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Enregistrement W3024887519 · doi:10.1149/ma2020-013493mtgabs

Experimental and Computational Studies of Disperse Blue in Organic Non-Aqueous Redox Flow Batteries

2020· article· en· W3024887519 sur OpenAlexaff
Maedeh Pahlevaninezhad, Puiki Leung, Majid Pahlevani, Carlos Ponce de León, 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-clésEnergy storageRenewable energyProcess engineeringRedoxChemical energyBattery (electricity)Materials scienceFlow batteryEnvironmental scienceChemistryElectrical engineeringPower (physics)EngineeringThermodynamics

Résumé

récupéré en direct d'OpenAlex

Renewable energy sources such as wind and solar are replacing fossil fuels for electricity generation. However, intermittency of wind and solar limits their wide-spread adoptions. The energy fed into the power grid must be matched with the consumer energy demand to prevent blackouts and destabilization of the grid [1, 2]. Converting this intermittent power into a base-load power is a challenge in the energy sector. Energy storage systems (ESSs) can store and supply large scale energy as required to address this challenge. Recently, redox flow batteries (RFBs) have gained practical interest among the other energy storage technologies in light of their long lifetime, independent sizing of power and energy, high round-trip efficiency, scalability and design flexibility, fast response, and low environmental impact [3-5]. Redox-active materials is an important constituent of RFBs since battery cycling performance is highly dependent on redox-active properties such as solubility, redox potential, chemical stability, and cost. Organic redox-active materials have recently received attention as they provide competitive electrochemical characteristics, flexible design, and they are abundant in nature [6]. Aqueous designs face commercial difficulty because RFBs have low energy and power density due to the limited cell voltage of 1.23 V. The limited voltage is due to the evolutions of hydrogen and oxygen in the water electrolysis [7]. Solvent substitution is one solution to enable higher energy densities in RFBs, using non-aqueous solution also provides a large design space for enhancement of material solubility, cell potential and the number of electrons stored in the redox species [7-8]. In this study, a new organic redox molecule, tetra amino anthraquinone (Disperse Blue: DB), is evaluated and compared with other organic systems reported in the literature [5, 7] such as b enzoquinone (BQ), naphthoquinone (NQ), anthraquinone (AQ), tetramethyl piperidinyloxyl (Tempo), and phenylenediamine (PD) in non-aqueous solvent by means of cyclic voltammetry. A three-electrode system was utilized to conduct cyclic voltammetry (CV) experiments using glassy carbon working electrodes. The battery performance was evaluated by using a flow cell design with an electrode area of 2.5 cm 2 . The electrolytic solution: 40 mM DB solution in dimethyl sulfoxide solvent (DMSO) and 1 M Bis (trifluoromethane) sulfonimide lithium salt, was circulated through the cell at a flow rate of 10 cm 3 min -1 . Graphite felt and Nafion 115 were used as the electrode and membrane, respectively. In addition, density functional theory (DFT) calculations were used to better understand the electrochemical behavior of the active quinone molecules at different oxidation states. Figure 1 shows the molecular orbital energy levels (HOMO and LUMO) of the DB organic dye and other similar organic molecules obtained by DFT calculations in DMSO. A relatively small HOMO-LUMO gap means a lower overpotential required for the oxidation and reduction processes [8]. The DB had narrower bandgaps (<3 eV) than other quinone molecules (> 3.9 eV), suggesting that the selected molecule has better kinetics than other organic molecules. The results of the CV and charge-discharge experiments will be presented demonstrating that this organic molecule can improve RFBs energy density. [1] J. Winsberg, C. Stolze, S. Muench, F. Liedl, M.D. Hager, U.S. Schubert., TEMPO/Phenazine Combi-Molecule: A Redox Active Material for Symmetric Aqueous RedoxFlow Batteries, ACSEnergyLett. 2016, 1, 976−980. [2] E.S. Beh, D.D Porcellinis, R.L. Gracia, K.T. Xia, R.G. Gordon, M.J. Aziz, A Neutral pH Aqueous Organic− Organometallic Redox Flow Battery with Extremely High Capacity Retention, ACS Energy Lett. 2017, 2, 639−644. [3] X. Wei, W. Duan, J. Huang, L. Zhang, B. Li, D. Reed, W. Xu,V. Sprenkle, Wei Wang, A High-Current, Stable Nonaqueous Organic Redox Flow Battery, ACS Energy Lett. 2016, 1, 705−711. [4] K.H. Hendriks, C.S. Sevov, M.E. Cook, M.S. Sanford., Multielectron Cycling of a Low-Potential Anolyte in Alkali Metal Electrolytes for Nonaqueous Redox Flow Batteries, ACSEnergyLett. 2017, 2, 2430−2435. [5] P. Leung., A.A. Shah., L. Sanz., C. Flox., J.R. Morante., Q. Xu., M.R. Mohamed., C. Ponce de León., F.C. Walsh., Recent developments in organic redox flow batteries: A critical review, Journal of Power Sources., 360, 243 – 283, 2017. [6] Wei , X., Pan, W., Duan, W., Hollas, A., Yang, Zh., Li, B., Nie, Z., Liu, J., Reed, D., Wang, W., Sprenkle, V., Materials and Systems for Organic Redox Flow Batteries: Status and Challenges, ACS Energy Lett. vol. 29, 2187-2204, 2017. [7] R. Emanuelsson, M. Sterby, M. Strømme, M.S. din, An All-Organic Proton Battery, J. Am. Chem. Soc. 2017, 139, 4828−4834 [8] P. Leung., J. Bu., P.Q. Velasco., M.R. Roberts., N. Grobert., P.S. Grant., Single-Step Spray Printing of Symmetric All-Organic SolidState Batteries Based on Porous Textile Dye Electrodes, Adv. Energy Mater., 1901418, 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)0,000
score de la tête « metaresearch » (Gemma)0,000
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 enseignants0,530
Score d'incertitude au seuil0,523

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
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 ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,022
Tête enseignante GPT0,281
Écart entre enseignants0,259 · 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

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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