Carbon Paper Electrodes Modified with Bismuth for Enhancing the Performance of Vanadium Redox Flow Batteries
Bibliographic record
Abstract
Redox flow batteries (RFBs) are a promising technology for grid scale stationary energy storage to complement renewable energy systems. Although RFBs have a relatively low energy density, they offer important benefits such as long lifetime, decoupled energy and power, high round-trip efficiency, scalability and design flexibility, fast response and low environmental impacts. These benefits make them superior to many other stationary energy storage technologies [1-3]. The vanadium RFB (VRFB) is the most widely used RFB in industry for grid scale energy storage and for integration of renewable energy generation systems [4]. However, due to their slow redox reaction rates, their performance and stability are limited and VRFBs would thus benefit from enhancement of the electrode kinetics [5]. Various catalysts have been employed to improve the redox reaction rates [5-6]. Bismuth is considered a suitable candidate due to its good reactivity with oxygen, low toxicity, relatively low cost and comparable catalytic properties to other metal competitors [5, 7-8]. In this study, we present a novel modified VRFB electrode composed of carbonized bismuth-polyaniline (Bi-PANI) on a thermally-treated carbon paper substrate. The battery performance was evaluated in a 5 cm2 flow cell using a ‘zero-gap’ design with a 1.6 M VOSO4 in 3 M H2SO4 electrolyte. Charge-discharge of the VRFB was performed at a constant current density (ranging from 10 to 80 mA cm−2) with upper and lower voltage cut-offs of 1.65 and 0.8 V, respectively. Thermally treated carbon paper electrodes were tested as a comparison, and it was found that the VRFB efficiency was significantly enhanced, from 51% to 68% at a current density of 80 mA cm− 2, due to the modification of the electrodes with Bi-PANI. The charge-discharge capacity was also improved by around 17.2%. In addition, the stability of the battery using the modified electrodes was evaluated for over 200 cycles. The improved battery performance was attributed to the catalytic effect of the Bi particles on the VO2+/VO2 + and V2+/V3+ redox reactions leading to a significant increase in capacity, voltage, and energy efficiency. References: [1] M. Skyllas-Kazacos, L. Cao, M. Kazacos, N. Kausar, A. Mousa, ChemSusChem. 9 (2016) 1521–1543. [2] A.K. Singh, M. Pahlevaninezhad, N. Yasri, E. Roberts, ChemSusChem. (2021). [3] M. Pahlevaninezhad, P. Leung, M. Pahlevani, F. C. Walsh, C. Ponce de Leon, and E. P. L. Roberts, Experimental and Computational Studies of Disperse Blue-1 in Organic Non-Aqueous Redox Flow Batteries, J. Power Sources, Volume 500, 15 July 2021, 229942. [4] X.Z. Yuan, C. Song, A. Platt, N. Zhao, H. Wang, H. Li, K. Fatih, D. Jang, Int. J. Energy Res. (2019). [5] S. Moon, B.W. Kwon, Y. Chung, Y. Kwon, Journal of The Electrochemical Society, 166 (12) A2602-A2609 (2019) [6] W. Lee, C. Jo, S. Youk, H. Y. Shin, J. Lee, Y. Chung, and Y. Kwon, Appl. Surf. Sci., 429, 187 (2018) [7] Z González, A. Sánchez, C. Blanco, M. Granda, R. Menéndez, and R. Santamaria, Electrochem. Commun., 13, 1379 (2011). [8] B. Li, M. Gu, Z. Nie, Y. Shao, Q. Luo, X. wei, X. Li, J. Xiao, C. Wang, V. Sprenkle, and W. Wang, Nano Lett., 13, 1330 (2013).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".