Doped, Defect‐Enriched Carbon Nanotubes as an Efficient Oxygen Reduction Catalyst for Anion Exchange Membrane Fuel Cells
Bibliographic record
Abstract
Abstract Bond polarization of doped atoms and carbon and lattice defects are considered important aspects in the catalytic mechanisms of oxygen reduction reaction (ORR) on heteroatom‐doped carbon catalysts. Previous work on metal‐free catalysts has focused either on bond polarization or lattice defects. Here multi‐heteroatom doped defect‐enriched carbon nanotubes (MH‐DCNTs) that combine both effects to enhance ORR activity are designed. Lattice defects in MH‐DCNTs are enriched by unzipping and length‐shortening of carbon nanotubes, and also by creating carbon vacancies via decomposition of doped F atoms. Electrochemical analysis using rotating disc electrode voltammetry shows that the ORR kinetic current density of MH‐DCNT increases with lattice‐defect density, the latter of which is verified by Raman spectroscopy, while the onset potential increases with annealing temperatures. An optimized MH‐DCNT ORR catalyst exhibits a half‐wave potential of 0.81 V versus reversible hydrogen electrode and limiting current density of 5.0 mA cm −2 at an electrode rotation speed of 1600 rpm in 0.1 m KOH. Further, it is demonstrated that MH‐DCNT, as a cathode catalyst layer in an anion exchange membrane fuel cell (AEMFC), delivers a peak power density of 250 mW cm −2 , which is ≈70% the performance of an AEMFC using a conventional Pt/C catalyst.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".