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Record W3024144323 · doi:10.1149/ma2020-0110866mtgabs

Printing of Graphene Supercapacitors with Enhanced Capacitances Induced By a Leavening Agent

2020· article· en· W3024144323 on OpenAlexaff
Minh-Hao Pham, Ali Khazaeli, Gabrielle Godbille, Brant A. Peppley, Dominik P. J. Barz

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsQueen's University
Fundersnot available
KeywordsSupercapacitorGrapheneMaterials scienceNanotechnologyCapacitanceFabricationEnergy storageElectrodeChemistryPower (physics)

Abstract

fetched live from OpenAlex

The rapid development of portable and wearable electronics has been driving a demand for lightweight and flexible energy storage systems. In this regard, flexible supercapacitors are considered as promising since they possess long cycle life and high power rates. Among available materials for supercapacitor electrodes, graphene has gained considerable attention in recent years, due to its high specific surface area, high thermal and electronic conductivity as well as other favourable mechanical features. A key challenge for the development of graphene supercapacitors is associated with the scalability and simplicity of the fabrication process. Although printing technologies offer a rapid, precise, scalable, and cost-effective fabrication method for energy storage systems, printed graphene-based supercapacitors usually deliver a lower capacitance compared to those fabricated with other methods, which is mainly related to the re-stacking of the graphene sheets after printing. This work presents a straightforward and scalable leavening agent-assisted approach in order to enhance the capacitance of printed flexible graphene-based supercapacitors. Our method consists of increasing the surface area of the reduced graphene oxide electrodes (rGO) due to the addition of the leavening agent ammonium carbonate to the GO ink. The inks are printed with a micro-dispensing technique. During reduction of GO to rGO, the leavening agent decomposes and gases are released which enlarge the inter-flake gaps and therefore suppresses the restacking of the GO flakes. The lateral size is a key parameter that controls the self-alignment of the GO flakes and, thus, has an impact on the capacitance of rGO electrodes. To assess the effect of the lateral flake size, three stock dispersions with different size ranges were prepared. In detail, the processed dispersions contained flakes with lateral sizes in the nano- (< 100 nm), submicro- (0.1 - 1.0 µm) or the micro-meter (> 1.0 µm) range. Experiments were performed to investigate the relationships between GO lateral size, the concentration of leavening agent and the capacitance of the printed rGO electrodes. In absence of the leavening agent, the specific capacitance slightly decreases with an increase of the flake size. In contrast, for inks with leavening agent concentrations of 3.0 and 6.0 wt. %, the specific capacitance increases with increasing flake size. The experiments reveal that there is a maximum specific capacitance of 112.1 ± 6.5 F g-1 at a leavening concentration of 3.7 wt. %. This specific capacitance is significantly higher compared to that of the electrode without the leavening agent approach (62.4 ± 5.2 F g-1) indicating a successful process for increasing the gaps between rGO flakes. With respect to cycle life, it is observed that the risen electrodes maintain better long-term capacitance stability compared to agent-free electrodes. The electrodes with the leavening agent approach feature an 85% capacitance retention after 2,000 charge-discharge cycles at a current density of 1.0 A g-1 while the leavening agent-free electrodes show a gradual capacitance decay to 80 %. 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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.231
Teacher spread0.205 · 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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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