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Record W4410421437 · doi:10.1016/j.cej.2025.163795

Progress and perspective on thermoplastic composites for hydrogen fuel cells

2025· article· en· W4410421437 on OpenAlexaff
Ali Tahir Manzoor, Vijay K. Tomer, Mohammad Moin Garmabi, Amirjalal Jalali, Nazmus Saadat, Abeer Khan, Ritu Malik, Mohini Sain

Bibliographic record

VenueChemical Engineering Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThermoplastic compositesThermoplasticPerspective (graphical)Composite materialFuel cellsMaterials sciencePolymer scienceEngineeringChemical engineeringComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Proton Exchange Membrane Fuel Cells (PEMFCs) have emerged as a promising and efficient technology for clean energy conversion. However, the progression of this technology has stagnated due to various material issues, specifically concerning the bipolar plate components. Due to the corrosion issues related to metallic bipolar plates, and the poor mechanical properties associated with graphite bipolar plates, more attention has been dedicated to the application of Polymer Matrix Composites (PMC) within this field. This paper first explores the key aspects driven by the desire to overcome the complexities and challenges associated with graphite and metallic bipolar plates and further demonstrates the advancements made within thermoplastics composite bipolar plate design. Since the bipolar plate’s properties are partly dictated by the binding material and the content of functional additives, this review provides an integration of different types of binding materials used so far. In addition, the employed manufacturing approach, and the functional additive behavior (i.e., dispersion, orientation, interfacial adhesion) of the manufacturing approach have also been discussed in detail. Lastly, this review presents the prospects and suggests areas for future research in the field. It highlights potential avenues for further studies and provides recommendations for future work that can contribute to advancements in the design and development of bipolar plates using thermoplastic composite materials

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 score0.066
Threshold uncertainty score0.508

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 head0.003
GPT teacher head0.194
Teacher spread0.191 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

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