Water pollution is one of the most pervasive problems afflicting people throughout the world, while adsorption is the most widely used method to remove the contaminants from water. Here, in this paper, we report an eco-friendly graphene oxide-chitosan (GO-CS) hydrogel as a new type of adsorbent for water purification. The GO-CS hydrogels were prepared via self-assembly of GO sheets and CS chains. A three-dimensional network composed of GO sheets crosslinked by CS was found in GO-CS hydrogels.The GO-CS composite hydrogels showed high adsorption capacity towards different contaminants, including cationic and anionic dyes, as well as heavy metal ions. The mechanism of the dye adsorption was investigated with a spectral method, and an electrostatic interaction was found to be the major interaction between ionic dyes and the hydrogel. The influence of the hydrogel composition on the adsorption capacity towards different adsorbates was also studied. Finally, it was demonstrated that the GO-CS hydrogel can be used as column packing, to fabricate a column for water purification by filtration.

The mechanism of self-discharge (SDC) in active electrolyte enhanced supercapacitors was investigated, and two strategies were devised to suppress the SDC process.

Most of current gene expression signatures for cancer prognosis are based on risk scores, usually calculated as some summaries of expression levels of the signature genes, whose applications require presetting risk score thresholds and data normalization. In this study, we demonstrate the critical limitations of such type of signatures that the risk scores of samples will change greatly when they are normalized together with different samples, which would induce spurious risk classification and difficulty in clinical settings, and the risk scores of independent samples are incomparable if data normalization is not adopted. To overcome these limitations, we propose a rank-based method to extract a prognostic gene pair signature for overall survival of stage I non-small-cell lung cancer. The prognostic gene pair signature is verified in three integrated data sets detected by different laboratories with different microarray platforms. We conclude that, different from the type of signatures based on risk scores summarized from gene expression levels, the rank-based signatures could be robustly applied at the individualized level to independent clinical samples assessed in different laboratories.

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