Deep-Eutectic-Solvent-Based Self-Healing Polymer Electrolyte for Safe and Long-Life Lithium-Metal Batteries

The deployment of high-energy-density lithium-metal batteries has been greatly impeded by Li dendrite growth and safety concerns originating from flammable liquid electrolytes. Herein, we report a stable quasi-solid-state Li metal battery with a deep eutectic solvent (DES)-based self-healing polymer (DSP) electrolyte. This electrolyte was fabricated in a facile manner by in situ copolymerization of 2-(3-(6-methyl-4-oxo-1,4-dihydropyrimidin-2-yl)ureido)ethyl methacrylate (UPyMA) and pentaerythritol tetraacrylate (PETEA) monomers in a DES-based electrolyte containing fluoroethylene carbonate (FEC) as an additive. The well-designed DSP electrolyte simultaneously possesses non-flammability, high ionic conductivity and electrochemical stability, and dendrite-free Li plating. When applied in Li metal batteries with a LiMn₂O₄ cathode, the DSP electrolyte effectively suppressed manganese dissolution from the cathode and enabled high-capacity and a long lifespan at room and elevated temperatures.     

深共融溶剂中合成2,4-二取代喹啉衍生物

在氯化胆碱和氯化锌组成的深共融溶剂中,以2-氨基苯乙酮和芳香炔烃为原料,通过环化偶联反应,合成了一系列2,4-二取代喹啉衍生物;当n(氯化胆碱)∶n(氯化锌)=1∶2,反应温度为80℃时,反应3 h即获得高达98%的产率.该方法无需额外添加催化剂,而且反应条件温和、操作简单、底物范围较广泛.     

Metal-free and base-free decarboxylation of substituted cinnamic acids in a deep eutectic solvent

Abstract

A metal-free and base-free strategy was developed in DES to synthesize styrenes for the first time by decarboxylation of cinnamic acid derivatives, which provided a renewable and cost efficiently protocol to access various styrenes including those with functional groups such as 4-vinylphenol and 1-chloro-4-vinylbenzene.     

深共熔溶剂-高效液相色谱联用提取测定环境水样中的3种药品和个人护理品

建立了深共熔溶剂-高效液相色谱联用提取测定环境水样中3种药品和个人护理品(PPCPs)的方法。通过优化前处理条件,3种PPCPs(氯霉素、氯苯甘醚和萘普生)利用氯化胆碱-乙二醇深共熔溶剂为提取剂,经超声功率120 W下超声波提取5 min,离心转速9000 r/min下离心10 min富集提取。采用外标法定量分析,在5.0~200.0 mg/L范围内线性关系良好,相关系数r≥0.9998。3种环境水样中PPCPs的回收率为81.4%~94.8%,相对标准偏差分别为1.5%,0.4%和0.3%。氯霉素、氯苯甘醚和萘普生的方法检出限(LODs)分别为0.9,3.3,1.6 mg/L,定量限(LOQs)分别为3.1,12.2,5.0 mg/L。方法能够满足环境水样中3种PPCPs的检测需求。     

人工智能助力当代化学研究

以机器学习为代表的人工智能在当代的科学研究中正在发挥越来越重要的作用.不同于传统的计算机程序,机器学习人工智能可以通过对大量数据的反复分析和自身模型的优化,即“学习”过程,从而在大量的数据中寻找客观事物的相互联系,形成具有更好预测和决策能力的新模型,做出合理的判断.化学研究的特点恰恰是机器学习人工智能的强项.化学研究经常要面对十分复杂的物质体系和实验过程,从而很难通过化学物理原理进行精准的分析和判断.人工智能可以挖掘化学实验中产生的海量实验数据的相关性,帮助化学家做出合理分析预测,大大加速化学研发过程.本文介绍了当代人工智能方法及用其解决化学问题基本原理,并通过具体案例展示了人工智能辅助解决不同化学研发问题的方法以及对应的机器学习算法.将人工智能运用在化学科学的尝试正处于蓬勃上升期,人工智能已经初步展示出对化学研究的强大助力,希望本文能帮助更多的国内的化学工作者了解和运用这一有力的工具.     

Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental times. We present a proof‐of‐concept of the application of deep learning and neural networks for high‐quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signals, which lifts the prohibiting demand for a large volume of realistic training data usually required for a deep learning approach.     

