Tailoring Hierarchically Porous Nitrogen‐, Sulfur‐Codoped Carbon for High‐Performance Supercapacitors and Oxygen Reduction
Bibliographic record
Abstract
Abstract Heteroatom‐doped carbon materials are intensively studied in supercapacitors and fuel cells, because of their great potential for sustainably bearing on the energy crisis and environmental pollution. Although enormous efforts are put in material perfection with a hierarchically porous microstructure, the simultaneous optimization of both porous structures and surface functionalities is hard to achieve due to inevitable concurrent dopant leaching effect and structural collapse under required high pyrolysis temperature. In this study, an in situ dehalogenation polymerization and activation protocol is introduced to synthesize nitrogen‐ and sulfur‐codoped carbon materials (NS‐PCMs) with hierarchical pore distribution and abundant surface doping, which endows them with good conductivity, abundant accessible active sites, and efficient mass transport. As a result, the as‐prepared carbon materials (NS‐a‐PCM‐1000) show an excellent mass specific capacitance of 461.5 F g−1 at a current density of 0.1 A g−1, long cycle life (>23 k, 10 A g−1), and high device energy and power density (17.3 Wh kg−1, 250 W kg−1). Significantly, NS‐a‐PCM‐1000 also exhibits one of the highest oxygen reduction reaction activities (onset potential of 1.0 V vs reversible hydrogen electrode) in alkaline media among all reported metal‐free catalysts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".