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Record W3025665596 · doi:10.1149/ma2020-012253mtgabs

Gel Polymer Electrolyte Made of an Amorphous Polyacrylonitrile-Based Elastomer

2020· article· en· W3025665596 on OpenAlexaff
Nina Verdier, David Lepage, Ramzi Zidani, Arnaud Prébé, David Aymé‐Perrot, Christian Pellerin, Mickaël Dollé, Dominic Rochefort

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMaterials scienceIonic conductivityPolyacrylonitrilePolymerCrystallinityElectrolyteChemical engineeringElastomerAcrylonitrileLithium (medication)Polymer chemistryComposite materialChemistryCopolymer

Abstract

fetched live from OpenAlex

Lithium-ion batteries (LiBs) are a mature technology which has attracted much attention over the last three decades with the development of portable devices. With the increasing number of LiBs and their use in electric vehicles, there are persistent efforts to enhance their safety by identifying suitable substitutes for liquid non-aqueous electrolytes, which are highly flammable, volatile, and can cause leakage. To this end, gel polymer electrolytes (GPEs) represent a promising alternative. To be suitable in applications and maximize ionic conductivity, polymers must meet specific requirements, the most important ones being a low glass transition temperature (T g ) and a low crystallinity, as ions move faster in amorphous phase. Moreover, these polymers need to be electrochemically stable and provide mechanical resistance as well as flexibility to the GPE. To increase the conductivity, polymers with polar functional groups that can dissolve salts are required. Several polymers, like PEO or PAN have already been studied but one limitation is their high T g (above ambient temperature) as well as a high crystallinity which make them brittle and limit the ionic conductivity. To circumvent this limitation, we studied an amorphous polymer based on acrylonitrile monomers. This elastomer, HNBR (Hydrogenated Nitrile Butadiene Rubber), was previously studied by our group and was shown to be thermally cross-linkable, leading to a polymer chemically resistant to electrolytes and electrochemically stable over a wide potential range. Hence, we propose to investigate thermally cross-linked HNBRs to gain a better understanding on the effect of interactions between polar nitrile functions and lithium ions and finally use this system as a GPE for Li-ion battery. To do so, a three-component system comprising HNBR:solvent:LiTFSI was studied to pinpoint the correct ratio to provide the GPE with competitive conductivity. Infrared spectroscopy was used to shed light on the interactions between nitriles and lithium ions while PFG-NMR was utilized to obtain spin-lattice relaxation times (T 1 ) and diffusion coefficients of 7 Li and 19 F for various HNBR-based GPEs. These results were correlated to EIS measurements and among the GPEs tested, those composed of 2M LiTFSI in propylene carbonate and HNBR with an acrylonitrile content of 50% are the most promising. This study highlights the benefits of high acrylonitrile content in the polymer and the use of a solvent with moderate donor number to promote interactions between nitriles and Li + .

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.195
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.208
Teacher spread0.198 · 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 teacher head, 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
Published2020
Admission routes1
Has abstractyes

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