Bibliographic record
Abstract
Redox flow batteries (RFBs) are a relatively new generation of electrochemical devices suitable for large-scale energy storage applications. The separation between the electrolyte storage tanks and the electrochemical cell in RFBs simplifies the battery scale-up and facilitates the energy/power ratio tuning. In comparison to current energy storage devices, RFBs are beneficial due to their portability, flexibility and low maintenance cost [1]. Among the different types of RFBs investigated, those based on zinc and cerium are very attractive due to the large negative and positive electrode potentials in an aqueous media. Thus, zinc-cerium RFBs are capable of providing one of the highest cell voltages (~ 2.4 V) among flow batteries and a large theoretical energy density [2]. To date, Zn-Ce RFBs have primarily been investigated galvanostatically to determine their charge, voltage and energy efficiencies and attempts have been made to suppress the rate of the hydrogen and oxygen evolution side reactions [3-6]. In order to further optimize the performance of these batteries and to elucidate the future pathways to enhance their efficiency, the sources of voltage loss in the battery during discharge must be identified and the role of the positive and negative half-cells in the voltage loss determined. Toward this goal, we have conducted in situ polarization and EIS experiments on a full-cell Zn-Ce RFB with reference electrodes inserted in the system [7]. The insertion of the reference electrodes in the RFB enables us to decouple the contribution of negative and positive electrodes to the total performance loss. At low and intermediate current densities, the main contributor to the voltage loss during discharge is the kinetic overpotential of the negative Zn/Zn 2+ half-cell. On the other hand, at high current densities, mass transfer limitations at the positive Ce 3+ /Ce 4+ half-cell cause a large potential drop in the system. From in situ kinetic studies, we have measured an exchange current density of ∼ 7.4×10 −3 A cm −2 for Zn oxidation and ∼ 24.2×10 −3 A cm −2 for Ce 4+ reduction, which is consistent with our previous findings from battery operation that the kinetics of the negative electrode reaction is slow compared to that of the positive electrode at low-to-intermediate current densities. The use of an alternative mixed methanesulfonate-chloride negative electrolyte to reduce the kinetic overpotential of the negative half-cell reaction and the influence of the flow rate on the mass-transfer rate of the positive half-cell reaction have also been investigated and will be discussed in this presentation. [1] De Leon, C. P., Frías-Ferrer, A., González-García, J., Szánto, D. A., & Walsh, F. C. (2006). Redox flow cells for energy conversion. Journal of power sources , 160(1), 716-732. [2] Walsh, F. C., Ponce de Léon, C., Berlouis, L., Nikiforidis, G., Arenas‐Martínez, L. F., Hodgson, D., & Hall, D. (2015). The development of Zn–Ce hybrid redox flow batteries for energy storage and their continuing challenges. ChemPlusChem , 80(2), 288-311. [3] Leung, P. K., Ponce-de-León, C., Low, C. T. J., & Walsh, F. C. (2011). Zinc deposition and dissolution in methanesulfonic acid onto a carbon composite electrode as the negative electrode reactions in a hybrid redox flow battery. Electrochimica Acta , 56(18), 6536-6546. [4] Nikiforidis, G., Berlouis, L., Hall, D., & Hodgson, D. (2014). An electrochemical study on the positive electrode side of the zinc–cerium hybrid redox flow battery . Electrochimica Acta , 115, 621-629. [5] Amini, K., & Pritzker, M. D. (2018). Electrodeposition and electrodissolution of zinc in mixed methanesulfonate-based electrolytes. Electrochimica Acta , 268, 448-461. [6] Amini, K., & Pritzker, M. D. (2019). Improvement of zinc-cerium redox flow batteries using mixed methanesulfonate-chloride negative electrolyte. Applied Energy , 255, 113894. [7] Amini, K., & Pritzker, M. D. (2020). In situ polarization study of zinc–cerium redox flow batteries, Journal of Power Sources , 471, 228463
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".