Considerations for Ionic Diffusion in Slurry Electrolytes for Redox Flow Batteries
Bibliographic record
Abstract
Slurry electrodes have been proposed as a means to enhance the scalability of hybrid redox flow battery (RFB) chemistries for better usability in utility scale energy storage applications 1–3 . In conventional hybrid RFB’s, scalability is limited due the spatial constraints of the flow cell and the metal deposited by the negative half-reaction on charge 1 . By using a slurry electrode, the solid metal can be deposited onto electrically conductive particles dispersed in the electrolyte instead of on the stationary electrode within the flow cell. In this way, hybrid RFB chemistries can achieve the same scalability as more commonly studied true RFB chemistries, such as all-vanadium. Due to the high abundance, low cost, and low toxicity of iron electrolytes, the all-iron RFB chemistry is of particular interest for use with a slurry electrode 2,4 . The usefulness of the slurry electrode depends on the current distribution of the plating reaction. To successfully decouple the storage and power capacities of the RFB and thus enhance its scalability 5 , the faradaic current of the plating reaction must occur predominantly on the mobile slurry particles, as opposed to on the stationary current collector 1 . This current distribution is dependent on a variety of factors, such as the applied overpotential, the electrical conductivity of the slurry, the ionic conductivity of the electrolyte, the kinetics of the reaction, and the rate of ionic mass transport to reaction sites. Ionic mass transport in electrolytes containing slurry electrodes may differ from ionic transport in neat electrolyte in interesting and important ways. Due to the volume fraction of the electrolyte occupied by solid particles, the effective concentration of the ionic species may be lower than in neat electrolyte. Further, the solid particle volume fraction hinders ionic diffusion by introducing diffusion path tortuosity. This effect is more severe in higher slurry particle loadings. In this work, the effect of varying dispersed solid particle loading on ionic diffusivity is investigated via voltammetry using a rotating disk electrode. The diffusivities of ionic iron species are measured as a function of the volume fraction of solids dispersed in the electrolyte. Comparisons with the Bruggeman correlation 6,7 are made and amendments to the Levich equation are considered. (1) Petek, T. J.; Hoyt, N. C.; Savinell, R. F.; Wainright, J. S. Slurry Electrodes for Iron Plating in an All-Iron Flow Battery. J. Power Sources 2015 , 294 , 620–626. https://doi.org/10.1016/j.jpowsour.2015.06.050. (2) Petek, T. J. Enhancing the Capacity of All-Iron Flow Batteries: Understanding Crossover and Slurry Electrodes. Ph.D. Thesis 2015 , No. May. (3) Narayanan, T. M.; Zhu, Y. G.; Gençer, E.; McKinley, G.; Shao-Horn, Y. Low-Cost Manganese Dioxide Semi-Solid Electrode for Flow Batteries. Joule 2021 , 5 (11), 2934–2954. https://doi.org/10.1016/j.joule.2021.07.010. (4) Dinesh, A.; Olivera, S.; Venkatesh, K.; Santosh, M. S.; Priya, M. G.; Inamuddin; Asiri, A. M.; Muralidhara, H. B. Iron-Based Flow Batteries to Store Renewable Energies. Environ. Chem. Lett. 2018 , 16 (3), 683–694. https://doi.org/10.1007/s10311-018-0709-8. (5) Weber, A. Z.; Mench, M. M.; Meyers, J. P.; Ross, P. N.; Jeffrey, T.; Liu, Q. Redox Flow Batteries , a Review Environmental Energy Technologies Division , Lawrence Berkeley National Laboratory , Department of Mechanical , Aerospace and Biomedical Engineering , University of Tennessee , Department of Chemical Engineering , McGill Un. 1–72. (6) Tjaden, B.; Cooper, S. J.; Brett, D. J.; Kramer, D.; Shearing, P. R. On the Origin and Application of the Bruggeman Correlation for Analysing Transport Phenomena in Electrochemical Systems. Curr. Opin. Chem. Eng. 2016 , 12 , 44–51. https://doi.org/10.1016/j.coche.2016.02.006. (7) Chung, D. W.; Ebner, M.; Ely, D. R.; Wood, V.; Edwin García, R. Validity of the Bruggeman Relation for Porous Electrodes. Model. Simul. Mater. Sci. Eng. 2013 , 21 (7). https://doi.org/10.1088/0965-0393/21/7/074009.
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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 | 0.002 |
| 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".