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Record W4404818483 · doi:10.1002/adfm.202418073

A Proton Selective Carbon Nitride Layer for High Durability Fuel Cells

2024· article· en· W4404818483 on OpenAlexaff
Keenan Smith, Fabrizia Foglia, Adam J. Clancy, Michael A. Pope, Dan J. L. Brett, Thomas S. Miller

Bibliographic record

VenueAdvanced Functional Materials · 2024
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Waterloo
FundersEngineering and Physical Sciences Research CouncilNatural Environment Research CouncilRoyal SocietyRoyal Academy of Engineering
KeywordsMaterials scienceDurabilityLayer (electronics)NitrideCarbon fibersCarbon nitrideGraphitic carbon nitrideComposite materialNanotechnologyComposite numberOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract To achieve a balance between performance and durability in electrochemical energy conversion systems, such as fuel cells (FC) and water electrolyzers (WE), proton exchange membranes (PEMs) must be optimized for minimal thickness and resistance while maximizing gas rejection and durability. 2D materials, with Angstrom‐scale pores, hold the potential to revolutionize these devices by enabling highly selective proton transport and mitigating degradation pathways. However, to date no material has been implemented that can prevent gas crossover and extend the device lifetime without compromising initial performance. In this study, it is demonstrated that polytriazine imide (PTI), a 2D graphitic carbon nitride with optimally sized and functionalized lattice pores, facilitates unimpeded proton transport. By engineering a centimeter‐scale monolayer film composed of tessellated PTI nanosheets and placing it at the cathode‐PEM interface, significant gains in performance, efficiency, and durability ‐are achieved. These results in PEMFCs show halved hydrogen crossover and over a threefold increase in lifetime. This approach promises to accelerate the adoption of economically viable FCs and WEs with enhanced output and extended service lifetimes, essential for achieving a decarbonized society.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.908

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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 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

Citations3
Published2024
Admission routes1
Has abstractyes

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