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Enregistrement W3022567685 · doi:10.1149/ma2018-01/44/2604

Decorating Graphene Oxide with Ionic Liquid Nanodroplets: An Approach Leading to Energy Dense, High Voltage Supercapacitors

2018· article· en· W3022567685 sur OpenAlexaff
Zimin She, Debasis Ghosh, Michael A. Pope

Notice bibliographique

RevueECS Meeting Abstracts · 2018
Typearticle
Langueen
DomaineMaterials Science
ThématiqueSupercapacitor Materials and Fabrication
Établissements canadiensUniversity of Waterloo
Organismes subventionnairesnon disponible
Mots-clésSupercapacitorCapacitanceElectrolyteMaterials scienceIonic liquidPower densityIonic conductivityElectrodeGrapheneElectrochemistryCapacitorEnergy storageChemical engineeringElectrochemical windowOxideVoltageNanotechnologyElectrical engineeringChemistryPower (physics)ThermodynamicsOrganic chemistryPhysics

Résumé

récupéré en direct d'OpenAlex

Supercapacitors, also known as electric double-layer capacitors (EDLCs), are able to store energy rapidly and reversibly through the formation of a double-layer of electronic and ionic charge, closely spaced, at the electrode/electrolyte interface.[1-2] Due to the combination of various advantageous properties, such as efficient operation at high power density, long cycle life and improved safety compared to Li-ion batteries,[3,4] supercapacitors are being increasingly used as alternative power sources to rechargeable batteries. However, the implementation of supercapacitors in practical application is still restricted by the limited energy density, typically 5-8 Wh/L,[5] which is much lower than that of lead-acid batteries which can achieve ~50-90 Wh/L.[6] Considering a symmetric configuration, the volumetric energy density (E V) of a supercapacitor is directly proportional to the volumetric capacitance (C V) of a single electrode and the square of operating voltage (U) following the equation, Ev = 1/8(C V·U 2). Therefore, in such a system, there are two ways to improve energy density: boosting capacitance and extending cell voltage window. The operating voltage of EDLCs is typically limited by the stability of electrolyte and thus room temperature ionic liquids (ILs) with large electrochemical stability windows (> 3-4 V) have become promising next-generation electrolytes. However, the relatively high viscosity of ILs results in lower ionic conductivity compared to traditional aqueous or organic electrolytes and also leads to challenges with pore wetting. On the electrode side, materials with a high intrinsic capacitance (C DL) per area and a large ion-accessible surface area (SSA) are needed to achieve high gravimetric capacitance (C G) since C G = C DL·SSA. The potentially high electrical conductivity, surface area, and chemical stability of graphene-based materials make them promising candidate electrode.[7,8] Theoretically, single layer graphene can exhibit SSA as high as 2675 m2/g. While pristine graphene is limited by its low quantum capacitance leading to C DL ~3-4 mF/cm2, more defective and functionalized graphene produced by the chemical or thermal reduction of graphene oxide (GO) have been shown to exhibit C DL > 17 mF/cm2 in non-aqueous electrolyte leading to theoretical gravimetric capacitance, C G,theoretical > 450 F/g if all of graphene’s surface area could be made ion-accessible. To prevent aggregation and restacking of graphene-based materials into lower SSA structures, we demonstrated an IL microemulsion system (Figure 1) that spontaneously assembles on the surface of GO, placing nanometer-sized droplets of a high-performance, hydrophobic IL 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (EMImTFSI) directly onto the available surface of well-dispersed single GO layers. We first demonstrate that a common non-ionic surfactant, Tween 20 is capable of forming a stable microemulsion with EMImTFSI with a particle size on the order of several nanometers. These surfactant stabilized nano-droplets (microemulsion particles) spontaneously adsorb to GO sheets yielding a dispersion which can be cast directly onto current collectors leading to a dense nanocomposite of GO/IL/Tween 20. Tween 20 is then removed by evaporation, while the GO is thermally reduced leading to what we refer to as layered IL-mediated reduced GO (IM-rGO) electrodes. The approach is found to yield high ion-accessible SSA as evidenced by one of the highest C G ever reported (302 F/g) when the composite contains 80 wt% IL. These results indicate that the microemulsion particles formed were better able to deploy IL as a spacer to prevent rGO sheets from restacking. Reducing the IL content to 60 wt%, resulted in dense electrodes that exhibited a C V = 218 F/cm3, which is the highest value reported to date among all graphene-based supercapacitors leading to exceptional volumetric energy density. Reference (1) Conway, B. E. Electrochemical Supercapacitors: Scientific Fundamentals and Technological Applications. Springer Science & Business Media 2013. (2) Miller, J. R.; Simon, P. Electrochemical capacitors for energy management. Science 2008, 321, 651. (3) Du Pasquier, A.; Plitz, I.; Menocal, S.; Amatucci, G. A comparative study of Li-ion battery, supercapacitor and nonaqueous asymmetric hybrid devices for automotive applications. J. Power Sources 2003, 115, 171. (4) Khaligh, A.; Li, Z. Battery, ultracapacitor, fuel cell, and hybrid energy storage systems for electric, hybrid electric, fuel cell, and plug-in hybrid electric vehicles: State of the art. IEEE transactions on Vehicular Technology 2010, 59, 2806. (5) Burke, A. R&D considerations for the performance and application of electrochemical capacitors. Electrochim. Acta 2007, 53, 1083. (6) Linden, D. Handbook of batteries. Fuel and Energy Abstracts 1995, 265. (7) Pope, M. A.; Aksay, I. A. Four-Fold Increase in the Intrinsic Capacitance of Graphene through Functionalization and Lattice Disorder. J. Phys. Chem. C 2015, 119, 20369. (8) Stoller, M. D.; Park, S.; Ruoff, R. S. Graphene-based ultracapacitors. Nano Lett. 2008, 8, 3498. 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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

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 recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

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 Opus0,016
Tête enseignante GPT0,232
Écart entre enseignants0,215 · 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 source (Gemma direct ou Codex distillé), 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
Publié2018
Routes d'admission1
Résumé présentoui

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