A gadolinium-based magnetic ionic liquid for supramolecular dispersive liquid–liquid microextraction followed by HPLC/UV for the determination of favipiravir in human plasma

Favipiravir is a potential antiviral medication that has been recently licensed for Covid-19 treatment. In this work, a gadolinium-based magnetic ionic liquid was prepared and used as an extractant in dispersive liquid–liquid microextraction (DLLME) of favipiravir in human plasma. The high enriching ability of DLLME allowed the determination of favipiravir in real samples using HPLC/UV with sufficient sensitivity. The effects of several variables on extraction efficiency were investigated, including type of extractant, amount of extractant, type of disperser and disperser volume. The maximum enrichment was attained using 50 mg of the Gd-magnetic ionic liquid (MIL) and 150 μl of tetrahydrofuran. The Gd-based MIL could form a supramolecular assembly in the presence of tetrahydrofuran, which enhanced the extraction efficiency of favipiravir. The developed method was validated according to US Food and Drug Administration bioanalytical method validation guidelines. The coefficient of determination was 0.9999, for a linear concentration range of 25 to 1.0 × 10⁵ ng/ml. The percentage recovery (accuracy) varied from 99.83 to 104.2%, with RSD values (precision) ranging from 4.07 to 11.84%. The total extraction time was about 12 min and the HPLC analysis time was 5 min. The method was simple, selective and sensitive for the determination of favipiravir in real human plasma.     

Development and validation of LC–MS/MS method for amlexanox in rat plasma and its application in preclinical pharmacokinetics

Amlexanox, an anti-inflammatory and anti-allergic agent, has been widely used clinically for the treatment of canker sores, asthma, and allergic rhinitis. Recently, amlexanox has received considerable attention in curing nonalcoholic fatty liver diseases and hepatitis virus infection. Herein, we first established a sensitive high-performance liquid chromatography-tandem mass spectrum (LC–MS/MS) method for the determination of amlexanox in rat plasma. Propranolol was used as the internal standard (IS). Using a simple protein precipitation method, the amlexanox and IS were separated with Capcell Pak C18 column (2.0 × 50 mm, 5 μm) and eluted with water and acetonitrile each containing 0.1% formic acid using gradient elution condition at a flow rate of 0.4 mL·min⁻¹. Amlexanox and IS were detected by a triple quadrupole mass in multiple reactive monitoring (MRM) under the transitions of m/z 299.2 → 281.2 and m/z 259.9 → 116.1 with positive electrospray ionization, respectively. The calibration curves of amlexanox were established with the range of 50 to 2000 ng·mL⁻¹ (r² > 0.99). The validation method consisted of selectivity, accuracy, precision, carryover effect, matrix effect, recovery, dilution effect, and stability. The fully validated method was successfully applied to the pharmacokinetic study of amlexanox in Wistar rats.     

Insights into MWCNTs/polyimide nanocomposites: from synthesis to application as free-standing flexible electrodes in low-cost micro-supercapacitors

In the pursuit to enlarge the library of polyimide materials for energy applications, new polyimide/MWCNTs composite films have been developed by MWCNTs-assisted polycondensation reaction of a hydroxyl and triphenylmethane-containing diamine with benzophenone tetracarboxylic dianhydride targeting to highlight their electrical storage capability as flexible electrodes in micro-supercapacitors (mSCs). The Fourier-transform infrared spectroscopy, proton nuclear magnetic resonance, UV–vis, fluorescence, and Raman spectroscopies were used to demonstrate the evolution of interfacial interactions between MWCNTs and the precursors (diamine monomer and intermediate polyamidic acid) and polyimide matrix that proved to be the origin of MWCNTs homogeneous dispersion. Thus, composite films incorporating 1, 3, 5, and 10 w.t.% MWCNTs were obtained and thoroughly investigated with regard to their morphology, mechanical behavior, thermal stability, and electrical conductivity. The electrochemical performance of these composites was first analyzed in a classical three-electrode cell by cyclic voltammetry and galvanostatic charge-discharge in both aqueous and organic electrolyte systems. By far, the best electrical storage capacity was obtained with the composite polyimide film containing 10% MWCNTs that was further used as both active material and current collector in a flexible symmetric mSC realized by a straightforward and low-cost procedure. In the attempt to better exploit the advantages of this composite film, it was layered with a graphite-containing paint and tested as an electrode in a flexible mSC, which provided satisfactory results. To our knowledge, this is the first report on the electrical charge storage capability of a polyimide/MWCNTs free-standing film as a flexible electrode in mSCs, which do not require time- and resource-consuming processing steps.     

