Hierarchical Porous Carbon Derived from Coal Tar Pitch Containing Discrete Co–Nx–C Active Sites for Efficient Oxygen Electrocatalysis and Rechargeable Zn–Air Batteries
Bibliographic record
Abstract
Coal tar pitch (CTP), as a byproduct of the rich, low-cost, high-carbon yield coal industry, is expected to achieve high value-added and comprehensive utilization. The oxygen reduction reaction is the cornerstone of both fuel cells and zinc–air batteries (ZABs). Herein, as a proof-of-concept application, the activated CTP (ACTP) first used as carbon source through cobalt/nitrogen reengineering is demonstrated to be an effective and straightforward strategy for fabrication of high-performing hierarchical porous carbons (Co/N-HPCs). The as-prepared Co/N-HPC 150/800 with a large specific surface area of 2076.58 m 2 g –1 exhibits superior electrocatalytic properties for both oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) in terms of a half-wave potential of 0.85 V (vs RHE) for ORR and an onset potential of 1.42 V (vs RHE) for OER, respectively, in 0.1 M KOH, superior to commercial Pt/C catalyst. Notably, we have experimentally shown that Co/N-HPC 150/800 as the air electrode for zinc–air battery demonstrates a high discharge peak power density achieving 425 mW cm –2, a current density of 291 mA cm –2 at a voltage of 1 V, and a long cycling stability beyond 250 h at 10 mA cm –2 . Moreover, the obtained rechargeable Zn–air battery exhibits a low discharge–charge voltage gap and long cycle life (2100 cycles with 10 min per cycle). The outstanding electrocatalytic properties are attributed to the unique hierarchical pore structures as well as coupling effects originated from cobalt doping and trace nitrogen-containing active sites from aqueous ammonia, which largely facilitates the charge transfer and enhances the stability of rechargeable ZABs. It opens a new and viable way for realizing the high value-added utilization of CTP.
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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".