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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 OpenAlex
Thuy Nguyen Thanh Tran, Maosen Zhao, Shujiang Geng, 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 · 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 ZnSO 4 ) 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.

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), Insufficient payload (model declined to judge)
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 score0.401
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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.014
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