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Record W2805143819 · doi:10.1149/ma2018-01/8/774

(Invited) Flexible and Self-Healing Aqueous Supercapacitors By Polyampholyte Gel Electrolytes with Biochar Electrodes and Their Unique Low Temperature Properties

2018· article· en· W2805143819 on OpenAlexaff
Hyun‐Joong Chung

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceChemical engineeringSelf-healing hydrogelsSmall-angle X-ray scatteringSupercapacitorPolymerElectrolyteDielectric spectroscopyNanotechnologyPolymer chemistryComposite materialElectrodeElectrochemistryChemistryScattering

Abstract

fetched live from OpenAlex

Quenched polyampholytes provide a novel class of tough hydrogel that has self-healing ability, strong adhesion, and mechanical flexibility. Understanding the structure of polymer chains in the hydrogel and the phase behavior of water therein has broad impact on various applications, such as lubrication, adhesion, and electrical conductivity, as well as the hydrogel’s low temperature properties. In this paper, the structure of polymer chains of hydrogels made of a model charge-balanced polyampholyte, a random copolymer of poly(4-vinylbenzenesulfonate-co-[3-(methacryloylamino) propyl] trimethylammonium chloride), was investigated by small- and wide-angle x-ray scattering (SAXS and WAXS). The SAXS results suggested a networked globule structure in the charge-balanced polyampholyte hydrogels prevented freezing of water in the hydrogel, while the evidence of non-frozen water at low temperatures, such as –45 °C was monitored by solid-state 2H NMR. Correspondingly, we observed high ionic conductivity at low temperatures using electrochemical impedance spectroscopy (EIS). Interestingly, multiple freezing-thawing cycles did not impact the phase behavior of water in the hydrogel. We also found evidence that the crosslinked network structure of the polyampholyte chains disrupts the crystalline growth of ice, resulting in ‘slush-like’ ice formation. Utilizing the scientific investigations, a flexible and self-healing supercapacitor with high energy density in low temperature operation was fabricated using a polyampholyte hydrogel electrolyte. The electrode material was a biochar (produced from the low-temperature pyrolysis of biological wastes) bound by self-assembled reduced graphene oxide. Our novel sample preparation technique to enable the self-reinforcing wrapping of reduced graphene oxide sheets is turned out to have universal efficacy. The procedures of this technique will be introduced in the presentation. At the room temperature, the fabricated supercapacitor showed high energy density of 30 Wh/kg with 90% capacitance retention after 5000 charge-discharge cycles at room temperature at a power density of 50 W/kg. At –30 °C, the supercapacitor exhibited an energy density of 10.5 Wh/kg at a power density of 500 W/kg, which is threefold increase compared to control sample that do not incorporate polyampholyte hydrogel electrolyte. We also harnessed the tunable optical property of the polyampholyte hydrogel to fabricate a smart window. Specifically, we modulated the overall hydrophilicity/phobicity of polyampholyte chains when synthesizing the random copolymer and adjusted the upper critical solution temperature (UCST) at high precision, thus achieved a fine-tuning of UCST between 15 and 65 °C. Finally, we developed a stretchable, high-contrast, optically tunable stretchable window which consists of the PA hydrogel and a printed stretchable electric heater by our own ink recipe. In summary, we performed fundamental studies on the phase behavior of polymer chains and water molecules in quenched polyampholyte hydrogels by using synchrotron SAXS/WAXS, solid-state NMR, EIS, and DSC. We utilized the understanding in energy storage and smart window applications, both of which are unconventional for the application of tough hydrogels. In addition, we utilized renewable resource of biochar to fabricate low-cost, high-performance electrode material.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.200
Teacher spread0.193 · 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
Published2018
Admission routes1
Has abstractyes

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