Pulsed Electrodeposition of Nanocatalysts for Energy Storage Applications: Mathematical Modeling and Experimental Studies
Bibliographic record
Abstract
With the growing demand for clean energy, the need for affordable, efficient energy storage technologies has never been more urgent. Pumped hydro and compressed air storage technologies have been widely used, but they are heavily dependent on location and are not considered to be universal solutions. Secondary batteries, including Li-ion and lead-acid batteries, have played a major role in storing excess electrical energy, but they suffer from high cost, performance degradation over time, and environmental issues during use and disposal. Metal-air batteries have experienced a renaissance on account of their acceptable energy density, superior cycleability, enhanced stability and cost benefits compared with other commercially available batteries. Despite these desirable properties, metal-air batteries suffer from a number of thermodynamics and kinetics hurdles that must be overcome, if their widespread deployment is desired. One of the major impediments to wide-scale commercialization of metal-air batteries continues to be their inferior capacity and cycle stability. Part of these issues can be ascribed to the shortcomings of the cathode materials and structures in general, and the inferiority of their catalyst layers in particular. It is well established that nano-structuring battery components would improve their short- and long-term performance by enhancing reaction kinetics. Nanocatalysts such as transition metals, platinum and manganese dioxide were electrodeposited on a number of conductive substrates, including carbon paper, carbon cloth and titania nanotubes (TNTs) with high aspect ratios. Catalyst electrodeposition was performed utilizing pulsed current electrodeposition with different waveforms, low duty cycles, and high peak deposition current densities. All catalysts were characterized 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 10-50 nm for nickel and oxides of manganese. A mathematical model was developed to determine the roles of various deposition process parameters on the size and catalytic activity of the resulting nanocatalysts in different environments. The model is based on progressive nucleation and takes into account some key contributing factors towards growth current and, ultimately, nucleation, including diffusion, ohmic and charge transfer phenomena. A varying diffusion coefficient during electrodeposition was also considered and evaluated. Model predictions were confirmed by experimental measurements with emphasis on platinum and manganese dioxide nanocatalysts for use in PEM fuel cells and metal-air batteries, respectively.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".