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Record 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 on OpenAlexaff
Zimin She, Debasis Ghosh, Michael A. Pope

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSupercapacitorCapacitanceElectrolyteMaterials scienceIonic liquidPower densityIonic conductivityElectrodeGrapheneElectrochemistryCapacitorEnergy storageChemical engineeringElectrochemical windowOxideVoltageNanotechnologyElectrical engineeringChemistryPower (physics)ThermodynamicsOrganic chemistryPhysics

Abstract

fetched live from 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

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.016
GPT teacher head0.232
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
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