid in the ternary deep eutectic solvent was used as both a monomer and template. The molecularly imprinted polymer based on the ternary deep eutectic solvent was characterized by Fourier transform infrared spectroscopy, thermogravimetric analysis, field‐emission scanning electron microscopy, Brunauer–Emmett–Teller surface area analysis, atomic force microscopy, and elemental analysis. A series of molecularly imprinted polymers based on choline chloride/caffeic acid/ethylene glycol with different molar ratios was prepared and applied to the molecular recognition of polyphenols. A comparison of the recognition ability of molecularly imprinted polymers to polyphenols revealed that the choline chloride/caffeic acid/ethylene glycol (1:0.4:1, molar ratio) molecularly imprinted polymer had the best molecular recognition effect with 132 μg/g of protocatechuic acid, 104 μg/g of catechins, 80 μg/g of epicatechin, and 123 μg/g of caffeic acid in 6 h, as well as good molecular recognition ability for polyphenols from a Radix Asteris sample. These results show that the ternary deep eutectic solvent based molecularly imprinted polymer is a potential medium that can be applied to drug purification, drug delivery, and drug analysis.     

Exploration of a ternary deep eutectic solvent of methyltriphenylphosphonium bromide/chalcone/formic acid for the selective recognition of rutin and quercetin in Herba Artemisiae Scopariae

Methyltriphenylphosphonium bromide/chalcone/formic acid, a green ternary deep eutectic solvent, was applied as a functional monomer and dummy template simultaneously in the synthesis of a new molecularly imprinted polymer. Ternary deep eutectic solvent based molecularly imprinted polymers are used as a solid‐phase extraction sorbent in the separation and purification of rutin and quercetin from Herba Artemisiae Scopariae combined with high‐performance liquid chromatography. Fourier transform infrared spectroscopy and field‐emission scanning electron microscopy were applied to characterize the deep eutectic solvent based molecularly imprinted polymers synthesized using different molar ratios of chalcone. The static and competitive adsorption tests were performed to examine the recognition ability of the molecularly imprinted polymers to rutin and quercetin. The ternary deep eutectic solvent consisting of formic acid/chalcone/methyltriphenylphosphonium bromide (1:0.05:0.5) had the best molecular recognition effect. After optimization of the washing solvents (methanol/water, 1:9) and eluting solvents (acetonitrile/acetic acid, 9:1), a reliable analytical method was developed for strong recognition towards rutin and quercetin in Herba Artemisiae Scopariae with satisfactory extraction recoveries (rutin: 92.48%, quercetin: 94.23%). Overall, the chalcone ternary deep eutectic solvent‐based molecularly imprinted polymer coupled with solid‐phase extraction is an effective method for the selective purification of multiple bioactive compounds in complex samples.     

Microwave-Assisted One-Pot Lipid Extraction and Glycolipid Production from Oleaginous Yeast Saitozyma podzolica in Sugar Alcohol-Based Media

Glycolipids are non-ionic surfactants occurring in numerous products of daily life. Due to their surface-activity, emulsifying properties, and foaming abilities, they can be applied in food, cosmetics, and pharmaceuticals. Enzymatic synthesis of glycolipids based on carbohydrates and free fatty acids or esters is often catalyzed using certain acyltransferases in reaction media of low water activity, e.g., organic solvents or notably Deep Eutectic Systems (DESs). Existing reports describing integrated processes for glycolipid production from renewables use many reaction steps, therefore this study aims at simplifying the procedure. By using microwave dielectric heating, DESs preparation was first accelerated considerably. A comparative study revealed a preparation time on average 16-fold faster than the conventional heating method in an incubator. Furthermore, lipids from robust oleaginous yeast biomass were successfully extracted up to 70% without using the pre-treatment method for cell disruption, limiting logically the energy input necessary for such process. Acidified DESs consisting of either xylitol or sorbitol and choline chloride mediated the one-pot process, allowing subsequent conversion of the lipids into mono-acylated palmitate, oleate, linoleate, and stearate sugar alcohol esters. Thus, we show strong evidence that addition of immobilized Candida antarctica Lipase B (Novozym 435^®), in acidified DES mixture, enables a simplified and fast glycolipid synthesis using directly oleaginous yeast biomass.     

