A list of current publications from our group using a range of Artificial intelligence techniques for the analysis of DC and FTACV voltammetry.
The techniques applied to voltammetry range from optimisation, Bayesian inference, Machine learning using Deep Neural Networks and a
range of other techniques that have been used to qualitatively and quantitatively identify physical voltammetric systems.
Much of the open source code used can be found in these publications or on out GITHUB repositories.
2022
Bond, A. M., Zhang, J., Gundry, L., & Kennedy, G. F. (2022). Opportunities and Challenges in Applying Machine Learning to Voltammetric Mechanistic Studies. Current Opinion in Electrochemistry, 101009. https://doi.org/10.1016/j.coelec.2022.101009Gundry, L., Kennedy, G., Bond, Alan M., & Zhang, J. (2022). Inclusion of multiple cycling of potential in the deep neural network classification of voltammetric reaction mechanisms. Faraday Discussions, 233, 44-57. https://doi.org/10.1039/d1fd00050k2021
Gundry, L., Guo, S.-X., Kennedy, G., Keith, J., Robinson, M., Gavaghan, D., Bond, A. M., & Zhang, J. (2021). Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry. Chemical Communications, 57(15), 1855-1870. https://doi.org/10.1039/d0cc07549cGundry, L., Kennedy, G., Keith, J., Robinson, M., Gavaghan, D., Bond, A. M., & Zhang, J. (2021). A Comparison of Bayesian Inference Strategies for Parameterisation of Large Amplitude AC Voltammetry Derived from Total Current and Fourier Transformed Versions. ChemElectroChem, 8(12), 2238-2258. https://doi.org/10.1002/celc.2021003912019
Li, J., Kennedy, G. F., Gundry, L., Bond, A. M., & Zhang, J. (2019). Application of Bayesian Inference in Fourier-Transformed Alternating Current Voltammetry for Electrode Kinetic Mechanism Distinction. Analytical Chemistry, 91(8), 5303-5309. https://doi.org/10.1021/acs.analchem.9b00129Kennedy, G. F., Zhang, J., & Bond, A. M. (2019). Automatically Identifying Electrode Reaction Mechanisms Using Deep Neural Networks. Analytical Chemistry, 91(19), 12220-12227. https://doi.org/10.1021/acs.analchem.9b01891