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.     

Influence of Ga/(Ga + In) grading on deep‐defect states of Cu(In,Ga)Se2 solar cells

The benefits of gallium (Ga) grading on Cu(In,Ga)Se₂ (CIGS) solar cell performance are demonstrated by comparing with ungraded CIGS cells. Using drive‐level capacitance profiling (DLCP) and admittance spectroscopy (AS) analyses, we show the influence of Ga grading on the spatial variation of deep defects, free‐carrier densities in the CIGS absorber, and their impact on the cell's open‐circuit voltage V_oc. The parameter most constraining the cell's V_oc is found to be the deep‐defect density close to the space charge region (SCR). In ungraded devices, high deep‐defect concentrations (4.2 × 10¹⁶cm^–3) were observed near the SCR, offering a source for Shockley–Read–Hall recombination, reducing the cell's V_oc. In graded devices, the deep‐defect densities near the SCR decreased by one order of magnitude (2.5 × 10¹⁵ cm^–3) for back surface graded devices, and almost two orders of magnitude (8.6 × 10¹⁴ cm^–3) for double surface graded devices, enhancing the cell's V_oc. In compositionally graded devices, the free‐carrier density in the absorber's bulk decreased in tandem with the ratio of gallium to gallium plus indium ratio GGI = Ga/(Ga + In), increasing the activation energy, hindering the ionization of the defect states at room temperature and enhancing their role as recombination centers within the energy band. (© 2015 WILEY‐VCH Verlag GmbH &Co. KGaA, Weinheim)     

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.     

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。     

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.