The Ostruthin was extracted and identified its structure from rhizomes of Luvunga scandens located at Ta Cu Mountain, Binh Thuan province, Vietnam for the first time. Gold nanoparticles (AuNPs) were synthesized by the green method. The AuNPs acted as antitumor against breast cancer cell line (MCF-7), human liver cancer cell line (HepG2), and Non-Small Cell Lung (NCI-H460). They showed the potential antitumor activity against MCF-7 with the IC50 value of 65.47�3.09 �M. The antitumor activity of the AuNPs was also compared with the extracted constituents from the root of Luvunga Scandens in a previous article. The AuNPs were exposed to high antitumor activity against MCF-7 and Hep G2, human cancer cell lines. The AuNPs have also been tested the antibacterial activity and shown the moderate antibacterial activity on both Salmonella enterica and Bacillus subtilis at a concentration of 0.25 mM.
In this study, the stability constants (logβ11) of twenty-eight new complexes between several ion metals and thiosemicarbazone ligands were predicted on the basis of the quantitative structure property relationship (QSPR) modeling. The stability constants were calculated from the results of the QSPR models. The QSPR models were built by using the multivariate least regression (QSPRMLR) and artificial neural network (QSPRANN). The molecular descriptors, physicochemical and quantum descriptors of complexes were generated from molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The best linear model QSPRMLR involves five descriptors, namely Total energy, xch6, xp10, SdsN, and Maxneg. The quality of the QSPRMLR model was validated by the statistical values that were R2train = 0.860, Q2LOO = 0.799, SE = 1.242, Fstat = 54.14 and PRESS = 97.46. The neural network model QSPRANN with architecture I(5)-HL(9)-O(1) was presented with the statistical values: R2train = 0.8322, Q2CV = 0.9935 and Q2test = 0.9105. Also, the QSPR models were evaluated externally and achieved good performance results with those from the experimental literature. In addition, the results from the QSPR models could be used to predict the stability constants of other new metal-thiosemicarbazones.
The stability constants (logβ11) of forty-two new metal-thiosemicarbazone complexes were predicted based on the results of the quantitative structure-property relationship (QSPR). The QSPR models were developed from 88 logb11 values of experimental complexes by using the multivariate linear regression (QSPRMLR) and artificial neural network (QSPRANN). Four descriptors such as xch9, xv0, core-core repulsion and cosmo area were found out in the best of the linear model QSPRMLR which was harshly evaluated by the statistical values: R2train = 0.864, Q2LOO = 0.840, SE = 0.711, Fstat = 131,355 and PRESS = 49.31. Furthermore, the artificial neural network model QSPRANN with architecture I(4)-HL(5)-O(1) was discovered with the same variables of the QSPRMLR model that the statistical results were extremely impressive as R2train = 0.970, Q2CV = 0.984 and Q2test = 0.974. Also, both of the QSPR models were externally validated on the data set of 18 logb11 values of independently experimental complexes. As a consequence, the results from the QSPR models could be used to calculate the stability constants of other new metal-thiosemicarbazones.
Introduction: Understanding the fractions of lignin is important for further conversion of lignin into valuable products. Herein, the “home-made” lignin from Earleaf Acacia tree was extracted by sequential industrial organic solvent and characterized each fraction to reveal its properties for further catalytic applications. Methods: In this work, lignin was prepared from the Earleaf Acacia tree using the soda method. Then, the prepared lignin was fractionated by sequential solvents of ethyl acetate, ethanol, methanol, and acetone. Each lignin fractions were characterized by FT-IR and GPC. Results: The FT-IR results confirmed the soda method can produce lignin from woodchips. The fractionation of lignin separated the lignin mixture into different molecular weight fraction from light – medium into heavy compounds. Conclusion: Lignin was produced from woodchips using the soda method successfully. The fractionation using the sequential organic solvents showed the separation of different molecular weight of lignin, which allow to apply for the further conversion into useful products.
Lignin is one of main components of lignocellulosic along with cellulose and hemicellulose. It is a by-product of the paper and pulp industry, and has aromatic backbones making them an ideal renewable feedstock of aromatic compounds for a range of applications. Catalytic conversion of lignin from Earleaf Acacia tree was performed using high pressure/temperature reactor with Ru/C catalyst and protic solvents. The results showed that the conversion of lignin depends on the solvent polarity of protic solvents, and Ru/C catalyst enhanced the lignin conversion. Phenolic compounds are the main components of lignin conversion. Those compounds can be applied as a basement for bulk chemical and fuels.
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