(Invited) Pulsed Electrodeposition of Metallic Nanoparticles for Energy Conversion and Storage Applications
Bibliographic record
Abstract
Metallic nanoparticles, including platinum, nickel and copper, were deposited on carbon substrates and titania nanotubes (TNTs) using a pulsed current electrodeposition technique with very low duty cycles, high peak current densities, and pulses delivered in both milli- and nano-second range employing triangular and ramp-down waveforms. The presence of nanoparticles, ranging from 2-50 nm in diameter, were confirmed by scanning electron microscopy and X-ray powder diffractometry. Further electrochemical characterization was performed utilizing cyclic voltammetry and frequency response analysis. A simple yet robust mathematical model was developed to predict the influence of different electroplating parameters, including peak deposition current density, pulse waveform, pulse on-time and pulse off-time, on the size and catalytic activity of the resulting nano-catalyst layers. The model is based on progressive nucleation and considers contributions from both nucleation and growth currents. The model was further refined by considering all contributing factors towards growth current, including diffusion, ohmic and charge transfer phenomena as well as changing diffusion coefficients, during electrodeposition. According to the model, at high peak deposition current densities and low duty cycles, the ramp-down waveform yielded the highest nucleation rates, confirming the experimental findings in which nanoparticles generated with the above waveform produced the smallest average grain size, ranging from 2 to 5 nanometer in diameter for platinum nanocatalysts on carbon cloths and TNTs and 10 to 50 nm in diameter for nickel and copper nanoparticles deposited on TNTs.
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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) | 0.001 | 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".