On the Performance and Structural Stability of Cathodic Electrocatalysts with Complex Nanoscale Morphology
Bibliographic record
Abstract
Increasing complexity of nanosynthesis has produced a variety of intricate nanoparticle shapes which have attracted significant attention for applications in electrocatalysis. These complex nanoelectrocatalyst shapes not only introduce high surface areas and high energy active sites, but also improve electrocatalytic performance by altering electric field at the surface thereby concentrating reagents and accelerating electrocatalysis. However, long-term structural stability of complex nanoelectrocatalysts necessary for their industrial implementation often remains unknown and difficult to predict. Here, we study structural stability in cathodic reactions (CO 2 and water electrolysis) of noble metal nanoparticles with star- and core-cage structures that collectively cover major structural nanoscale features including extrusions, holes, voids, and nanogaps. Based on structural and surface analysis, density functional theory and finite element method calculations, we demonstrate that uneven electric field and current density distributions in these morphologies influence metal mobility resulting in their structural degradation. Our results revealed that extrusions and holes facing the anode form electric field hot spots for electrolysis, that also facilitate surface reconstruction. The holes and voids perpendicular to the direction facing the anode form current crowding spots within the metal that facilitate electromigration via local Joule heating, while being blind spots for electrolysis due to low local electric field in these areas. Finally, structural features within nanocages are electrically shielded resulting in them being blind spots for both electrolysis and electromigration. This study provides guidelines for the structural design of electrocatalysts, highlighting that it should not only be dictated by the surface atomic organization and the generation of electric field hotspots, but also by current distribution within the electrode itself, to ensure not only high material performance but also improved stability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".