将阿魏酸(ferulic acid,FA)与大豆分离蛋白(soy protein isolate,SPI)通过自由基法共价结合后制备葡萄糖酸内酯(gluconolactone,GDL)诱导的乳液凝胶,探究FA自由基法共价结合对SPI结构、乳液特性、乳液凝胶特性的影响。通过大豆分离蛋白...将阿魏酸(ferulic acid,FA)与大豆分离蛋白(soy protein isolate,SPI)通过自由基法共价结合后制备葡萄糖酸内酯(gluconolactone,GDL)诱导的乳液凝胶,探究FA自由基法共价结合对SPI结构、乳液特性、乳液凝胶特性的影响。通过大豆分离蛋白-阿魏酸(soy protein isolate-ferulic acid,SFA)乳液凝胶的分子间作用力、质构特性及持水性的分析,确定FA结合的最佳添加量为150μmol/g(相对于SPI质量)。在此条件下,光谱分析显示,FA对SPI有荧光猝灭作用,FA结合后SPI的β-折叠含量下降,α-螺旋含量、β-转角含量和无规卷曲含量上升。SFA乳液的Zeta电位绝对值、界面蛋白含量增大,平均粒径、表观黏度减小。SFA乳液凝胶的终存储模量(G’)升高,低场核磁共振测定中弛豫时间和峰比例变化表明SFA乳液凝胶水合特性更佳;SFA乳液凝胶具有更加均匀致密的多孔网络结构。结果显示,经150μmol/gFA自由基法共价结合的SPI具有制备乳液凝胶的应用价值。展开更多
Searching and designing new materials play crucial roles in the development of energy storage devices. In today's world where machine learning technology has shown strong predictive ability for various tasks, the ...Searching and designing new materials play crucial roles in the development of energy storage devices. In today's world where machine learning technology has shown strong predictive ability for various tasks, the combination with machine learning technology will accelerate the process of material development. Herein, we develop ESM Cloud Toolkit for energy storage materials based on Mat Elab platform, which is designed as a convenient and accurate way to automatically record and save the raw data of scientific research. The ESM Cloud Toolkit includes multiple features such as automatic archiving of computational simulation data, post-processing of experimental data, and machine learning applications. It makes the entire research workflow more automated and reduces the entry barrier for the application of machine learning technology in the domain of energy storage materials. It integrates data archive, traceability, processing, and reutilization, and allows individual research data to play a greater role in the era of AI.展开更多
文摘将阿魏酸(ferulic acid,FA)与大豆分离蛋白(soy protein isolate,SPI)通过自由基法共价结合后制备葡萄糖酸内酯(gluconolactone,GDL)诱导的乳液凝胶,探究FA自由基法共价结合对SPI结构、乳液特性、乳液凝胶特性的影响。通过大豆分离蛋白-阿魏酸(soy protein isolate-ferulic acid,SFA)乳液凝胶的分子间作用力、质构特性及持水性的分析,确定FA结合的最佳添加量为150μmol/g(相对于SPI质量)。在此条件下,光谱分析显示,FA对SPI有荧光猝灭作用,FA结合后SPI的β-折叠含量下降,α-螺旋含量、β-转角含量和无规卷曲含量上升。SFA乳液的Zeta电位绝对值、界面蛋白含量增大,平均粒径、表观黏度减小。SFA乳液凝胶的终存储模量(G’)升高,低场核磁共振测定中弛豫时间和峰比例变化表明SFA乳液凝胶水合特性更佳;SFA乳液凝胶具有更加均匀致密的多孔网络结构。结果显示,经150μmol/gFA自由基法共价结合的SPI具有制备乳液凝胶的应用价值。
基金supported by the National Natural Science Foundation of China (Grant Nos. 52022106 and 52172258)the Informatization Plan of Chinese Academy of Sciences (Grant No. CASWX2021SF-0102)。
文摘Searching and designing new materials play crucial roles in the development of energy storage devices. In today's world where machine learning technology has shown strong predictive ability for various tasks, the combination with machine learning technology will accelerate the process of material development. Herein, we develop ESM Cloud Toolkit for energy storage materials based on Mat Elab platform, which is designed as a convenient and accurate way to automatically record and save the raw data of scientific research. The ESM Cloud Toolkit includes multiple features such as automatic archiving of computational simulation data, post-processing of experimental data, and machine learning applications. It makes the entire research workflow more automated and reduces the entry barrier for the application of machine learning technology in the domain of energy storage materials. It integrates data archive, traceability, processing, and reutilization, and allows individual research data to play a greater role in the era of AI.
