Understanding Reaction Mechanisms Using Dynamic Electrochemical Impedance Spectroscopy: Methanol and Formic Acid Oxidation
Bibliographic record
Abstract
Electrochemical impedance spectroscopy (EIS) is a suitable tool for mechanistic studies due to the amount of data that is accessible through this technique [1]. However, even if the frequency range is wide, the mechanistic information is convoluted making the interpretation complicated. Therefore, data analysis of impedance data is often limited to pattern recognition and fitting of equivalent circuits. Methods for calculating EIS spectra from a stated mechanism is readily available and easily implemented [2]. However, the system under study needs to be well understood so that realistic reaction mechanisms can be formulated. In this work, methanol and formic acid oxidation at platinum electrodes were studied at temperatures up to 140°C by using a self-pressurized autoclave [3]. Dynamic EIS (dEIS) was used, where a multisine potential signal is superimposed on a cyclic voltammogram allowing for the calculation of the EIS spectra at any point during the voltammogram [4]. This enables the study of the reactions for transient surface conditions that are not accessible in a conventional steady-state EIS measurement. These two reactions have been characterized previously by EIS and ac voltammetry, and modeling of the reaction mechanisms have been reported and discussed [5,6]. In the case of methanol oxidation, six different reaction models were proposed and fitted to the experimental voltammogram using non-linear optimization in Maple. An example of the results of this procedure are shown in Fig. 1, and we were able to distinguish between the models, for example, the fitted dEIS spectra indicated that the surface reaction between adsorbed CO and adsorbed OH was chemical, as was proposed by Kauranen and co-workers [7]. In the case of formic acid oxidation, nine different models were tested, and a ternary reaction pathway model was necessary to give satisfactory fit to the experimental data. The role of adsorbed formate (HCOO) has been controversial [8,9], and through our mechanistic modeling, we found that formate likely contributes to both the direct (no strongly adsorbed intermediates) and indirect (through adsorbed CO) reaction pathways. In many cases, the suggested reaction mechanisms could all reasonably represent the experimental cyclic voltammogram, as exemplified by Fig. 1, and the dEIS data was necessary to distinguish between the models. This demonstrates the important role that EIS and dEIS can have in mechanistic studies of electrochemical reactions. 1. D. D. Macdonald, Electrochim. Acta, 2006 , 51, 1376-1388. 2. D. A. Harrington, J. Electroanal. Chem. , 1998 , 449, 9-28. 3. T. Holm, P. K. Dahlstrøm, O. S. Burheim, S. Sunde, D. A. Harrington, F. Seland, Electrochim. Acta , 2016 , 222, 1792-1799. 4. R. Sacci, F. Seland, D. A. Harrington, Electrochim. Acta , 2014 , 131, 13-19. 5. U. Krewer, M. Christov, T. Vidakovic, K. Sundmacker, J. Electroanal. Chem. , 2006 , 589(1), 148-159. 6. F. Seland, R. Tunold, D. A. Harrington, Electrochim. Acta , 2008 , 53(23), 6851-6864. 7. P. S. Kauranen, E. Skou, J. Munk, J. Electroanal. Chem. , 1996 , 404(1), 1-13. 8. K. Jiang, H.-Z. Zhang, S. Zou, W.-B. Cai, Phys. Chem. Chem. Phys. , 2014 , 16, 20360. 9. J. Joo, T. Uchida, A. Cuesta, M. T. M. Koper, M. Osawa, Electrochim. Acta , 2014 , 129, 127-136. Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".