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Record W4313490777 · doi:10.1002/adfm.202211736

“Duet‐Insurance” Eutectic Electrolytes for Zinc‐Ion Capacitor Pouch Cells

2023· article· en· W4313490777 on OpenAlexafffund
Xuejun Lu, Li Tao, Keqi Qu, Amardeep Amardeep, Jian Liu

Bibliographic record

VenueAdvanced Functional Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsMaterials scienceCapacitanceElectrolyteEutectic systemElectrochemistryCapacitorAnodeAqueous solutionChemical engineeringRaman spectroscopySupercapacitorComposite materialVoltageAlloyChemistryElectrical engineeringPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Abstract Eutectic electrolytes are emerging to be explored to meet the high demand for aqueous zinc‐ion capacitors (AZICs) due to their high electrochemical stability and environmental‐friendly feature. Notably, a large‐scale requirement for a practical AZIC has been addressed to balance decent performance and increase package size. Thus, a “duet‐insurance” eutectic electrolyte (DIEE) is proposed by introducing successively heavy water (D 2 O) and dimethyl sulfoxide in an aqueous chaotropic Zn salt solution, which hindered the severe irreversibility of Zn anodes compared to that lacking this dual‐solvent protection. For example, the optimal DIEE delivered a high capacitance of 352.9 F g −1 at 0.5 A g −1 and excellent capacitance retention of ≈100% over 30,000 cycles at 2 A g −1 implementing in pouch cells and showing the ability to operate at a low temperature. Besides, experimental (e.g., nuclear magnetic resonance spectroscopy, Raman spectroscopy, electric double‐layer capacitance, etc.) and theoretical techniques (e.g., molecular dynamics simulation, density functional theory) illustrate the vital presence of contact ion pairs and aggregates ion species. Ultimately, this efficient DIEE system employing “duet‐insurance” protections provides a promising direction for designing novel eutectic electrolytes for AZICs chemistries.

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 score0.002

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)0.0010.000

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 head0.018
GPT teacher head0.248
Teacher spread0.230 · 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

Citations38
Published2023
Admission routes2
Has abstractyes

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