Understanding Potential Losses and pH Distribution in the Electrochemical Nitrate Reduction Reaction to Ammonia

被引:8
作者
Ahmadi, Maryam [1 ]
Nazemi, Mohammadreza [1 ,2 ]
机构
[1] Colorado State Univ, Dept Mech Engn, Ft Collins, CO 80523 USA
[2] Colorado State Univ, Sch Mat Sci & Engn, Ft Collins, CO 80524 USA
基金
美国国家科学基金会;
关键词
WATER; SINGLE; DRIVEN; FUEL; CELLS;
D O I
10.1021/acs.iecr.3c04540
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Electrochemical nitrate reduction reaction (NO3-RR) to ammonia is a promising route to eliminate one of the major pollutants in surface water and groundwater. When powered by renewable electricity, electrolysis provides a sustainable method to generate ammonia from nitrate ions, facilitating the transition from a linear to a circular economy. Optimizing the physical and chemical properties of electrolysis cells is crucial to making this process economically viable for widespread implementation. Here, we explore how the choice of current density, conductivity, pH, interelectrode distance, membrane, catalyst, and buffer solution affect nitrate removal performance and efficiency. We developed a modeling framework to investigate the cell characteristics and fluid dynamics during electrochemical NO3-RR using both laminar and bubbly flows. To obtain more precise results, we employed the bubbly flow model (i.e., multiphase fluid) to take into account how gas production near the electrode surface affects liquid velocity, pH distribution, and, ultimately, potential losses. We exploit mass transfer theory to include the current density effect on migration and diffusion. In the absence of a buffer solution, the Nernstian loss became a significant portion of the polarization loss, which increased with current density. We identified the positive effect of the membrane on energy efficiency as being more significant at smaller interelectrode distances. This study provides insights into the origin of potential losses and pH distribution, enabling electrochemical cell optimization for renewable fuel synthesis.
引用
收藏
页码:9315 / 9328
页数:14
相关论文
共 52 条
  • [1] Three dimensional modeling of an solid oxide fuel cell coupling charge transfer phenomena with transport processes and heat generation
    Andersson, Martin
    Paradis, Hedvig
    Yuan, Jinliang
    Sunden, Bengt
    [J]. ELECTROCHIMICA ACTA, 2013, 109 : 881 - 893
  • [2] [Anonymous], 1989, CRC HDB CHEM PHYS
  • [3] Solute transport solved with the Nernst-Planck equation for concrete pores with 'free' water and a double layer
    Appelo, C. A. J.
    [J]. CEMENT AND CONCRETE RESEARCH, 2017, 101 : 102 - 113
  • [4] Bard A. J., 2022, Electrochemical Methods:Fundamentals and Applications
  • [5] Electrocatalytic nitrate-to-ammonia conversion with ∼100% Faradaic efficiency via single-atom alloying
    Cai, Jinmeng
    Wei, Yingying
    Cao, Ang
    Huang, Jingjing
    Jiang, Zheng
    Lu, Siyu
    Zang, Shuang-Quan
    [J]. APPLIED CATALYSIS B-ENVIRONMENT AND ENERGY, 2022, 316
  • [6] Efficient conversion of low-concentration nitrate sources into ammonia on a Ru-dispersed Cu nanowire electrocatalyst
    Chen, Feng-Yang
    Wu, Zhen-Yu
    Gupta, Srishti
    Rivera, Daniel J.
    Lambeets, Sten, V
    Pecaut, Stephanie
    Kim, Jung Yoon Timothy
    Zhu, Peng
    Finfrock, Y. Zou
    Meira, Debora Motta
    King, Graham
    Gao, Guanhui
    Xu, Wenqian
    Cullen, David A.
    Zhou, Hua
    Han, Yimo
    Perea, Daniel E.
    Muhich, Christopher L.
    Wang, Haotian
    [J]. NATURE NANOTECHNOLOGY, 2022, 17 (07) : 759 - +
  • [7] Roles of Copper in Nitrate Reduction at Copper-Modified Ru/C Catalysts
    Chen, Jia-Qi
    Ye, Xu-Xu
    Zhou, Da
    Chen, Yan-Xia
    [J]. JOURNAL OF PHYSICAL CHEMISTRY C, 2023, 127 (06) : 2918 - 2928
  • [8] Learning Optimal Forms of Constitutive Relations Characterizing Ion Intercalation from Data in Mathematical Models of Lithium-Ion Batteries
    Daniels, Lindsey
    Sahu, Smita
    Sanders, Kevin J.
    Goward, Gillian R.
    Foster, Jamie M.
    Protas, Bartosz
    [J]. JOURNAL OF PHYSICAL CHEMISTRY C, 2023, 127 (35) : 17508 - 17523
  • [9] Metallic Co Nanoarray Catalyzes Selective NH3 Production from Electrochemical Nitrate Reduction at Current Densities Exceeding 2 A cm-2
    Deng, Xiaohui
    Yang, Yongpeng
    Wang, Lei
    Fu, Xian-Zhu
    Luo, Jing-Li
    [J]. ADVANCED SCIENCE, 2021, 8 (07)
  • [10] Dwight E., 1972, AM I OFPHYSICS HDB