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Record W4399248532 · doi:10.1007/s12598-024-02705-w

Electrolytes additives for Zn metal anodes: regulation mechanism and current perspectives

2024· article· en· W4399248532 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

VenueRare Metals · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Alberta
FundersChangzhou UniversityNational Natural Science Foundation of China
KeywordsElectrolyteCurrent (fluid)AnodeMechanism (biology)MetalMaterials scienceInorganic chemistryChemistryMetallurgyElectrodeEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract With distinct advantages such as high gravimetric and volumetric capacity (5855 mAh·cm −3 and 820 mAh·g −1 ), low redox potential (− 0.762 V vs. standard hydrogen electrode (SHE)), high abundance, low toxicity and intrinsic safety of Zn metal anode, Zn‐ion batteries have become a potential alternative to Li‐ion batteries. However, several challenges still need to be addressed prior to the practical applications of Zn‐ion batteries, such as dendrite growth during Zn plating/stripping and interfacial side reactions on the Zn surface. Such issues can be addressed by introducing additives to regulate the components and structures of the electrolyte. In this review, we systematically discussed the core issues of metallic Zn anodes and comprehensively summarized a novel perspective of the regulation mechanism of inhibiting dendrite growth or interfacial side reactions in Zn anodes by introducing additives into aqueous electrolytes. Furthermore, some discussions and prospects for aqueous Zn ion batteries (AZIBs) are presented for future research.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.552

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 studies0.0000.000
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 head0.019
GPT teacher head0.295
Teacher spread0.277 · 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