image5_An Ordinary Differential Equation Model for Simulating Local-pH Change at Electrochemical Interfaces.tif
The local pH value at an electrochemical interface (pHs) inevitably changes during redox reactions involving the transfer of H+ or OH− ions. It is important to quantitatively estimate pHs during polarization, as this parameter has a significant impact on the activity and selectivity of electrochemical reactions. Numerical simulation is an effective means of estimating pHs because it is not subject to experimental constraints. As demonstrated in a number of studies, pHs can be estimated by solving partial differential equations that describe diffusion process. In the present work, we propose a method to consider the process by using ordinary differential equations (ODEs), which can significantly reduce the computational resources required for estimating pHs values. In the ODE-based model, the description of the diffusion process was achieved by considering the reaction plane in the diffusion layer over which the H+ and OH− concentrations are balanced while assuming that the concentration profiles in the layer are in a steady state. The resulting model successfully reproduces experimental voltammograms characterized by local pH changes in association with the hydrogen evolution and hydrogen peroxide reduction reactions.
History
References
- https://doi.org//10.1039/c1cp21717h
- https://doi.org//10.1021/acsami.7b07351
- https://doi.org//10.1021/acscatal.7b02373
- https://doi.org//10.1016/j.jelechem.2015.09.029
- https://doi.org//10.1147/rd.372.0085
- https://doi.org//10.1021/acscatal.8b01032
- https://doi.org//10.1002/anie.201909238
- https://doi.org//10.1149/2.0191711jes
- https://doi.org//10.1021/jacs.5b08259
- https://doi.org//10.1007/BF00241925
- https://doi.org//10.1002/celc.201402373
- https://doi.org//10.1016/j.elecom.2011.03.032
- https://doi.org//10.1016/0022-0728(91)85117-8
- https://doi.org//10.1016/s0013-4686(00)00522-3
- https://doi.org//10.1149/2.022209jes
- https://doi.org//10.1002/anie.201604654
- https://doi.org//10.1246/bcsj.72.1247
- https://doi.org//10.1007/s10008-015-2813-z
- https://doi.org//10.1149/2.0011702jes
- https://doi.org//10.1149/2.1561709jes
- https://doi.org//10.1016/j.jelechem.2008.02.006
- https://doi.org//10.1021/jp012461s
- https://doi.org//10.1016/j.jelechem.2013.11.002
- https://doi.org//10.1021/acs.langmuir.7b00696
- https://doi.org//10.1021/ac050800y
- https://doi.org//10.1002/anie.201802756
- https://doi.org//10.1021/cs500522g
- https://doi.org//10.1039/c5cp03283k
- https://doi.org//10.1021/jacs.6b07612
- https://doi.org//10.1021/jp1048887
- https://doi.org//10.3390/catal9030224
- https://doi.org//10.1016/j.snb.2010.05.058
- https://doi.org//10.1016/j.cattod.2015.06.009
- https://doi.org//10.1002/anie.201610432
- https://doi.org//10.1002/anie.201607942
- https://doi.org//10.1002/anie.201912637
Usage metrics
Read the peer-reviewed publication
Categories
- Nuclear Engineering (incl. Fuel Enrichment and Waste Processing and Storage)
- Chemical Engineering not elsewhere classified
- Chemical Sciences not elsewhere classified
- Carbon Sequestration Science
- Energy Generation, Conversion and Storage Engineering
- Automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels)
- Power and Energy Systems Engineering (excl. Renewable Power)
- Renewable Power and Energy Systems Engineering (excl. Solar Cells)
- Carbon Capture Engineering (excl. Sequestration)
- Nuclear Engineering
- Non-automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels)