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Record W4286452465 · doi:10.1002/batt.202100420

Ethylene Glycol as an Antifreeze Additive and Corrosion Inhibitor for Aqueous Zinc‐Ion Batteries

2022· article· en· W4286452465 on OpenAlexaff
Thuy Nguyen Thanh Tran, Maosen Zhao, Shujiang Geng, Douglas G. Ivey

Bibliographic record

VenueBatteries & Supercaps · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEthylene glycolAntifreezeAqueous solutionElectrolyteZincCorrosionMaterials scienceInorganic chemistryPrecipitationEthyleneIonChemical engineeringChemistryMetallurgyOrganic chemistryElectrodeCatalysis

Abstract

fetched live from OpenAlex

Abstract Aqueous zinc‐ion batteries are promising candidates for portable and large‐scale applications because of their intrinsically high safety, low cost and high theoretical energy density. However, existing aqueous zinc‐ion batteries usually suffer from zinc corrosion and poor performance at subzero temperatures. Herein, to address these problems, the electrolyte in aqueous zinc‐ion batteries (1 M ZnSO4) is modified by adding suitable amounts of ethylene glycol. The addition of ethylene glycol improves the antifreezing ability of the aqueous electrolyte and increases the conductivity of electrolyte at low temperatures. Ethylene glycol serves two purposes, as an antifreeze additive to significantly enhance the capacity of batteries at low temperatures and as a corrosion inhibitor to suppress zinc corrosion and byproduct precipitation. This work offers a facile strategy to realize aqueous zinc‐ion batteries with good performance at low temperatures.

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 score0.001

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 communication0.0000.000
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 head0.014
GPT teacher head0.257
Teacher spread0.243 · 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

Citations49
Published2022
Admission routes1
Has abstractyes

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