Bimetallic CoNi Alloy Nanoparticles Embedded in Pomegranate-like Nitrogen-Doped Carbon Spheres for Electrocatalytic Oxygen Reduction and Evolution
Bibliographic record
Abstract
Electrocatalysts for oxygen reactions play a vital role in the applications of energy conversion devices, such as in fuel cells, water splitting, and metal–air batteries. Developing novel nonprecious bifunctional electrocatalysts is particularly essential for improved-performance metal–air batteries by overcoming sluggish oxygen reactions. Herein, we demonstrate a composite electrocatalyst with bimetallic CoNi alloy nanocrystals embedded within nitrogen-doped highly graphitic carbon spheres (N-CoNi/PCS). Owing to the efficient design strategy, such a composite electrocatalyst exhibits a unique pomegranate-like morphology and microstructure. When applied to an alkaline electrolyte system, the porous electrocatalyst delivers a low half-wave potential of 0.80 V toward ORR and overpotential of 540 mV toward OER with excellent durability. The high performance is systematically studied and ascribed to the unique nanoarchitecture which enables fast ion transportation and electron transfer, easy access to abundant active sites, and robustness. This work forwards the field of fabricating a highly active, affordable, and stable bifunctional electrocatalyst for energy conversion.
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".