Utilizing Microelectrodes to Understand Indirect Redox Targeting Reactions for High Energy Density Flow Batteries
Bibliographic record
Abstract
One of the major problems with widespread implementation of renewable energy is the lack of cost-effective long-duration grid-scale energy storage filling in for the intermittent nature of solar and wind energy. Redox flow batteries (RFBs) are considered a promising solution to this problem due to their decoupled energy and power capabilities, allowing them to be scaled up for long-duration more efficiently and cost-effectively than other electrochemical energy storage devices [1]. The energy capacity of RFBs is primarily determined by the quantity of active species in the electrolyte, which is a product of the volume and the concentration. Thus, the limiting factor for energy density in RFBs is typically the solubility of the active species [2]. One probable path to enhancing the energy density without increasing the concentration is by utilizing the concept of indirect redox targeting reactions [3]. These redox-targeting flow batteries (RTFB) use solid charge storage materials in the electrolyte tanks to boost capacity, allowing the active species to act as mediators which transfer their charge by reversibly reducing or oxidizing the solid material [4]. In this study, cobalt hexacyanoferrate (CoHCF) was used as a solid capacity booster in an electrolyte containing vanadium(iv/v)bis-hydroxyiminodiacetate (VBH) as mediators in acetonitrile. Constant current cycling was performed in a symmetric cell configuration with vanadium ions shuttling between vanadium(iv) and vanadium(v) oxidation states. To monitor the concentration of redox active species accurately and in real-time, a carbon fiber ultramicroelectrode (UME) was installed on the capacity limiting half-cell. This in-line UME set-up provides a better understanding of how CoHCF interacts with mediators and insights on active species crossover between half-cells. In addition, this study aims to examine the kinetics of the indirect redox-targeting reactions. As a rarely studied charge transfer process, very few studies have been reported on the kinetics of indirect redox targeting reactions [5]. Scanning electrochemical microscopy is used to understand how counter cations affect the kinetics of the reaction between the solid booster material and the mediator. Various counter cations (e.g., Ca2+, Na+, Li+) are used with the previously mentioned VBH-based mediators in order to determine the effective rate constants (keff) as well as the reversibility of the charge transfer reactions. References: [1] X. Wang, J. Chai, J. Jiang, Nano Mater. Sci., 1, 17-24 (2021) [2] S. K. Pahari, T. C. Gokoglan, B. R. B. Visayas, J. Woehl, J. A. Golen, R. Howland, M. L. Mayes, E. Agar, P. J. Cappillino, RSC Adv., 11, 5432-5443 (2021). [3] F. Zhang, M. Gao, S. Huang, H. Zhang, X. Wang, L. Liu, M. Han, Q. Wang, Adv. Mater., 34, 2104562 (2021). [4] J. Egitto, T. C. Gokoglan, S.K. Pahari, J. N. Bolibok, S. R. Aravamuthan, F. Liu, X. Jin, P. J. Cappillino, E. Agar, ASME. J. Electrochem. En. Conv. Stor., 19(4): 041005 (2022). [5] R. Yan, J. Ghilane, K. C. Phuah, T. N. Pham Truong, S. Adams, H. N. Randriamahazaka, Q. Wang, J. Phys. Chem. Lett., 9 (3), 491-496 (2018).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".