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- Fan, Ke, et al.
(författare)
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Direct Observation of Structural Evolution of Metal Chalcogenide in Electrocatalytic Water Oxidation
- 2018
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Ingår i: ACS Nano. - : American Chemical Society (ACS). - 1936-0851 .- 1936-086X. ; 12:12, s. 12369-12379
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Tidskriftsartikel (refereegranskat)abstract
- As one of the most remarkable oxygen evolution reaction (OER) electrocatalysts, metal chalcogenides have been intensively reported during the past few decades because of their high OER activities. It has been reported that electron-chemical conversion of metal OER chalcogenides into oxides/hydroxides would take place after the OER. However, the transition mechanism of such unstable structures, as well as the real active sites and catalytic activity during the OER for these electrocatalysts, has not been understood yet; therefore a direct observation for the electrocatalytic water oxidation process, especially at nano or even angstrom scale, is urgently needed. In this research, by employing advanced Cs-corrected transmission electron microscopy (TEM), a step by step oxidational evolution of amorphous electrocatalyst CoSx into crystallized CoOOH in the OER has been in situ captured: irreversible conversion of CoSx to crystallized CoOOH is initiated on the surface of the electrocatalysts with a morphology change via Co(OH)(2) intermediate during the OER measurement, where CoOOH is confirmed as the real active species. Besides, this transition process has also been confirmed by multiple applications of X-ray photoelectron spectroscopy (XPS), in situ Fourier-transform infrared spectroscopy (FTIR), and other ex situ technologies. Moreover, on the basis of this discovery, a high-efficiency electrocatalyst of a nitrogen-doped graphene foam (NGF) coated by CoSx has been explored through a thorough structure transformation of CoOOH. We believe this in situ and in-depth observation of structural evolution in the OER measurement can provide insights into the fundamental understanding of the mechanism for the OER catalysts, thus enabling the more rational design of low-cost and high-efficient electrocatalysts for water splitting.
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| 2. |
- Zhang, Jialin, et al.
(författare)
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FS-GBDT : identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT
- 2021
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Ingår i: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1477-4054 .- 1467-5463. ; 22:3
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Tidskriftsartikel (refereegranskat)abstract
- Cancer is a highly heterogeneous disease caused by dysregulation in different cell types and tissues. However, different cancers may share common mechanisms. It is critical to identify decisive genes involved in the development and progression of cancer, and joint analysis of multiple cancers may help to discover overlapping mechanisms among different cancers. In this study, we proposed a fusion feature selection framework attributed to ensemble method named Fisher score and Gradient Boosting Decision Tree (FS-GBDT) to select robust and decisive feature genes in high-dimensional gene expression datasets. Joint analysis of 11 human cancers types was conducted to explore the key feature genes subset of cancer. To verify the efficacy of FS-GBDT, we compared it with four other common feature selection algorithms by Support Vector Machine (SVM) classifier. The algorithm achieved highest indicators, outperforms other four methods. In addition, we performed gene ontology analysis and literature validation of the key gene subset, and this subset were classified into several functional modules. Functional modules can be used as markers of disease to replace single gene which is difficult to be found repeatedly in applications of gene chip, and to study the core mechanisms of cancer.
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