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Record W2509218561 · doi:10.1149/ma2016-02/53/3920

Tetraethylammonium Hydroxide with Polyacrylamide As Hydroxide Conducting Polymer Electrolytes for Electrochemical Capacitors

2016· article· en· W2509218561 on OpenAlexaff
Jak Li, Keryn Lian

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectrolyteMaterials sciencePolymerIonic conductivityChemical engineeringHydroxidePolyacrylamideElectrochemistryPotassium hydroxideInorganic chemistryElectrodeChemistryPolymer chemistryComposite material

Abstract

fetched live from OpenAlex

Introduction Alkaline electrolytes, especially KOH, have been extensively used for energy storage devices such as batteries and electrochemical capacitors (ECs) in symmetric and asymmetric configurations [1-3]. An alkaline polymer electrolyte enables safe, thin, and flexible devices. Due to the limitation of crystallization with KOH, quaternary ammonium hydroxide based polymer electrolytes, specifically, tetraethylammonium hydroxide (TEAOH) in polyvinyl alcohol (PVA) was studied and shown to be a suitable alternative to KOH-PVA [4]. Solid AC-line filtering devices were enabled using this TEAOH-PVA polymer electrolyte [5]. However, PVA is a semi-crystalline polymer with limitations in water retention which deters the electrochemical performance of these alkaline polymer electrolytes. In this study, our objectives are to (a) develop an alkaline polymer electrolyte using TEAOH with polyacrylamide (PAM) as an alternative polymer matrix to PVA, (b) characterize the polymer electrolyte in terms of its long term ionic conductivity and material properties, and (c) fabricate solid EC devices and compare them with a liquid analogue. Experimental Aqueous TEAOH-PAM polymer electrolytes were prepared in different precursor solutions. Smooth metallic nickel (Ni) foils were used as electrodes with 1 cm2 areas. Two-electrode test vehicles were made by sandwiching TEAOH-PAM between the Ni electrodes and sealed with a chemically inert tape. Solid EC devices were made by sandwiching TEAOH-PAM between CNT-graphite electrodes on a Ni substrate with 1 cm2geometric surface areas. All cell characterizations were performed under ambient conditions unless otherwise specified. Results The ionic conductivity of the polymer electrolytes with different TEAOH:PAM ratios were measured and demonstrated remarkable ionic conductivities up to ca. 20 mScm-1 in the pristine condition. The trend revealed an optimal composition with 75-25 weight ratio of TEAOH-PAM. As shown in Figure 1, the TEAOH-PAM cells were tracked over a period of 50 days under ambient conditions and demonstrated good environmental stability. Further conditioning the cells under 45% RH showed an appropriate representation of the cells under steady-state. Details regarding ionic conductivity and long term stability will be discussed and related to the structural and chemical characterizations of these TEAOH-PAM polymer electrolytes. Finally, solid EC device performances will be compared to liquid TEAOH devices. References [1] K.-W. Nam and K.-B. Kim, "A study of the preparation of NiO x electrode via electrochemical route for supercapacitor applications and their charge storage mechanism," Journal of the Electrochemical Society, vol. 149, pp. A346-A354, 2002. [2] http://www.elton-cap.com/products/ [3] Y.-G. Wang, Z.-D. Wang, and Y.-Y. Xia, "An asymmetric supercapacitor using RuO 2/TiO 2 nanotube composite and activated carbon electrodes," Electrochimica Acta, vol. 50, pp. 5641-5646, 2005. [4] H. Gao, J. Li, K. Lian., "Alkaline quaternary ammonium hydroxides and their polymer electrolytes for electrochemical capacitors," RCS Advances, vol. 4, pp. 21332-21339, 2014. [5] H. Gao, J. Li, J. R. Miller, R. A. Outlaw, S. Butler, and K. Lian, "Solid-state electric double layer capacitors for ac line-filtering," Energy Storage Materials, vol. 4, pp. 66-70, 2016. 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.237
Teacher spread0.221 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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