4.8 Article

Density functional theory study of furfural electrochemical oxidation on the Pt (111) surface

期刊

JOURNAL OF CATALYSIS
卷 373, 期 -, 页码 322-335

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcat.2019.04.012

关键词

Electrocatalysis; DFT; Biomass; Electrooxidation

资金

  1. China Scholarship Council
  2. National Science Foundation [ACI - 1053575, 1665155]
  3. Computational Materials Education and Training (CoMET) NSF Research Traineeship [DGE-1449785]
  4. Direct For Mathematical & Physical Scien
  5. Division Of Chemistry [1665155] Funding Source: National Science Foundation

向作者/读者索取更多资源

Electro-oxidation of furfural may allow for tunability of product selectivity by varying the electrode potential. We have applied density functional theory (DFT) to investigate the electrocatalytic oxidation mechanism on the Pt (1 1 1) surface. The potential-dependent reaction free energy profiles for furfural electrocatalytic oxidation to furoic acid, succinic acid, maleic acid, and maleic anhydride are reported. After comparing several possible furfural oxidation paths, we conclude that the electro-oxidation of furfural preferentially proceeds to furoic acid, with further oxidation slowed by difficult C-C bond dissociation. Oxidation beyond furoic acid can proceed to succinic acid via 2(3H)-furanone as an intermediate and to maleic acid and maleic anhydride via 2(5H)-furanone as an intermediate. The rate of these processes is likely limited by the decarboxylation of furoic acid. DFT analysis of elementary step thermodynamics and kinetics suggests that the selectivity between furoic acid, succinic acid, maleic acid, or other oxidized products is tunable by varying the electrode potential. Initial experimental results show furoic acid as the most significant product (>80% selectivity) at 0.9 V-RHE on a Pt electrode, in agreement with DFT results. These results broaden our fundamental understanding into electrocatalytic oxidation of furfural, which is applicable in upgrading renewable biomass derivatives. (C) 2019 Elsevier Inc. All rights reserved.

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