Controlled Growth of Platinum Nanoparticles during Electrodeposition using Halide Ion Containing Additives
Bibliographic record
Abstract
Optimizing platinum (Pt) utilization is a necessary step towards developing affordable electrocatalysts for fuel cells and related technologies. Electrodeposition is a scalable approach to preparing Pt nanoparticles (NPs). Herein, Cl− and Br− ions are used in excess as additives during the electrodeposition of Pt NPs to influence nucleation and growth processes as a means of tuning particle morphology and their electrocatalytic activity. Adding NaCl formed larger particles with urchin-like morphologies while adding NaBr produced smaller, more uniform NPs that were evenly dispersed across the substrate. Mixtures of these two halide ion species improved surface coverage and size distribution of the NPs. Particle size was further decreased, and their surface coverage increased by combining the addition of excess halide ions with using a higher applied potential to initiate “nucleation” followed by a lower applied potential to promote particle “growth.” Mass activity towards the oxygen reduction reaction was the highest for Pt NPs electrodeposited in the presence of Br−. The addition of cetyltrimethylammonium chloride and cetyltrimethylammonium bromide during electrodeposition produced small NPs with an even higher mass activity, which was attributed to the formation of porous nanostructures. This study demonstrates techniques to improve Pt utilization and electrocatalytic activity of electrodeposited Pt NPs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".