4.8 Article

Graphene Oxide. Origin of Acidity, Its Instability in Water, and a New Dynamic Structural Model

期刊

ACS NANO
卷 7, 期 1, 页码 576-588

出版社

AMER CHEMICAL SOC
DOI: 10.1021/nn3047378

关键词

graphene oxide; acidity; dynamic structural model; graphene interaction with water

资金

  1. Air Force Office of Scientific Research [FA9550-09-1-0581]
  2. Multi-Chem/Halliburton Group LLC
  3. MI SWACO/Schlumberger
  4. Division Of Chemistry
  5. Direct For Mathematical & Physical Scien [0947054] Funding Source: National Science Foundation

向作者/读者索取更多资源

The existing structural models of graphene oxide (GO) contradict each other and cannot adequately explain the acidity of its aqueous solutions. Inadequate understanding of chemical structure can lead to a misinterpretation of observed experimental phenomena. Understanding the chemistry and structure of GO should enable new functionalization protocols while explaining GO's limitations due to its water instability. Here we propose an unconventional view of GO chemistry and develop the corresponding dynamic structural model (DSM). In contrast to previously proposed models, the DSM considers GO as a system, constantly changing its chemical structure due to interaction with water. Using potentiometric titration, C-13 NMR, FTIR, UV-vis, X-ray photoelectron microscopy, thermogravimetric analysis, and scanning electron microscopy we show that GO does not contain any significant quantity of preexisting acidic functional groups, but gradually generates them through Interaction with water. The reaction with water results In C-C bond cleavage, formation of vinylogous carboxylic acids, and the generation of protons. An electrical double layer formed at the GO interface in aqueous solutions plays an important role In the observed GO chemistry. Prolonged exposure to water gradually degrades GO flakes converting them Into humic acid-like structures. The proposed DSM provides an explanation for the acidity of GO aqueous solutions and accounts for most of the known spectroscopic and experimental data.

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