Nano-Engineered Materials for Energy Conversion and Storage Applications: Synthesis, Characterization, and Mathematical Modeling
Bibliographic record
Abstract
Pulsed current electrodeposition (PCE) was employed to deposit a number of metals on different substrates for various applications. Influence of key PCE parameters, including duty cycle, peak deposition current density, pulse on-time-on and off-time and type of pulse waveform, on the morphology, size, distribution and catalytic activity of the electrodeposited layers were investigated. Platinum and palladium were electrodeposited on carbon substrates for fuel cell applications and nickel, copper, cobalt and platinum were electrodeposited on highly ordered titania nanotubes (TNTs) for solar hydrogen generation and wastewater treatment processes. The presence of nanocrystalline platinum and palladium, ranging from 2-10 nm in diameter, on carbon paper and cloth were confirmed by scanning electron microscopy and x-ray diffractometry. Their catalytic activity also was examined by fabricating membrane-electrode assemblies and testing them in single-cell proton exchange membrane and direct methanol fuel cells. Doped TNTs also were characterized by microscopy, x-ray diffractometry and UV-Vis spectroscopy. Structural investigation of the prepared TNTs via a simple anodization technique followed by metal doping, utilizing the aforementioned PCE method, confirmed the presence of anatase nanocrystalline TNTs after annealing in air and vacuum at elevated temperatures. The presence of nickel and copper nanoparticles 10-50 nm in diameter also was observed and, subsequently, confirmed. The efficiency of the photoanodes was found to increase as the amount of dopant increased to 3%. Further increase in metal dopants, however, resulted in a decrease in efficiency. A comprehensive mathematical model based on progressive nucleation was developed to accurately predict the influence of the aforementioned electroplating parameters on the resulting layers and films with the aim of process optimization for a particular plating system. The model takes into account contributions from both nucleation and growth currents and assumes a dynamic; i.e., changing, diffusion coefficient during electrodeposition. According to this model, high peak deposition current densities, low duty cycles, pulses delivered in microsecond range and employing a ramp-down waveform yielded the highest nucleation rates, leading to the smallest average grain size of the deposited nanoparticles, and confirming the experimental findings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".