Synthesis and Electrocatalytic Oxygen Reduction Performance of the Sulfur-Doped Carbon Nanocages
Bibliographic record
Abstract
The sluggish oxygen reduction reaction (ORR) is the bottleneck in the development of fuel cells, and replacing precious and nondurable Pt catalysts by the material with low cost, high activity and good stability is a main challenge. Car- bon-based metal-free ORR electrocatalysts have become a promising alternative of commercial Pt/C catalyst due to their superior catalytic activity, high stability and low cost. Recent studies revealed that the doping of N, B, P or S atoms could boost the ORR electrocatalytic performance of carbon nanomaterials, and the catalytic activities were highly dependent on the doping elements, amounts and microstructures. In this study, sulfur-doped carbon nanocages (SCNCs) were synthesized by chemical vapor deposition method using in situ generated MgO as template and thiophene/benzene as precursors. The resultant SCNCs possessed high specific surface area of ca. 1000 m 2 •g -1 , abundant pore structure and superior graphitization degree. The X-ray photoelectron spectroscopy result showed sulfur atoms were doped into the carbon framework as the C― S―C moieties. The content of sulfur in the SCNCs was adjusted in the range of 0~3.45 at% by changing the amount of thiophene in the precursor. All the SCNCs samples had comparable specific surface area and similar pore structure. As an electrocatalyst for oxygen reduction reaction (ORR) in alkaline medium, the SCNCs exhibited a sulfur-content-dependent performance. The SCNCs with sulfur content of 0.84 at% demonstrated the optimal ORR performance. With further increas- ing the sulfur content, the ORR performance of the SCNCs gradually degraded and even inferior to that of the pure CNCs when the sulfur content was higher than 1.61 at%. In addition, the SCNCs showed better stability and immunity to methanol crossover than the Pt/C catalyst. This result is suggestive for designing advanced metal-free ORR electrocatalysts by regulat- ing the species and content of dopants and doping microstructures. Keywords carbon nanocages; sulfur doping; oxygen reduction; metal-free; fuel cells
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".