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Record W4320169428 · doi:10.20517/energymater.2022.84

Zn-based batteries for energy storage

2023· article· en· W4320169428 on OpenAlex

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

VenueEnergy Materials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Waterloo
FundersGovernment of Jiangsu ProvinceNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsSustainable energyEnergy storageField (mathematics)Energy (signal processing)Science and engineeringComputer scienceTelecommunicationsEngineering physicsData scienceEngineeringEngineering ethicsRenewable energyElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Zn-based electrochemistry is considered to be the most promising alternative to Li-ion batteries due to its abundant reserves and cost-effectiveness. In addition, aqueous electrolytes are more convenient to be used in Zn-based batteries due to their good compatibility with Zn-chemistry, thereby reducing cost and improving safety. Furthermore, Zn2+/Zn couples involve two-electron redox chemistry, which can provide higher theoretical energy capacity and energy density. Based on this, a series of Zn-based battery systems, including Zn-ion batteries, Zn-air batteries, and Zn-based redox flow batteries, have received more and more research attention. Here, the fundamentals and recent advances in Zn-based rechargeable batteries are presented, along with perspectives on further research directions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.080
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0030.000

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 head0.022
GPT teacher head0.242
Teacher spread0.220 · 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