![]() SEM and AFM observations suggest that Zuotai is a kind of ancient nanodrug. EDX result shows that Zuotai contains Hg, S, O, Fe, Al, Cu, and other elements. Given this, energy dispersive spectrometry of X-ray (EDX), scanning electron microscopy (SEM), atomic force microscopy (AFM), and powder X-ray diffraction ( XRD) were used to assay the elements, micromorphology, and phase composition of nine Zuotai samples from different regions, respectively the XRD fingerprint features of Zuotai were analyzed by multivariate statistical analysis. However, little is known about the chemical substance basis of its pharmacodynamics and the intrinsic link of different samples sources so far. Zuotai ( gTso thal ) is one of the famous drugs containing mercury in Tibetan medicine. Li, Cen Yang, Hongxia Du, Yuzhi Xiao, Yuancan Zhandui Sanglao Wang, Zhang Ladan, Duojie Bi, Hongtao Wei, Lixin PMID:27738409Ĭhemical Species, Micromorphology, and XRD Fingerprint Analysis of Tibetan Medicine Zuotai Containing Mercury. The present research has revealed the physicochemical characteristics of Zuotai, and it would play a positive role in interpreting this mysterious Tibetan drug. XRD fingerprint analysis indicates that the similarity degrees of nine samples are very high, and the results of multivariate statistical analysis are broadly consistent with sample sources. XRD test shows that β-HgS, S8, and α-HgS are its main phase compositions. Its particles are mainly in the range of 100–800 nm, which commonly further aggregate into 1–30 μm loosely amorphous particles. Zuotai (gTso thal) is one of the famous drugs containing mercury in Tibetan medicine. ![]() Li, Cen Yang, Hongxia Xiao, Yuancan Zhandui Sanglao Wang, Zhang Ladan, Duojie Bi, Hongtao ), strip.Chemical Species, Micromorphology, and XRD Fingerprint Analysis of Tibetan Medicine Zuotai Containing Mercury table =TRUE, ylim = c ( 0, 500 ), xlab = 'Deg 2Theta', ylab = 'Counts', panel = function (x, y. Xyplot (value ~ x | treatment + horizon, data =xrd. long$treatment, c ( 'MG', 'MG+GLY', 'K', 'K 350', 'K 550' ) ) # change the strip background colors # (list(strip.background = list(col = grey(c(0.9,0.8)) ))) # plot the data along with some common d-spacings: var = c ( 'K', 'K 350', 'K 550', 'MG', 'MG+GLY' ), variable_name = 'treatment' ) # set a better ordering of the treatments # load libs require (lattice ) require ( reshape ) # read the composite data in as a table # format is 2theta,MG,MG+GLY,K,K350,K550 ![]()
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