Development of Cathodes for Aluminum-Ion and Aluminum-Air Batteries Using Pulsed Electrodeposition
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, environmentally friendly energy storage platforms. Rechargeable batteries are now considered to be viable options for large-scale energy storage with lithium-ion and redox flow batteries leading the charge. Over the past decades, aluminum-ion batteries have experienced a renaissance on account of their high volumetric energy density, superior cycleability, enhanced stability and significant cost benefits compared with other commercially available secondary batteries. Aluminum-air batteries also hold significant promise as primary energy storage platforms, but they suffer from thermodynamics and kinetics hurdles, leading to inferior performance. To address these issues, researchers have focused on optimizing different cell components, including anodes, cathodes, and electrolytes. It is well established that nano-structuring battery components would markedly improve their short- and long-term performance by enhancing reaction kinetics. One of the major impediments to wide-scale commercialization of Al-ion batteries continues to be its inferior capacity and cycle stability compared with commercially available secondary batteries. Part of these issues can be ascribed to the failure of cathode materials currently being investigated and used. As for primary Al-air batteries, conventional air cathodes do not perform very well either; and there are many reasons as to why the incorporation of such air cathodes into Al-air batteries would lead to inferior performance or premature failure; the most important of which is low catalytic activity. To address a number of these issues, pulsed current electrodeposition was employed to deposit nano-catalysts such nickel, silver, platinum and oxides of manganese on different support materials, including carbon paper and cloth, titania nanotubes, and stainless steel and nickel meshes. The influence of various electrodeposition parameters, including peak current density, duty cycle, type of waveform and pulse frequency, on resulting layers were systematically investigated. In most cases, nanocatalysts about 2-40 nm in diameter were obtained. Various electrochemical tests were utilized to characterize the resulting layers. A simple mathematical model based on progressive nucleation also was developed to predict the influence of the aforementioned electrodeposition parameters on the resulting nano-catalyst layers. 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 (5% or less), 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 2-10 nanometer in diameter for platinum and silver nanocatalysts on carbon cloths and titania nanotubes, and 10-50 nm in diameter for nickel nanoparticles deposited on stainless steel and nickel meshes and 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".