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Enregistrement W3024640838 · doi:10.1149/ma2020-013484mtgabs

The Effect of Metal Impurities on the All-Vanadium Redox Flow Battery Performance

2020· article· en· W3024640838 sur OpenAlexaff
Maedeh Pahlevaninezhad, Majid Pahlevani, Edward P.L. Roberts

Notice bibliographique

RevueECS Meeting Abstracts · 2020
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvanced battery technologies research
Établissements canadiensQueen's UniversityUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésElectrolyteVanadiumMaterials scienceFlow batteryElectrodeBattery (electricity)RedoxElectrochemistryInorganic chemistryChemistry

Résumé

récupéré en direct d'OpenAlex

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 (Mn 2+ , Fe 2+ and Al 3+ ) on the performance of VRFBs. A three-electrode system was utilized to conduct cyclic voltammetry (CV) experiments. Carbon paper (thermally treated at temperature of 500 o 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 cm 2 . The electrolytic solution (1 M VOSO 4 solution in 3 M H 2 SO 4 ) 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 (Mn 2+ , Fe 2+ and Al 3+ ) showed that the highest peak separation was obtained in the presence of Fe 2+ . The peak separations for Mn 2+ and Al 3+ 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 Al 3+ and Fe 2+ . 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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,850
Score d'incertitude au seuil0,448

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,015
Tête enseignante GPT0,236
Écart entre enseignants0,221 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2020
Routes d'admission1
Résumé présentoui

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