MétaCan
Menu
Back to cohort
Record W2562854090 · doi:10.1002/adma.201601741

Engineered Graphene Materials: Synthesis and Applications for Polymer Electrolyte Membrane Fuel Cells

2016· review· en· W2562854090 on OpenAlex
Daping He, Haolin Tang, Zongkui Kou, Mu Pan, Xueliang Sun, Jiujun Zhang, Shichun Mu

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

VenueAdvanced Materials · 2016
Typereview
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsNational Research Council CanadaWestern University
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceGrapheneElectrolyteDurabilityProton exchange membrane fuel cellMembranePolymerNanotechnologyConductivityFuel cellsElectrochemistryElectrodeComposite materialChemical engineeringEngineering

Abstract

fetched live from OpenAlex

Engineered graphene materials (EGMs) with unique structures and properties have been incorporated into various components of polymer electrolyte membrane fuel cells (PEMFCs) such as electrode, membrane, and bipolar plates to achieve enhanced performances in terms of electrical conductivity, mechanical durability, corrosion resistance, and electrochemical surface area. This research news article provides an overview of the recent development in EGMs and EGM-based PEMFCs with a focus on the effects of EGMs on PEMFC performance when they are incorporated into different components of PEMFCs. The challenges of EGMs for practical PEMFC applications in terms of production scale, stability, conductivity, and coupling capability with other materials are also discussed and the corresponding measures and future research trends to overcome such challenges are proposed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.506
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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