A Mathematical Model and Experimental Examination of the Electrodeposition of Nanocatalysts on Various Substrates for Energy Conversion and Storage Applications
Bibliographic record
Abstract
Pulsed electrodeposition of nanocatalysts from aqueous electrolyte solutions has received considerable attention on account of its ease of use, relative low cost, reproducibility, and access to several process parameters, compared with other widely used techniques. It is, therefore, desirable to determine the influence of different operating parameters to optimize the electrodeposition process. A detailed mathematical model, which includes the effects of ionic migration, was developed to accurately predict the influence of various electrodeposition parameters on the resulting electrodeposited layers in terms of size, distribution and catalytic activity. Most of the available models for pulsed electrodeposition are restricted to conventional square pulses and ignore contributions from the growth current during nucleation. The model presented here is based on progressive nucleation and takes into account contributions from both nucleation and growth currents. Furthermore, the model considers all contributing factors towards growth current, including diffusion, ohmic and charge transfer phenomena. Lastly, unlike many models that are restricted to a constant diffusion coefficient for a given species, the presented model is based on a system with varying diffusion coefficients for all active species in the electrolyte. Nanocatalysts, including platinum, nickel, copper, and cobalt, were electrodeposited on different substrates employing a pulsed current electrodeposition (PCE) technique. Pulses with low duty cycle, high peak deposition current densities, and delivered in milli- and nano-second range utilizing square, triangular, and ramp-down waveforms were used. The presence of nanocatalysts, ranging from 2-50 nm in diameter, was confirmed by SEM and XRD. Additional electrochemical characterization techniques were also employed. According to the model, at high peak deposition current densities and low duty cycles, ramp-down waveform yields the highest nucleation rate, confirming the experimental findings in which nanoparticles generated with the above waveform produces the smallest average grain size, ranging from 2 to 10 nanometer in diameter for platinum nanocatalysts on carbon cloths and 5 to 50 nm in diameter for nickel and copper nanoparticles deposited on titania nanotubes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".