The Effect of Metal Impurities on the All-Vanadium Redox Flow Battery Performance
Bibliographic record
Abstract
Vanadium redox flow batteries (VRFB) are a rapidly emerging technology for grid scale energy storage and the integration of the renewable energy generation. However, the cost of the electrolyte is a major barrier for implementation of VRFBs [1, 2]. The quality of the electrolyte has a significant impact on the cell performance and cost. The presence of impurities even with low concentrations in the vanadium electrolyte solution can alter the stability of the electrolyte and influence cell performance, energy density, operating temperature range, electrochemical kinetics and production costs [1-4]. Because a universal standard for the electrolyte specifications has not been defined in the market, a high purity electrolyte is always favored by researchers and customers to avoid potential detrimental impacts of impurities on the system performance. High purity greatly contributes to the high cost of electrolyte for VRFBs. Thus, understanding the impact of the impurities present in electrolyte on the performance of VRFBs is vital for commercialization of VRFBs [5]. This paper aims to conduct a systematic study on the effect of iron, aluminum and manganese ions (Mn2+, Fe2+ and Al3+) on the performance of VRFBs. A three-electrode system was utilized to conduct cyclic voltammetry (CV) experiments. Carbon paper (thermally treated at temperature of 500o C for 1 h in an air atmosphere), a platinum wire, and a saturated calomel electrode (SCE), were used as the working, counter and reference electrodes, respectively. The battery performance was evaluated in a flow cell using a ‘zero-gap’ cell design with an electrode area of 5 cm2. The electrolytic solution (1 M VOSO4 solution in 3 M H2SO4) was circulated through the cell. Thermally treated carbon papers were used as cathode and anode electrodes. For charge-discharge experiments, constant current density (10, 20, 30, 40 and 60 mAcm−2) was applied with 1.65 and 0.8 V as upper and lower voltage limits. The effects of each impurity were studied at five different concentrations (0.02, 0.04, 0.06, 0.08 & 0.1 M) through CV and charge-discharge experiments. The CV results shown in Figure 1 indicate that side reactions of gas evolution from water electrolysis will increase as the concentrations of the metal impurities increase. Comparison of the effect of three transition metal ions (Mn2+, Fe2+ and Al3+) showed that the highest peak separation was obtained in the presence of Fe2+. The peak separations for Mn2+and Al3+ were almost the same. Figure 2 shows the battery performance obtained in the zero-gap cell for a range of current densities versus cycle number, for a pure electrolyte and in the presence of aluminum and iron. The coulombic efficiency improved as the current density increased. Generally, the presence of impurities affected the coulombic efficiency and could result in side reactions and capacity fading, which will have a negative effect on the battery performance. The voltage and energy efficiencies obtained for a range of current densities are shown in Figures 2 (b) and (c), respectively. Similar to the coulombic efficiency results, the highest voltage efficiency was observed with the pure electrolyte. Based on the cyclic voltammetry results, the kinetics of the vanadium reaction falls, and the peak separation increased with increasing concentration of the contaminant metal ions Al3+ and Fe2+. Thus, the voltage efficiency decreased in the presence of these metal ions in the electrolyte. The energy efficiencies have the same trend and illustrates that the contaminant metal ions are competitive with vanadium ions for adsorption on the electrode surface and thus affect the vanadium redox reaction kinetics [6]. The side reactions caused by the metal ions have a negative influence on the performance of VRFBs. [1] A. Parasuraman, T.M. Lim, Ch, Menictas, M. Skyllas-Kazacos, A review of electrolyte additives and impurities in vanadium redox flow batteries, Electrochimica Acta, 27-40, 2013. [2] Cao, L., Skyllas-Kazacos, M., Menictas, Ch., Noack, A review of electrolyte additives and impurities in vanadium redox flow batteries, Energy Chemistry, pp.1269-1291, 2018. [3] A.K. Singh., N. Yasri., K. Karan, E.P. L. Roberts, Electrocatalytic Activity of Functionalized Carbon Paper Electrodes, ACS Appl. Energy Mater. 2019, 2324−2336. [4] John, J. St., Imergy uses recycled vanadium to cut materials costs for flow batteries, Greentech media, 2014. [5] J.H. Park, J.J. Park, H.J Lee, B.S. Min, J.H. Yang, Influence of Metal Impurities or Additives in the Electrolyte of a Vanadium Redox Flow Battery, The Electrochemical Society, page 1263-1268, 2018. [6] Ding, M., Liu, T., Zhang, Y., Cai, Z., Yang, Y., Yuan, Y., Effect of Fe(III) on positive electrolyte for vanadium redox flow battery, R. SOC. open sci. 181309, 2019. Figure 1
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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 | 0.001 |
| 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 | 0.000 |
| 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".