Liquid Cell Transmission Electron Microscopy Sheds Light on The Mechanism of Palladium Electrodeposition
Bibliographic record
Abstract
Electrodeposition is widely used to fabricate tunable nanostructured materials in applications ranging from biosensing to energy conversion. A model based on 3D island growth is widely accepted in the explanation of the initial stages of nucleation and growth in electrodeposition. However, there are regions in the electrodeposition parameter space where this model becomes inapplicable. We use liquid cell transmission electron microscopy along with post situ scanning electron microscopy to investigate electrodeposition in this parameter space, focusing on the effect of the supporting electrolyte, and to shed light on the nucleation and growth of palladium. Using a collection of electron microscopy images and current time transients recorded during electrodeposition, we discover that electrochemical aggregative growth, rather than 3D island growth, best describes the electrodeposition process. We then use this model to explain the change in the morphology of palladium electrodeposits from spherical to open clusters with nonspherical morphology when HCl is added to the electrolyte solution. The enhanced understanding of the early stages of palladium nucleation and growth and the role of electrolyte in this process provides a systematic route toward the electrochemical fabrication of nanostructured materials.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".