A Two-Dimensional Transient Model to Investigate the Influence of Flow Field Design on Zinc Deposition and Performance in a Zinc-Iodide Flow Battery
Bibliographic record
Abstract
Zinc-based flow batteries are attractive because they have high aqueous solubility and hence high energy density, as well as rapid kinetics [1,2]. Among the aqueous redox flow batteries (RFBs), the zinc-iodide flow battery has been reported to have very high discharge energy density [1]. However, aqueous zinc batteries suffer from dendrite formation resulting in capacity fading and a decreased battery performance. In our previous study [3], we evaluated a novel flow field design for the zinc-iodide flow battery. When compared to a conventional flow-by (FB) mode, significantly higher charge and energy efficiencies were obtained with the novel arrangement. In the novel flow design, part of the was drawn through the porous electrode towards the current feeder, and this was found to enhance the effective surface area and decrease the ohmic loss. This design, called flow-by-through (FBT), with a component of flow in the direction of the current feeder, can prevent the blockage of the mass/ion transport pathway and enhance the flow distribution in hybrid flow batteries [4]. Based on X-ray computed tomography and SEM characterization, a denser zinc deposit, with more zinc deposition inside the felt were observed with the FBT mode, which may explain the increased discharge capacity and cycle life of the battery [3]. A mathematical model of the zinc-iodide RFB can help us better understand the battery performance in the different flow modes. Models of RFBs have been used to investigate the impact of the flow field on performance since 1980 [5]. In this study, we have used a two-dimensional transient model with the COMSOL multi-physics software package to study the galvanostatic charge/discharge behavior of the zinc-iodide RFB in FB and FBT modes. The model considers the electrochemical kinetics, mass transport and the current and potential distribution in a single cell zinc-iodide flow battery. This is the first reported modeling study of the influence of the FBT design on the performance of an RFB. The galvanostatic charge-discharge of a zinc-iodide battery was modelled at a current density of 20 mA cm -2 , and a charge duration of 20000 s, corresponding to a state of charge of 25.7%. These conditions were chosen to enable comparison and validation with experimental data are shown in Figure 1. The rate of zinc ion consumption and the amount of zinc deposition on the graphite felt electrode for both FB and FBT arrangements were evaluated using the model. The distribution of zinc deposition, potential and current, will be presented, which provide new insights into the impact of the flow arrangement on the battery performance. The model will be applied to predict cell voltage and efficiency, maximum discharge capacity and maximum discharge power density of the zinc-iodide RFB for FB and FBT arrangements. [1] Li, B., Nie, Z., Vijayakumar, M., Li, G., Liu, J., Sprenkle, V., & Wang, W. (2015). Ambipolar zinc-polyiodide electrolyte for a high-energy density aqueous redox flow battery. Nature communications , 6 (1), 1-8. [2] Higashi, S., Lee, S. W., Lee, J. S., Takechi, K., & Cui, Y. (2016). Avoiding short circuits from zinc metal dendrites in anode by backside-plating configuration. Nature communications , 7 (1), 1-6. [3] ShakeriHosseinabad, F., Daemi, S. R., Momodu, D., Brett, D. J., Shearing, P. R., & Roberts, E. P. (2021). Influence of Flow Field Design on Zinc Deposition and Performance in a Zinc-Iodide Flow Battery. ACS Applied Materials & Interfaces , 13 (35), 41563-41572. [4] Zhou, X., Lin, L., Lv, Y., Zhang, X., Fan, L., & Wu, Q. (2020). Elucidating effects of component materials and flow fields on Sn–Fe hybrid flow battery performance. Journal of Power Sources , 450 , 227613. [5] Trainham, J. A., & Newman, J. (1981). A comparison between flow-through and flow-by porous electrodes for redox energy storage. Electrochimica Acta , 26 (4), 455-469. Figure 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".