Iron and Nickel Phthalocyanine‐Modified Nanocarbon Materials as Cathode Catalysts for Anion‐Exchange Membrane Fuel Cells and Zinc‐Air Batteries**
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.
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 ( E 1/2 ) for ORR and the potential at which the OER current density reaches 10 mA cm −2 ( E j =10 ), but the best performing catalysts are FeNiN−MC ( E 1/2 =0.88 V, E j =10 =1.58 V) and FeNiN−MWCNT ( E 1/2 =0.87 V, E j =10 =1.59 V). In AEMFC analyses, FeNiN−MWCNT cathode provides peak power density ( P max ) of 406 mW cm −2 , slightly higher than that of FeNiN−MC ( P max =386 mW cm −2 ). Both catalysts exhibit a good RZAB performance ( P max of 85 mW cm −2 for FeNiN−MWCNT). The assembled RZABs run stably for 48 h without any significant loss of performance.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 it