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Record W2149970240 · doi:10.1149/1.3502338

Nitrogen Doped Carbon Nanotube Thin Films as Efficient Oxygen Reduction Catalyst for Alkaline Anion Exchange Membrane Fuel Cell

2010· article· en· W2149970240 on OpenAlex

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

VenueECS Transactions · 2010
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCarbon nanotubeEthylenediamineCatalysisMaterials scienceChemical engineeringInorganic chemistryCathodeNitrogenCarbon fibersAlkaline fuel cellMembraneThin filmIon exchangeNanotechnologyIonChemistryComposite materialOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

Nitrogen doped carbon nanotubes (N-CNTs) were synthesized from three different aliphatic diamine compounds. Ethylenediamine based N-CNTs (ED-CNTs) were found to have the highest nitrogen content and displayed significant oxygen reduction reaction (ORR) activity. ED-CNTs were fabricated into a thin, free standing film for use as cathode layer in an alkaline anion exchange membrane electrode assembly (MEA). These thin films displayed significantly higher performance in the alkaline MEA setup when compared with commercial carbon supported platinum (Pt/C). The increase in performance was attributed to the distinct structural properties of N-CNTs and enhanced electronic properties resulting from a high degree of nitrogen incorporation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.863

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)0.0010.000

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 head0.007
GPT teacher head0.200
Teacher spread0.193 · 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