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Record W4384484840 · doi:10.1002/celc.202300113

An Insight into Synthesis of the Antifreeze Alkaline Hydrogel Electrolyte: Fine‐Tuning Chemistries for Efficient Ion Transport

2023· article· en· W4384484840 on OpenAlexafffund
Chun Keat Khor, Chance Coady Blackstone, Anna Ignaszak

Bibliographic record

VenueChemElectroChem · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of New Brunswick
FundersNew Brunswick Innovation FoundationCanada Foundation for Innovation
KeywordsElectrolyteGlycerolCryoprotectantAcrylateChemistrySelf-healing hydrogelsIonic bondingChemical engineeringPolymerDiffusionIonPolymer chemistryCopolymerOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Herein, the chemical compatibility of the hydrogel electrolyte in highly alkaline pH was evaluated. Through simple experiments, we demonstrated that the frequently used polymer compound, acrylamide, is not stable at a high pH. The addition of glycerol as a cryoprotectant in highly alkaline hydrogels was also problematic due to the possible base‐initiated decomposition of glycerol to polyglycerols. Hence, a quick and simple one‐pot synthesis of highly alkaline potassium poly(acrylate) hydrogel with 1 vol % glycerol was proposed. The ionic conductivities of the hydrogel are 46.48 mS/cm and 8.67 mS/cm at 22 and −23 °C, respectively. One important benefit from the addition of the cryoprotectant is that the hydrogel sustained its mechanical features at temperatures as low as −80 °C. We also reported here for the first time the diffusion coefficients ( D at ∼10 −8 cm 2 /s), ionic mobilities ( μ at ∼10 −7 cm 2 /Vs), and ion density ( n at ∼10 −7 cm −3 ) of the hydrogel electrolyte used in flexible alkaline batteries.

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 score0.008
Threshold uncertainty score0.671

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 head0.007
GPT teacher head0.211
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 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

Citations1
Published2023
Admission routes2
Has abstractyes

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