Experimental and Computational Investigations of Pulsed Electrodeposition of Well-Adhered Nanocatalysts on Conductive Materials for Energy Applications
Bibliographic record
Abstract
Electrical energy storage has emerged as a critical component in North America’s clean energy transformation. Vehicle electrification and integration of renewables such as wind and solar power into the grid have further underlined the importance of affordable, durable and environmentally friendly energy storage platforms. Rechargeable batteries, including Li-ion and metal-air batteries, have been receiving significant attention as well as criticism for their relatively high cost and sluggish oxygen reduction reaction, respectively. Accordingly, investigation into the development of catalysts with high activity, long life, and low cost has become an area of active research. Nanocatalysts such as cobalt, nickel and platinum-group metals were electrodeposited on various conductive substrates, including carbon paper, carbon cloth and highly-ordered titania nanotubes (TNTs). Catalyst electrodeposition was carried out employing pulse current electrodeposition with various waveforms, low duty cycles, and high peak deposition current densities. All catalysts were systematically characterized and tested using microscopy, spectroscopy and electrochemical techniques. The presence of nanocatalysts were confirmed by SEM and XRD and were found to be 2-5 nm for platinum and platinum-group metals and 10-50 nm for nickel and cobalt on carbon substrates and TNTs, respectively. A comprehensive mathematical model based on progressive nucleation was developed to elucidate the roles of various process parameters, including pulse waveform, peak deposition current density and duty cycle on the size and catalytic activity of the resulting nanocatalysts in different environments. The model considers some key contributing factors towards growth current and, ultimately, nucleation, including diffusion, ohmic and charge transfer phenomena as well as varying diffusion coefficients during electrodeposition. Model predictions were confirmed by experimental measurements especially on platinum-group metals deposited on carbon substrates for use in PEM fuel cells and bi-functional nanocatalysts for use in aluminum-air batteries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".