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Record W7023891459

Proton exchange membrane fuel cells: contamination and mitigation strategies

2010· book· en· W7023891459 on OpenAlexvenueno aff

Bibliographic record

VenueNPARC · 2010
Typebook
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
Fundersnot available
KeywordsProton exchange membrane fuel cellCommercializationContaminationFuel cellsAir contaminationMicrobial fuel cell
DOInot available

Abstract

fetched live from OpenAlex

Large-scale commercialization of proton exchange membrane fuel cell (PEMFC) technology has been hindered by issues of reliability, durability, and cost, which are all related to the degradation of fuel cell performance. This degradation often has root causes in contamination from fuel, air streams, or system components. With contributions from international scientists and engineers active in PEMFC research, Proton Exchange Membrane Fuel Cells: Contamination and Mitigation Strategies discusses the impacts of contamination and the contamination mitigation strategies to improve fuel cell performance and durability. The book covers the nature, sources, and electrochemistry of contaminants; their effects on fuel cell performance and lifetime; and the mechanisms of contamination. Exploring the major findings from experimental and theoretical studies in contamination-related research, the expert contributors present methods and tools used for diagnosing various contamination phenomena, along with strategies for mitigating the adverse effects of contamination. They also describe key issues in the future R and D of fuel cell contamination and control. Helping to facilitate pioneering PEMFC R and D and accelerate sustainable commercialization, this book contains the latest research efforts and novel developments as well as important new directions in PEMFC contamination. It offers a comprehensive overview of nearly every aspect of fuel cell contamination, from fundamentals to applications.

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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.807
Threshold uncertainty score0.911

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 integrity0.0010.000
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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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
GenreOther

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

Citations2
Published2010
Admission routes1
Has abstractyes

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