In Search of Excellence: Convex versus Concave Noble Metal Nanostructures for Electrocatalytic Applications
Bibliographic record
Abstract
Controlling the shape of noble metal nanoparticles is a challenging but important task in electrocatalysis. Apart from hollow and nanocage structures, concave noble metal nanoparticles are considered a new class of unconventional electrocatalysts that exhibit superior electrocatalytic properties as compared with those of conventional nanoparticles (including convex and flat ones). Herein, several facile and highly reproducible routes for synthesizing nanostructured concave noble metal materials reported in the literature are discussed, together with their advantages over noble metal nanoparticles with convex shapes. In addition, possible ways of optimizing the synthesis procedure and enhancing the electrocatalytic characteristics of concave metal nanoparticles are suggested. Nanostructured noble metals with concave features are found to show better catalytic activity and stability hence improve their practical applicability in electrocatalysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".