Continuous in-Situ Monitoring of Aqueous Chlorine Species Formed during Electrolysis of Saltwater
Bibliographic record
Abstract
Hydrogen is a promising alternative energy carrier to mitigate emissions arising from fossil fuel use. However, current methods of commercial hydrogen production, e.g., steam-methane reforming, are known to contribute significantly to greenhouse gas emissions. Therefore, there is substantial interest in green hydrogen production through water electrolysis. At the same time, sources of freshwater are limited and thus efforts are increasingly turning to saltwater electrolysis using renewable energy 1 . However, one of the key challenges then encountered is related to the chlorine evolution reaction (CER), which can compete with the desired oxygen evolution reaction (OER) at the anode, especially as the local pH becomes more acidic, which causes the thermodynamic potentials of the OER and CER to become more similar 1,2 . Cl 2 is the primary product of CER in acidic media, whereas OCl - is the predominant species in neutral and alkaline environments 1,2 . However, the CER is an undesirable reaction during water splitting due to the toxic and corrosive nature of the OCl - and gaseous Cl 2 formed 1,2 . While there are many efforts being made to minimize the CER by the development of highly selective OER electrocatalysts and through the use of membranes to prevent acidic conditions from building up at the anode, it is still important to monitor the amount of OCl - and Cl 2 formed under a variety of conditions of potential, current, anode catalyst material, solution agitation, etc. For this reason, our goal is to continuously monitor OCl - formation at the anode to quantitatively determine the Faradaic efficiency of oxygen production. Present-day methods of OCl - and Cl 2 monitoring each have their own drawbacks. For example, standard analysis techniques, such as gas chromatography (GC), can quantify the amount of Cl 2 produced but special corrosion protection measures must be taken to protect the GC columns and detectors. Further, analytical techniques, e.g., iodometric titration, are cumbersome as they require freshly prepared titrants/solutions 2,3 . In contrast, several electrochemical techniques, including cyclic voltammetry and differential pulse voltammetry, have been shown to quantitively detect OCl - in alkaline solutions 4–6 but have not been applied to saltwater electrolysis applications to our knowledge. In the present work, we demonstrate the in-situ quantification of the OCl - concentration during saltwater electrolysis by tracking the charge passed during what has been proposed to be OCl - reduction in a peak at ca. 1.5 vs RHE 6–8 . As our goal is to identify anode materials that are both corrosion resistant and intrinsically selective to the OER, this work also focusses on several different families of anode materials. Here, the amount of OCl - formed is determined continuously using a fourth electrode poised at a potential negative of 1.4 V vs RHE as a function of electrolysis time. The accuracy of this electrochemical method has been confirmed by iodometric titration and parallel rotating ring disc electrode analyses. Additional confirmation of the validity of this method has been obtained from the measured oxidation charge passed over various times at constant potential as compared to the amount of oxygen produced at the anode outlet as determined by gas chromatography, with the difference due to OCl - formation. This presentation will include the results of studies of the selectivity of the OER vs the CER at several new anode materials with time and as a function of current, potential, NaCl solution flow rates, and pH. Acknowledgements: This research is supported by the Natural Science and Engineering Research Council of Canada, the Canada First Research Excellence Fund, Alberta Innovates, Evolve Hydrogen Inc., Qualicase Ltd., and Fidelity Manufacturing Group. References: (1) Dresp, S.; Dionigi, F.; Klingenhof, M.; Strasser, P. ACS Energy Letters . 2019 , 933–942. https://doi.org/10.1021/acsenergylett.9b00220. (2) Tang, X.; Arif, I.; Diao, P. Journal of Electroanalytical Chemistry 2023 , 942 , 1–7. https://doi.org/10.1016/j.jelechem.2023.117569. (3) Suzuki, K.; Gordon, G. Anal Chem 1978 , 50 (11), 1596–1597. (4) Kesavan, S.; Kumar, D. R.; Dhakal, G.; Kim, W. K.; Lee, Y. R.; Shim, J. J. Nanomaterials 2023 , 13 (1), 1-14. https://doi.org/10.3390/nano13010151. (5) Muñoz, J.; Céspedes, F.; Baeza, M. Microchemical Journal 2015 , 122 , 189–196. https://doi.org/10.1016/j.microc.2015.05.001. (6) Kodera, F.; Umeda, M.; Yamada, A. Japanese Journal of Applied Physics, Part 2: Letters 2005 , 44 (22), 718-719. https://doi.org/10.1143/JJAP.44.L718. (7) Ordeig, O.; Mas, R.; Gonzalo, J.; Del Campo, F. J.; Muñoz, F. J.; De Haro, C. Electroanalysis 2005 , 17 (18), 1641–1648. https://doi.org/10.1002/elan.200403194. (8) Harrison, J. A.; Khan, Z. A. Electroanalytical chemistry and interfacial electrochemistry 1970 , 30 , 87–92.
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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".