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Record W2517046676 · doi:10.1149/ma2016-02/7/953

Aqueous-Based Polymer Electrolytes for Solid Energy Storage Systems

2016· article· en· W2517046676 on OpenAlexaff
Keryn Lian, Jak Li, Alvin Virya, Yee Wei Foong, Haoran Wu, Han Gao

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectrolyteMaterials scienceIonic conductivityPolymerChemical engineeringSupercapacitorEnergy storageAqueous solutionElectrochemistryChemistryElectrodeOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

The rapid growth in printable and wearable electronics has significantly increased the demand for high performance thin, flexible, and light-weight power sources. Solid-state energy storage devices, batteries and supercapacitors, enabled by polymer electrolytes are ideal solutions for such applications. In order to facilitate and promote high throughput and low cost (materials and processes) solid energy storage devices for wearable electronics, the polymer electrolytes should be highly conductive, easy to process, and possess good chemical and environmental stability in ambient conditions. We have developed a series of aqueous-based polymer electrolytes that are proton-conducting, hydroxide ion-conducting or neutral salt ion-conducting to match various cell chemistries. These aqueous-based polymer electrolytes can be applied via casting or printing methods and can potentially be implemented in roll-to-roll operations. One example is the polyacrylamide (PAM) system, in which ionic conducting species together with additives were blended into aqueous polymer matrix. In this work, three ionic conducting systems based on silicotungstic acid (SiWA) as proton-conductor [1], tetraethylammonium hydroxide (TEAOH) as anion conductor [2] and LiCl as neutral ion conductor were investigated. These three electrolytes all exhibited ionic conductivities > 10-2mS/cm and maintained stable performance under ambient conditions (room temperature and 45% relative humidity). Figure 1 shows a comparison of these 3 polymer electrolytes in terms of their ionic conductivity as a function of storage time. In this talk, an overview of these three polymer electrolytes will be provided. Materials and electrochemical characterizations of the polymer electrolytes as well as their performance in electrochemical double layer capacitors will be discussed and compared. References: [1] H. Gao and K. Lian, "Proton-Conducting Polymer Electrolytes and Their Applications in Solid Supercapacitors: A Review", RSC Advances, 2014, 4, 33091-33113. [2] H. Gao, J. Li, and K. Lian, "Alkaline Quaternary Ammonium Hydroxides and their Polymer Electrolytes for Electrochemical Capacitors", RSC Advances, 2014, 4, 21332-21339. Figure 1

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.008
GPT teacher head0.213
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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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