Highly Active Nitrogen-Doped Carbon Nanotubes for Oxygen Reduction Reaction in Fuel Cell Applications
Bibliographic record
Abstract
One of the main challenges in the commercialization of low temperature fuel cells is the slow oxygen reduction reaction (ORR) kinetics and the high cost and scarcity of platinum (Pt)-based catalysts. As a result, alternative non-noble electrocatalysts to Pt materials for ORR is needed to realize the practical application of fuel cells. In this study, nitrogen-doped carbon nanotubes (NCNTs) were synthesized as a non-noble electrocatalyst for the ORR using ethylenediamine (EDA-NCNT) and pyridine (Py-NCNT) as different nitrogen precursors by a single-step chemical vapor deposition (CVD) process. The resulting EDA-NCNT has shown similar ORR performance compared to platinum on carbon support in terms of onset and half-wave potentials. Moreover, EDA-NCNT showed superior ORR performance in terms of limiting current density, number of electrons transferred, and H 2 O selectivity. The effects of nitrogen content on ORR performance of NCNT were investigated by comparing EDA-NCNT with Py-NCNT. The ORR performance of Py-NCNT was inferior compared to EDA-NCNT in terms of onset and half-wave potentials, limiting current density, number of electrons transferred, and H 2 O selectivity. Further material characterizations by X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy illustrated a higher nitrogen content and more defects in EDA-NCNT compared to that of Py-NCNT which indicates the important role of the nitrogen precursor on nitrogen content and structure of NCNT. By combining the results of ORR activity and material characterization, it is concluded that higher nitrogen content and more defects of NCNT lead to high ORR performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".