Ultrasmall Co<sub>9</sub>S<sub>8 </sub>nanocrystals on Carbon Nanoplates for Efficient Bifunctional Oxygen Electrocatalysis
Bibliographic record
Abstract
Electrochemical energy storage and conversion technologies based on electrocatalysis have been attracting more and more attention addressing increasing concerns on fossil fuel crisis and environmental deterioration. Fuel cells, zinc-air batteries, and water electrolyzer are believed to be promising candidates due to the environmental friendliness and high efficiency. These systems are associated with key reactions including oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Due to slow kinetics of these reactions, efficient electrocatalysts, e.g., Pt for ORR and RuOx/IrOx for OER, are usually required to overcome the energy barrier in electrochemical reactions to increase the reaction rate. However, the most advanced electrocatalysts are still based on above-mentioned noble metals with high cost and scarcity, which inevitably retards the large-scale commercialization of these noble metal-based energy systems. It is of great significance to replace noble metal catalysts with earth-abundant, cost-effective, and highly efficient catalysts. Here, we reported the controlled synthesis of ultrafine Co9S8 nanocrystals embedded in N, S-codoped multilayer-assembled carbon nanoplates (Co9S8/NSCP) for highly efficient oxygen electrocatalysis. The bifunctional Co9S8/NSCP electrocatalyst displays a high half-wave potential for ORR, and a low overpotential for OER in 0.1M KOH at a current density of 10 mA cm -2, much better than those of single component counterparts (Co9S8 or carbon) and comparable to noble metal catalysts. The high performance of Co9S8/NSCP can be attributed to the rationally designed hierarchical architecture with nanosized Co9S8 nanocrystals, rich N, S-codopants, highly exposed surface area, and protective graphitic layers, providing abundant active sites with full utilization and stable carbon support towards fast catalytic kinetics and durability. This work will promote further research on the development of highly efficient and stable non-noble metal electrocatalysts for ORR and OER.
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".