Covalent modification of reduced graphene oxide with piperazine as a novel nanoadsorbent for removal of H2S gas

In the present research, piperazine grafted-reduced graphene oxide RGO-N-(piperazine) was synthesized through a three-step reaction and employed as a highly efficient nanoadsorbent for H2S gas removal. Temperature optimization within the range of 30–90 °C was set which significantly improved the adsorption capacity of the nanoadsorbent. The operational conditions including the initial concentration of H2S (60,000 ppm) with CH4 (15 vol%), H2O (10 vol%), O2 (3 vol%) and the rest by helium gas and gas hour space velocity (GHSV) 4000–6000 h−1 were examined on adsorption capacity. The results of the removal of H2S after 180 min by RGO-N-(piperazine), reduced graphene oxide (RGO), and graphene oxide (GO) were reported as 99.71, 99.18, and 99.38, respectively. Also, the output concentration of H2S after 180 min by RGO-N-(piperazine), RGO, and GO was found to be 170, 488, and 369 ppm, respectively. Both chemisorption and physisorption are suggested as mechanism in which the chemisorption is based on an acid–base reaction between H2S and amine, epoxy, hydroxyl functional groups on the surface of RGO-N-(piperazine), GO, and RGO. The piperazine augmentation of removal percentage can be attributed to the presence of amine functional groups in the case of RGO-N-(piperazine) versus RGO and GO. Finally, analyses of the equilibrium models used to describe the experimental data showed that the three-parameter isotherm equations Toth and Sips provided slightly better fits compared to the three-parameter isotherms

Dynamic Data Driven Application Systems for Identification of Biomarkers in DNA Methylation

The term ‘epigenetic’ refers to all heritable alterations that occur in a given gene function without having any change on the DeoxyriboNucleic Acid (DNA) sequence. Epigenetic modifications play a crucial role in development and differentiation of various diseases including cancer. The specific epigenetic alteration that has garnered a great deal of attention is DNA methylation, i.e., the addition of a methyl-group to cytosine. Recent studies have shown that different tumor types have distinct methylation profiles. Identifying idiosyncratic DNA methylation profiles of different tumor types and subtypes can provide invaluable insights for accurate diagnosis, early detection, and tailoring of the related treatment for cancer. In this study, our goal is to identify the informative genes (biomarkers) whose methylation level change correlates with a specific cancer type or subtype. To achieve this goal, we propose a novel high dimensional learning framework inspired by the dynamic data driven application systems paradigm to identify the biomarkers, determine the outlier(s) and improve the quality of the resultant disease detection. The proposed framework starts with a principal component analysis (PCA) followed by hierarchical clustering (HCL) of observations and determination of informative genes based on the HCL predictions. The capabilities and performance of the proposed framework are demonstrated using a DNA methylation dataset stored in Gene Expression Omnibus (GEO) DataSets on lung cancer. The preliminary results demonstrate that our framework outperforms the conventional clustering algorithms with embedded dimension reduction methods, in its efficiency to identify informative genes and outliers, and removal of their contaminating effects at the expense of reasonable computational cost