突出矿井瓦斯抽放限制影响因素的AHP-熵权法评价模型

为提高煤与瓦斯突出矿井瓦斯抽放效果,建立了3个一级指标、14个二级指标的突出矿井瓦斯抽放限制影响因素评价指标体系,利用AHP和熵权法分别确定指标因子主、客观权重.通过实地调研分析和反馈验证了AHP-熵权法的可行性和正确性,利用加权平均法确定评价模型的综合权重.研究表明:封孔方式、钻孔半径、抽放时间、煤体裂隙发育程度和抽放负压是目前影响煤矿瓦斯抽放效果的主控因素.     

基于不确定控制理论的最优策略研究

本文在不确定理论的框架下,研究一类带背景状态变量的最优控制模型.在乐观值准则下,利用不确定动态规划的方法,证明了不确定最优性原则,得到最优性方程.作为应用,求解一个固定缴费(DC)型养老金的最优投资策略问题,在乐观值准则下,以工资变量为背景状态变量,建立养老金模型.通过求解不确定最优性方程得到最优投资策略和最优支付率.     

基于GA_XGBoost模型的大学生科研能力预测问题研究

科学评价大学生科研创新能力对我国科研水平的提高具有重要意义.采用机器学习模型来预测大学生科研能力可以起到良好的效果,提出一种GAXGBoost模型来实现对大学生的科研能力预测.此模型是以Xgboost算法为基础,然后充分利用遗传算法的全局搜索能力自动搜索Xgboost最优超参数,避免了人为经验调参不准确的缺陷,最后采用精英选择策略以此确保每一轮都是最佳的进化结果.通过分析表明,所采用的GAXGBoost模型在大学生科研能力预测的结果中具有很高的精度,将此模型与Logistic Regression、Random Forest、SVM等模型进行对比,GAXGBoost模型的预测精度最高.     

模具结构对连续纤维增强热塑性复合材料性能的影响

采用自行设计的两种不同结构的熔融浸渍模具制备了连续玻璃纤维增强聚丙烯预浸带，测试了模具结构对预浸带的孔隙率、纤维断裂率、界面形貌、纤维分散均匀度和拉伸强度的影响，建立了纤维浸渍模型和纤维断裂模型，并通过理论模型对预浸带的孔隙率和断裂率进行理论预测。结果表明，本文建立的数学模型能够有效预测预浸带的浸渍程度和纤维断裂率，可用于浸渍模具结构的优化设计；在本文范围内，与波浪形模具相比，斜齿形模具的多楔形区结构可以有效地降低预浸带孔隙率和提升纤维分散程度；波浪形模具的流道圆角半径较大，楔形区个数较少，与斜齿形模具相比，可有效降低纤维断裂率并提升拉伸性能。     

共享自动驾驶汽车使用意愿模型及其影响因素

为了构建共享自动驾驶汽车(shared autonomous vehicles，SAV) 的使用意愿模型并分析其影响因素，通过使用时机和使用频率来体现公众对SAV的使用意愿；采用验证性因子分析将态度潜变量转化为潜变量得分，以此将态度潜变量引入传统的有序Logit模型；提出SAV使用意愿的有序Logit模型构建方法，并建立基于个人属性、通勤特征、态度潜变量的SAV使用意愿模型.研究发现:是否使用过滴滴拼车、工作单位停车费、自动驾驶态度对使用时机和3种价格(1，2，3元/km) 下的使用频率有显著影响；使用过滴滴拼车、工作单位收取停车费、支持自动驾驶的群体倾向于频繁地、更早地使用SAV出行；公众期望以1元/km或 2元/km即不超过目前滴滴拼车的价格使用SAV.