MétaCan
Menu
Back to cohort
Record W2558095361 · doi:10.1002/adma.201604685

Electrically Rechargeable Zinc–Air Batteries: Progress, Challenges, and Perspectives

2016· review· en· W2558095361 on OpenAlexafffund
Jing Fu, Zachary P. Cano, Moon Gyu Park, Aiping Yu, Michael Fowler, Zhongwei Chen

Bibliographic record

VenueAdvanced Materials · 2016
Typereview
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsBattery (electricity)Energy densityMaterials scienceNanotechnologyEnergy storageZincEngineering physicsEngineeringMetallurgy

Abstract

fetched live from OpenAlex

Zinc-air batteries have attracted much attention and received revived research efforts recently due to their high energy density, which makes them a promising candidate for emerging mobile and electronic applications. Besides their high energy density, they also demonstrate other desirable characteristics, such as abundant raw materials, environmental friendliness, safety, and low cost. Here, the reaction mechanism of electrically rechargeable zinc-air batteries is discussed, different battery configurations are compared, and an in depth discussion is offered of the major issues that affect individual cellular components, along with respective strategies to alleviate these issues to enhance battery performance. Additionally, a section dedicated to battery-testing techniques and corresponding recommendations for best practices are included. Finally, a general perspective on the current limitations, recent application-targeted developments, and recommended future research directions to prolong the lifespan of electrically rechargeable zinc-air batteries is provided.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.049
GPT teacher head0.320
Teacher spread0.271 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1,570
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueAdvanced MaterialsSame topicAdvanced battery technologies researchFrench-language works237,207