Low Concentration Slurry Electrodes for Redox Flow Batteries
Bibliographic record
Abstract
Iron redox flow batteries are a promising option for utility scale energy storage. In redox flow batteries (RFB), the power and energy storage capacities are decoupled, making them highly scalable 1,2 . Due to its high abundance, low cost, and low toxicity, iron is very attractive as a reactive species for both the positive and negative half cells in large scale redox flow batteries 3 . The Fe(II)/Fe(III) reaction is utilized at the positive electrode while the Fe(0)/Fe(II) reaction is used at the negative electrode. Unfortunately, the Fe(II) reduction reaction used in the negative cell involves plating solid iron onto the electrode during charge. This plating reaction limits the battery’s capacity based on the spatial constraints of the flow cell, coupling the power and storage capacities of the flow battery and limiting its scalability 2 . Slurry electrodes, consisting of a dispersion of conductive particles in the electrode, have been proposed as solution for this issue 4 . By having the metal deposit onto the mobile dispersion of particles, as in Figure 1B, instead of the stationary electrode as in Figure 1A, the power and storage capacities of a hybrid flow battery can be decoupled. Slurry electrodes have also been proposed in a number of other applications such as water deionization and supercapacitors 5 . Their use has also been studied for use in fully soluble RFB chemistries, such as all-vanadium 6,7 . However, nearly all of the previous work in slurry electrodes has been in highly concentrated slurries in order to take advantage of the conductivity of the percolated particle network. Unfortunately, these highly loaded slurries can be viscous and can cause clogs and failures in a flowing system such as an RFB 4,7 . In this work, the electrochemical behavior of slurries below the percolation threshold are investigated via voltammetry in a custom flow cell. The percolation threshold of a slurry is identified and the modified behavior of the Fe (II)/Fe (III) reaction is measured as a function of slurry concentration and flow rate. The results suggest that significant enhancement of the electrochemically active surface area can be achieved below the percolation threshold. (1) 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. (2) 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. (3) Petek, T. J. Enhancing the Capacity of All-Iron Flow Batteries: Understanding Crossover and Slurry Electrodes. Ph.D. Thesis 2015 , No. May. (4) 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. (5) Mourshed, M.; Niya, S. M. R.; Ojha, R.; Rosengarten, G.; Andrews, J.; Shabani, B. Carbon-Based Slurry Electrodes for Energy Storage and Power Supply Systems. Energy Storage Mater. 2021 , 40 (April), 461–489. https://doi.org/10.1016/j.ensm.2021.05.032. (6) Percin, K.; van der Zee, B.; Wessling, M. On the Resistances of a Slurry Electrode Vanadium Redox Flow Battery. ChemElectroChem 2020 , 7 (9), 2165–2172. https://doi.org/10.1002/celc.202000242. (7) Lohaus, J.; Rall, D.; Kruse, M.; Steinberger, V.; Wessling, M. On Charge Percolation in Slurry Electrodes Used in Vanadium Redox Flow Batteries. Electrochem. commun. 2019 , 101 (March), 104–108. https://doi.org/10.1016/j.elecom.2019.02.013. 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".