A 3-D nanoribbon-like Pt-free oxygen reduction reaction electrocatalyst derived from waste leather for anion exchange membrane fuel cells and zinc-air batteries
Bibliographic record
Abstract
Fe-Nx and Fe-S-based ORR electrocatalysts have emerged as rightful candidates to replace Pt in fuel cells to make the technology cheap and sustainable. Fe-N-C catalysts are generally prepared by the pyrolysis of conducting polymers, metal-organic frameworks, aerogels, etc., and the combination of multiple heteroatoms and metal precursors. These precursors are mostly expensive and their synthesis involves multiple steps. In this report, we have demonstrated the synthesis of a Fe-N-C catalyst from the waste leather obtained from the footwear and other leather-consuming industries. The pyrolysis of leather with FeCl3 (metal source) results in the formation of a highly thin and porous nano-ribbon like morphology. Waste leather acts as a cost-free single source of heteroatoms like N, S and carbon. The catalyst synthesized at a temperature of 900 °C shows an overpotential of 40 mV and better durability compared to the commercial Pt/C catalyst. The catalyst is demonstrated as the cathode for alkaline exchange membrane fuel cell (AEMFC) and zinc-air battery (ZAB) applications. In the AEMFC, a power density of 50 mW cm-2 and an OCV of 0.92 V are obtained whereas, in the ZAB, it exhibited a power density of 174 mW cm-2 compared to 160 mW cm-2 of the system based on the Pt/C catalyst.
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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) | 0.000 | 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".