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Record W3020822687 · doi:10.1002/batt.202000054

Compositional Effects of Gel Polymer Electrolyte and Battery Design for Zinc‐Air Batteries

2020· article· en· W3020822687 on OpenAlex
Thuy Nguyen Thanh Tran, Drew Aasen, Dinara Zhalmuratova, Matthew Labbe, Hyun‐Joong Chung, Douglas G. Ivey

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBatteries & Supercaps · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectrolyteBattery (electricity)ZincMaterials scienceChemical engineeringPolymerAcrylic acidPolymerizationMonomerElectrodeInorganic chemistryChemistryComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Abstract Poly(acrylic acid) (PAA) is a promising polymer host to support alkaline electrolytes in Zn‐air batteries. Herein, precursors containing different concentrations of monomers, crosslinkers and additives such as zinc oxide in alkaline solution are polymerized to fabricate gel polymer electrolytes (GPEs) via one‐pot synthesis. The compositional effects of the GPEs on battery performance are evaluated and a more efficient cell design is demonstrated. With a vertical double air electrode configuration, ZABs using PAA‐based electrolytes show unprecedented performance including high specific energy (913 Wh kg Zn −1 ), excellent cycling stability (at least 160 cycles at 2×10 mA cm −2 ) and high power density output (2×135 mW cm −2 ). The study represents a viable option to replace aqueous electrolytes for high performing ZABs.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.205
Threshold uncertainty score1.000

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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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