A modified quick,easy, cheap,effective, rugged,and safe method with hydrophobic natural deep eutectic solvent as extractant and analyte protectant for analyzing pyrethroid residues in tomatoes

In this work, a novel quick, easy, cheap, effective, rugged, and safe technique with hydrophobic natural deep eutectic solvent as both extractant and analyte protectant was developed and combined with gas chromatography–tandem mass spectrometry to analyze pyrethroid residues in tomatoes. Eight hydrophobic natural deep eutectic solvents were first evaluated as analyte protectants and those with decanoic acid or lactic acid as hydrogen bond donor were demonstrated to be effective in compensating for the matrix effects of pyrethroids in the gas chromatography system. Hence, they were added to solvent standards for correcting the quantitation errors instead of matrix‐matched calibration standards. Then the abilities of these acid‐based deep eutectic solvents to extract pyrethriods from tomatoes were evaluated. Results showed the recoveries of all pyrethroids reached to over 80% with only 5 mL menthol:decanoic acid (1:1) used, and good phase separation was easily achieved without the addition of inorganic salt in the extraction step, indicating hydrophobic natural deep eutectic solvent could be a green substitute for acetonitrile in the quick, easy, cheap, effective, rugged, and safe extraction. Compared with the conventional method, the proposed protocol improved the recoveries, reduced the matrix effects, and simplified the extraction step, demonstrating to be an effective, fast, and green method.     

A three‐phase solvent extraction system combined with deep eutectic solvent‐based dispersive liquid–liquid microextraction for extraction of some organochlorine pesticides in cocoa samples prior to gas chromatography with electron capture detection

A sample pretreatment method based on the combination of a three‐phase solvent extraction system and deep eutectic solvent‐based dispersive liquid–liquid microextraction has been introduced for the extraction of four organochlorine pesticides in cocoa samples before their determination by gas chromatography‐electron capture detection. A mixture of sodium chloride, acetonitrile, and potassium hydroxide solution is added to cocoa bean or powder. After vortexing and centrifugation of the mixture, the collected upper phase (acetonitrile) is removed and mixed with a few microliters of N,N‐diethanol ammonium chloride: pivalic acid deep eutectic solvent. Then it is rapidly injected into deionized water and a cloudy solution is obtained. Under optimum conditions, the limits of detection and quantification were found to be 0.011‐0.031 and 0.036‐0.104 ng/g, respectively. The obtained extraction recoveries varied between 74 and 92%. Also, intra‐ (n = 6) and interday (n = 4) precisions were less than or equal to 7.1% for the studied pesticides at a concentration of 0.3 ng/g of each analyte. The suggested method was applied to determine the studied organochlorine pesticide residues in various cocoa powders and beans gathered from groceries in Tabriz city (Iran) and aldrin and dichlobenil were found in some of them.     

Combining convolutional neural networks and in‐line near‐infrared spectroscopy for real‐time monitoring of the chromatographic elution process in commercial production of notoginseng total saponins

The chromatographic elution process is a key step in the production of notoginseng total saponins. Due to quality variability of loading samples and resin capacity decreasing over cycle time, saponins, especially the five main saponins of notoginseng total saponins, need to be monitored in real time during the elution process. In this study, convolutional neural networks, one of the most popular deep learning methods, were used to develop quantitative calibration models based on in‐line near‐infrared spectroscopy for notoginsenoside R₁, ginsenosides Rg₁, Re, Rb₁ and Rd, and their sum concentration, with root mean square error of prediction values of 0.87, 2.76, 0.60, 1.57, 0.28, and 4.99 mg/mL, respectively. Partial least squares calibration models were also developed for model performance comparison. Results show predicted concentration profiles outputted by both the convolutional neural network models and partial least squares models show agreements with the real trends defined by reference measurements, and can be used for elution process monitoring and endpoint determination. To the best of our knowledge, this is the first reported case study of combining convolutional neural networks and in‐line near‐infrared spectroscopy for monitoring of the chromatographic elution process in commercial production of botanical drug products.     

Development of a stir bar sorptive extraction method coupled to solidification of floating droplets dispersive liquid–liquid microextraction based on deep eutectic solvents for the extraction of acidic pesticides from tomato samples

A stir bar sorptive extraction method coupled with deep eutectic solvent based solidification of floating organic droplets–dispersive liquid–liquid microextraction has been used for the simultaneous derivatization and extraction of some acidic pesticides in tomato samples. In this method, initially the analytes are adsorbed on a coated stir bar from tomato juice filled in a narrow tube. After extraction, the stir bar is removed and a water–miscible deep eutectic solvent is used to elute the analytes. Afterward, a derivatization agent and a water–immiscible deep eutectic solvent (as an extraction solvent) with melting point near to room temperature are added to the obtained eluant at µL–levels and the obtained mixture is rapidly injected into deionized water. Under the optimum conditions, the introduced method indicated high enhancement (1543–3353) and enrichment (2530–2999) factors, low limits of detection (7–14 ng/L) and quantification (23–47 ng/L), good linearity (r² ≥ 0.9982), and satisfactory repeatabilities (relative standard deviation ≤12% for intra– and inter–day precisions at a concentration of 100 ng/L of each analyte). Finally, the proposed method was applied in analysis of the analytes in tomato samples.