MnOx/carbon nanotube/reduced graphene oxide nanohybrids as high-performance supercapacitor electrodes

Nanohybrids consisting of both carbon and pseudocapacitive metal oxides are promising as high-performance electrodes to meet the key energy and power requirements of supercapacitors. However, the development of high-performance nanohybrids with controllable size, density, composition and morphology remains a formidable challenge. Here, we present a simple and robust approach to integrating manganese oxide (MnOx) nanoparticles onto flexible graphite paper using an ultrathin carbon nanotube/reduced graphene oxide (CNT/RGO) supporting layer. Supercapacitor electrodes employing the MnOx/CNT/RGO nanohybrids without any conductive additives or binders yield a specific capacitance of 1070 F g−1 at 10 mV s−1, which is among the highest values reported for a range of hybrid structures and is close to the theoretical capacity of MnOx. Moreover, atmospheric-pressure plasmas are used to functionalize the CNT/RGO supporting layer to improve the adhesion of MnOx nanoparticles, which results in theimproved cycling stability of the nanohybrid electrodes. These results provide information for the utilization of nanohybrids and plasma-related effects to synergistically enhance the performance of supercapacitors and may create new opportunities in areas such as catalysts, photosynthesis and electrochemical sensors

Differentiation of cultivars of flos chrysanthemum with the use of high-performance liquid chromatography fingerprints and chemometrics

Flos Chrysanthemum is a generic name for a particular group of edible plants, which also have medicinal properties. There are, in fact, twenty to thirty different cultivars, which are commonly used in beverages and for medicinal purposes. In this work, four Flos Chrysanthemum cultivars, Hangju, Taiju, Gongju, and Boju, were collected and chromatographic fingerprints were used to distinguish and assess these cultivars for quality control purposes. Chromatography fingerprints contain chemical information but also often have baseline drifts and peak shifts, which complicate data processing, and adaptive iteratively reweighted, penalized least squares, and correlation optimized warping were applied to correct the fingerprint peaks. The adjusted data were submitted to unsupervised and supervised pattern recognition methods. Principal component analysis was used to qualitatively differentiate the Flos Chrysanthemum cultivars. Partial least squares, continuum power regression, and K-nearest neighbors were used to predict the unknown samples. Finally, the elliptic joint confidence region method was used to evaluate the prediction ability of these models. The partial least squares and continuum power regression methods were shown to best represent the experimental results.