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Record W1588054339

Cost analysis for durable proton exchange membrane in PEM fuel cells

2012· article· en· W1588054339 on OpenAlex
Ali Taleb, Erik Kjeang, Elicia Maine

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

VenuePortland International Conference on Management of Engineering and Technology · 2012
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsProton exchange membrane fuel cellAutomotive industryDurabilityProduction (economics)Investment (military)Computer scienceProcess engineeringFuel efficiencyGasolineDiesel fuelAutomotive engineeringReliability engineeringEnvironmental economicsManufacturing engineeringEngineeringFuel cellsWaste managementEconomics
DOInot available

Abstract

fetched live from OpenAlex

Proton exchange membranes (PEMs) are a major determinant of fuel cell lifetime. For automotive applications, standards call for high levels of operation stability, reportedly 5,500 hours for cars and over 20,000 hours for buses. In addition to durability, membranes should also meet a certain price target for fuel cells to be competitive with incumbent gasoline and diesel internal combustion engines. A techno-economic analysis has been performed to explore different membrane designs which are proposed to enhance durability. For this reason, a cost analysis platform has been created. The technical-economic cost model (TCM) developed depicts how the production cost per unit varies depending on the different fabrication methods, production rate limitations, material selection, labor distribution, energy consumption, financial parameters and the target production volume. This platform enables the efficient exploration of each potential design solution and identification of the key factors for each design. By using such an approach in the design, research time and resources can be saved by prioritizing RD the effect of additive on the overall cost is minor especially when the production process is unchanged. Comparing the results to existing market standards, we found that current industry standard assumptions are intended for conservative investment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.023
GPT teacher head0.242
Teacher spread0.218 · 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