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
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 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