Solubility and Stability of Some Pharmaceuticals in Natural Deep Eutectic Solvents-Based Formulations

Some medicines are poorly soluble in water. For tube feeding and parenteral administration, liquid formulations are required. The discovery of natural deep eutectic solvents (NADES) opened the way to potential applications for liquid drug formulations. NADES consists of a mixture of two or more simple natural products such as sugars, amino acids, organic acids, choline/betaine, and poly-alcohols in certain molar ratios. A series of NADES with a water content of 0–30% (w/w) was screened for the ability to solubilize (in a stable way) some poorly water-soluble pharmaceuticals at a concentration of 5 mg/mL. The results showed that NADES selectively dissolved the tested drugs. Some mixtures of choline-based NADES, acid-neutral or sugars-based NADES could dissolve chloral hydrate (dissociated in water), ranitidine·HCl (polymorphism), and methylphenidate (water insoluble), at a concentration of up to 250 mg/mL, the highest concentration tested. Whereas a mixture of lactic-acid–propyleneglycol could dissolve spironolacton and trimethoprim at a concentration up to 50 and 100 mg/mL, respectively. The results showed that NADES are promising solvents for formulation of poorly water-soluble medicines for the development of parenteral and tube feeding administration of non-water-soluble medicines. The chemical stability and bioavailability of these drug in NADES needs further studies.     

One-pot rapid synthesis of 4H-1-benzopyran derivatives in a deep eutectic solvent

A method for introducing a biologically active heterocycle, 2-methylquinoline into the 4-position of a 2-amino-4H-1-benzopyran skeleton is described. Choline chloride/glucose (1:1 molar ratio) was used as both the solvent and catalyst in the reaction of a salicylaldehyde, methylquinoline, and cyanoacetate to obtain 2-amino-4H-1-benzopyran derivatives in 48%–80% yields after short reaction times. The effects of the deep eutectic solvent type, substrate molar ratio, cosolvent, temperature, and reaction time were examined. The method has the advantages of simple steps, environmental friendliness, mild conditions, and wide substrate applicability. This is the first attempt to synthesize methylquinoline derivatives of 4H-1-benzopyran.     

促进深度学习的专题复习教学*——以“碳及其化合物”为例

以促进学生的深度学习为基本理念,围绕“自然界的碳循环”“大气中CO₂的控减排”“化学家们合成的新型碳家族成员”等板块,设计并成功实施系列情境问题,在系列问题的讨论、分析、解决过程中帮助学生建构“碳”家族成员间相互转化的知识网络,提高学生思维的纵深度,引发高阶思维,促进深度学习。     

An Electrocardiogram Signal Classification Algorithm Based on Improved Deep Residual Shrinkage Networks北大核心CSCD

心电信号分类是医疗保健领域的重要研究内容.针对大多数方法不能很好地降低样本数量少的类别漏诊率,以及降低预处理操作的复杂性问题,提出了一种基于改进深度残差收缩网络(IDRSN)的心电信号分类算法(即DRSL算法).首先,使用合成少数类过采样技术(SMOTE)扩充数量少的类别样本,从而解决了类不平衡问题;其次,利用改进深度残差收缩网络提取空间特征,其残差模块可以避免网络层加深造成的过拟合,压缩激励和软阈值化子网络可以提取重要局部特征并自动去除噪声;然后,通过长短期记忆网络(LSTM)提取时间特征;最后,利用全连接网络输出分类结果.在MIT-BIH心律失常数据集上的实验结果表明,该算法的分类性能优于IDRSN、DRSN、GAN+2DCNN、CNN+LSTM_ATTENTION、SE-CNN-LSTM分类算法.     

Quantum Bayesian computation

Quantum Bayesian computation is an emerging field that levers the computational gains available from quantum computers. They promise to provide an exponential speed-up in Bayesian computation. Our article adds to the literature in three ways. First, we describe how quantum von Neumann measurement provides quantum versions of popular machine learning algorithms such as Markov chain Monte Carlo and deep learning that are fundamental to Bayesian learning. Second, we describe quantum data encoding methods needed to implement quantum machine learning including the counterparts to traditional feature extraction and kernel embeddings methods. Third, we show how quantum algorithms naturally calculate Bayesian quantities of interest such as posterior distributions and marginal likelihoods. Our goal then is to show how quantum algorithms solve statistical machine learning problems. On the theoretical side, we provide quantum versions of high dimensional regression, Gaussian processes and stochastic gradient descent. On the empirical side, we apply a quantum FFT algorithm to Chicago house price data. Finally, we conclude with directions for future research.     

基于深度学习的材料超声回波衰减预测方法

材料超声回波衰减是评价材料均匀一致性的常用方法, 针对具有复杂结构的航空发动机盘件难以进行材料底面超声回波衰减评价的问题, 本文提出了利用超声背散射波信号直接预测底面回波衰减的方法。采用10MHz聚焦探头进行超声背散射波数据的采集, 利用深度学习技术构建和训练模型,建立了基于深度学习的材料底面回波衰减预测方法, 同时讨论了采用不同信号形式的超声波信号分类识别模型的准确率差异。研究发现：基于深度学习技术可实现通过超声背散射波预测材料的底面回波衰减, 预测结果和实际底面回波衰减试验结果具有良好的一致性。