Bimetallic metal-organic framework derived doped carbon nanostructures as high-performance electrocatalyst towards oxygen reactions
Bibliographic record
Abstract
Rational design and development of cost-effective, highly active and durable bifunctional electrocatalysts towards oxygen redox reactions is of critical importance but great challenge for the broad implementation of next-generation metal-air batteries for electric transportation. Herein, a high-performance electrocatalyst of cobalt and zinc sulfides nanocrystals embedded within nitrogen and sulfur co-doped porous carbon is successfully designed and derived from bimetallic metal-organic frameworks of cobalt and zinc containing zeolitic imidazolate frameworks. The unique nanostructure contains abundant electrocatalytic active sites of sulfides nanocrystals and nitrogen and sulfur dopants which can be fast accessed through highly porous structure originate from both zinc vaporization and sulfurization processes. Such bifunctional electrocatalyst delivers a superior half-wave potential of 0.86 V towards oxygen reduction reaction and overpotential of 350 mV towards oxygen evolution reaction, as well as excellent durability owing to the highly stable carbon framework with a great graphitized portion. The performance boosting is mainly attributed to the unique nanostructure where bimetallic cobalt and zinc provide synergistic effect during both synthesis and catalysis processes. The design and realization pave a new way of development and understanding of bifunctional electrocatalyst towards clean electrochemical energy technologies.
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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".