Mitigating cobalt nanoparticles in pyrolyzed Co-ZIF-derived oxygen reduction reaction electrocatalysts in alkaline media
Bibliographic record
Abstract
To advance sustainable energy technologies like fuel cells, metal-air batteries, and electrosynthesis of H 2 O 2 - driven by the oxygen reduction reaction (ORR) - pyrolyzed transition metal-carbon-nitrogen (TM-N x /C) electrocatalysts derived from zeolitic imidazolate frameworks (ZIFs) are promising alternatives to platinum-group metals. Pyrolysis, an essential step in preparing ZIF-derived TM-N x /C electrocatalysts, can induce the formation of metal-based nanoparticles, thereby reducing active-site density and catalytic efficiency. Understanding the formation of nanoparticles and mitigating them is therefore critical. Herein, we employ four strategies used during synthesis to minimize the presence of Co nanoparticles in Co-ZIF-derived ORR electrocatalysts: spatial isolation, dimensionality control, thermal exfoliation, and acid-washing. Electrochemical performance of the prepared electrocatalysts was evaluated using a rotating ring-disk electrode in 0.1 M KOH, and the materials were characterized by a variety of techniques to understand their physical and chemical properties. Besides influencing nanoparticle formation and presence, mitigation strategies also impacted catalyst surface areas, concentration of N-doped carbon defects, exposure of active sites, electrochemical surface areas, and electro-catalytic selectivity towards HO 2 - . Unlike spatial isolation (using dual Co 2+ /Zn 2+ nodes) and dimensionality control (2D vs. 3D ZIFs), acid washing (with nitric acid) and exfoliation (via KCl intercalation pre-pyrolysis) effectively produced Co nanoparticle-free electrocatalysts. Optimal ORR performance metrics were linked with combining multiple mitigation strategies, such as spatial isolation and exfoliation. Correlative physical and electrochemical characterizations illustrated the complex interplay between structure, property, and performance with different nanoparticle mitigation strategies. This work offers insights into deriving sustainable nanoparticle-free ZIF-derived electrocatalysts via pyrolysis, addressing a critical need in ORR-based technologies. • Real-time insights into the dynamics of Co nanoparticle formation in Co-ZIF-67 via in situ transmission electron microscopy. • Design and characterization of 2D and 3D Co-ZIF, Zn-ZIF, and Co/Zn-ZIF precursors pre- and post-pyrolysis. • The effectiveness of acid washing and thermal exfoliation in removing Co nanoparticles as sole strategies is demonstrated. • Combining strategies such as Co node spatial isolation with acid washing or thermal exfoliation showed synergistic effects. • Insights for optimizing Co/Zn ZIF-derived ORR electrocatalysts through structure-property-performance correlations.
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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".