MétaCan
Menu
Back to cohort
Record W4318065818 · doi:10.3389/fenrg.2023.1031458

A 2023 update on the performance of ionic-liquid proton-exchange-membrane fuel cells

2023· article· en· W4318065818 on OpenAlexafffund
Allan L. L. East, Manh Cuong Nguyen, Rolf Hempelmann

Bibliographic record

VenueFrontiers in Energy Research · 2023
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Regina
KeywordsIonic liquidElectrolyteProton exchange membrane fuel cellMembranePolymerizationConductivityChemical engineeringCathodeIonic conductivityMaterials sciencePower densityChemistryFuel cellsPower (physics)CatalysisThermodynamicsElectrodeOrganic chemistryPhysicsPolymerEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

A mini-review is provided of the literature concerning the performance of proton-exchange-membrane fuel cells (PEMFCs) containing (immobilized) ionic liquids as possible electrolytes, focusing on papers reporting performance metrics (power and current densities). For over a decade, and especially for combined heat and power applications, there has been a drive to design an ionic-liquid-holding membrane that could operate well under non-humidifying (water-free) conditions above 100°C. Such a goal has not yet been achieved: reported power densities are still below those of traditional low-temperature fuel cells. Other recent reviews have already pointed to three main issues: poor conductivity, poor cathode (oxygen-reduction) kinetics, and seepage of the liquid from the membrane. In this review, IL-PEMFCs are grouped into three IL classes (protic, aprotic, and polymerized), performance metric data are summarized, the cases producing the top ten peak power densities of the last 5 years are discussed, and the conductivity mechanisms of the leading cases are elucidated. Purely vehicular protic ILs appear to be too slow, and the trend toward enhanced Grotthuss H + relays via polymerized ILs is revealed.

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 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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.251
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations3
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in Energy ResearchSame topicFuel Cells and Related MaterialsFrench-language works237,207