The data of cyclic voltammograms were sanitized and transformed into two-dimensional matrices suitable for DL algorithms of ResNet architecture. While cyclic voltammograms are typically presented as images in literature, much of the white space in voltammograms contains little (if any) information. Hence, similar to the case of electrocardiogram, 19 a two-dimensional matrix of {v, i(E)}n, rather than the images of cyclic voltammograms as in the works of Bond and coworkers, is employed to store electrochemical information for the DL-based analysis (Fig. ). For the ease of training a ResNet, in each set of {v, i(E)}n the current densities i in voltammograms were normalized as inormalized against the largest i among all voltammograms in {v, i(E)}n, with inormalized in the forward scan designated as positive values. The electrochemical potentials E were adjusted so that the position of 0 V of the adjusted electrochemical potential (Eadjusted) roughly corresponds to the potential of studied redox couple (see Methods). Such data processing ensures a generally readable format of cyclic voltammograms despite the large variations in experimental testing conditions.