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Record W2898127216 · doi:10.1002/gch2.201800023

Ionic Liquids as Environmentally Benign Electrolytes for High‐Performance Supercapacitors

2018· review· en· W2898127216 on OpenAlexaff
Suniya Shahzad, Afzal Shah, Elaheh Kowsari, Faiza Jan Iftikhar, Anum Nawab, Benoı̂t Piro, Mohammad Salim Akhter, Usman Ali Rana, Yongjin Zou

Bibliographic record

VenueGlobal Challenges · 2018
Typereview
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsSupercapacitorElectrolyteIonic liquidCapacitorMaterials scienceElectrochemistryEnergy storageThermal stabilityIonic conductivityPower densityNanotechnologyVoltageElectrical engineeringChemical engineeringPower (physics)ChemistryElectrodeEngineering

Abstract

fetched live from OpenAlex

Electrochemical capacitors (ECs) are a vital class of electrical energy storage (EES) devices that display the capacity of rapid charging and provide high power density. In the current era, interest in using ionic liquids (ILs) in high-performance EES devices has grown exponentially, as this novel versatile electrolyte media is associated with high thermal stability, excellent ionic conductivity, and the capability to withstand high voltages without undergoing decomposition. ILs are therefore potentially useful materials for improving the energy/power performances of ECs without compromising on safety, cyclic stability, and power density. The current review article underscores the importance of ILs as sustainable and high-performance electrolytes for electrochemical capacitors.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.044
GPT teacher head0.290
Teacher spread0.246 · 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

Citations73
Published2018
Admission routes1
Has abstractyes

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