Facile Synthesis of Defect-Rich and S/N Co-Doped Graphene-Like Carbon Nanosheets as an Efficient Electrocatalyst for Primary and All-Solid-State Zn–Air Batteries
Bibliographic record
Abstract
Developing facile and low-cost porous graphene-based catalysts for highly efficient oxygen reduction reaction (ORR) remains an important matter for fuel cells. Here, a defect-enriched and dual heteroatom (S and N) doped hierarchically porous graphene-like carbon nanomaterial (D-S/N-GLC) was prepared by a simple and scalable strategy, and exhibits an outperformed ORR activity and stability as compared to commercial Pt/C catalyst in an alkaline condition (its half-wave potential is nearly 24 mV more positive than Pt/C). The excellent ORR performance of the catalyst can be attributed to the synergistic effect, which integrates the novel graphene-like architectures, 3D hierarchically porous structure, superhigh surface area, high content of active dopants, and abundant defective sites in D-S/N-GLC. As a result, the developed catalysts are used as the air electrode for primary and all-solid-state Zn–air batteries. The primary batteries demonstrate a higher peak power density of 252 mW cm –2 and high voltage of 1.32 and 1.24 V at discharge current densities of 5 and 20 mA cm –2, respectively. Remarkably, the all-solid-state battery also exhibits a high peak power density of 81 mW cm –2 with good discharge performance. Moreover, such catalyst possesses a comparable ORR activity and higher stability than Pt/C in acidic condition. The present work not only provides a facile but cost-efficient strategy toward preparation of graphene-based materials, but also inspires an idea for promoting the electrocatalytic activity of carbon-based materials.
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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".