Iron and Nickel Phthalocyanine‐Modified Nanocarbon Materials as Cathode Catalysts for Anion‐Exchange Membrane Fuel Cells and Zinc‐Air Batteries**
Bibliographic record
Abstract
Abstract Iron and nickel phthalocyanines along with different carbon supports, i. e., multi‐walled carbon nanotubes (MWCNT), graphene, carbide‐derived carbon, Vulcan carbon, and mesoporous carbon (MC, from Pajarito Powder, LLC), are used to prepare six bimetallic (Fe, Ni) N‐doped carbon‐based catalysts. The aim of this work is to investigate the electrocatalytic activity of bimetal phthalocyanine‐modified nanocarbon catalysts, e. g., the effect of carbon supports on the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), including the anion‐exchange membrane fuel cell (AEMFC) and rechargeable zinc‐air battery (RZAB) configuration. The catalysts exhibit excellent electrocatalytic activity as exemplified by their half‐wave potential (E1/2) for ORR and the potential at which the OER current density reaches 10 mA cm−2 (Ej=10), but the best performing catalysts are FeNiN−MC (E1/2=0.88 V, Ej=10=1.58 V) and FeNiN−MWCNT (E1/2=0.87 V, Ej=10=1.59 V). In AEMFC analyses, FeNiN−MWCNT cathode provides peak power density (Pmax) of 406 mW cm−2, slightly higher than that of FeNiN−MC (Pmax=386 mW cm−2). Both catalysts exhibit a good RZAB performance (Pmax of 85 mW cm−2 for FeNiN−MWCNT). The assembled RZABs run stably for 48 h without any significant loss of performance.
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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) | 0.001 | 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".