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Record W4386840657 · doi:10.1016/j.scib.2023.08.063

Practicable Zn metal batteries enabled by ultrastable ferromagnetic interface

2023· article· en· W4386840657 on OpenAlexaff
Chuang Sun, Wenduo Zhang, Daping Qiu, Minman Tong, Zhangsen Chen, Shuhui Sun, Chao Lai, Yanglong Hou

Bibliographic record

VenueScience Bulletin · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNational Key Research and Development Program of ChinaNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsFabricationAnodeMaterials scienceFerromagnetismPlating (geology)Stripping (fiber)Deposition (geology)CommercializationDurabilityZincNanotechnologyMetallurgyChemical engineeringElectrodeComposite materialChemistry

Abstract

fetched live from OpenAlex

Rechargeable zinc (Zn) metal batteries (RZMBs) are demonstrated as sustainable and low-cost alternative in the energy storage industry of the future. However, the elusive Zn deposition behavior and water-originated parasitic reactions bring significant challenges to the fabrication and commercialization of Zn anodes , especially under high plating/stripping capacity. In this work, the ferromagnetic interface in conjunction with the magnetic field (MF) to effectively address these fabrication hurdles is proposed. The introduction of ferromagnetic layer with high chemical durability not only maintains the long-term regulating deposition steadily by magnetic field , but also plays a significant role in preventing side reactions, hence reducing gas production. These merits allow Zn-anode to achieve over 350 h steady Zn-deposition with a depth of discharge (DOD Zn ) up to 82% and translates well to ZnFe-MF||V 2 O 5 full cells, supporting stable cycling at high mass loading of 13.1 mg/cm 2 , which makes RZMBs configurations promising for commercial applications.

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 score0.004

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)0.0010.001

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 head0.274
Teacher spread0.260 · 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

Citations28
Published2023
Admission routes1
Has abstractyes

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