Literature DB >> 31009211

Surface, Subsurface, and Bulk Oxygen Vacancies Quantified by Decoupling and Deconvolution of the Defect Structure of Redox-Active Nanoceria.

Rashid Mehmood1, Sajjad S Mofarah1, Wen-Fan Chen1, Pramod Koshy1, Charles C Sorrell1.   

Abstract

Oxygen vacancy concentrations are critical to the redox/photocatalytic performance of nanoceria, but their direct analysis is problematic under controlled atmospheres but essentially impossible under aqueous conditions. The present work provides three novel approaches to analyze these data from XPS data for the three main morphologies of nanoceria synthesized under aqueous conditions and tested using in vacuo analytical conditions. First, the total oxygen vacancy concentrations are decoupled quantitatively into surface-filled, subsurface-unfilled, and bulk values. Second, the relative surface areas are calculated for all exposed crystallographic planes. Third, XPS and redox performance data are deconvoluted according to the relative surface areas of these planes. Correlations based on two independent empirical results from volumetric surface XPS, combined with sequential deep XPS and independent EELS data, confirm that these approaches provide quantitative determinations of the different oxygen vacancy concentrations. Critically, the redox/photocatalytic performance depends not on the total oxygen vacancy concentration but on the concentration of the active sites on each plane in the form of subsurface-unfilled oxygen vacancies. This is verified by the pH-dependent performance, which can be increased significantly by exposing these vacancies to the surroundings. These approaches have significance to the design and engineering of semiconducting materials exposed to the environment.

Entities:  

Year:  2019        PMID: 31009211     DOI: 10.1021/acs.inorgchem.9b00330

Source DB:  PubMed          Journal:  Inorg Chem        ISSN: 0020-1669            Impact factor:   5.165


  1 in total

1.  Oxygen Vacancy Injection on (111) CeO2 Nanocrystal Facets for Efficient H2O2 Detection.

Authors:  Tong Li; Qi Wang; Zhou Wang
Journal:  Biosensors (Basel)       Date:  2022-08-03
  1 in total

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