Performance optimization of high energy density aluminum-air batteries: Effects of operational parameters and electrolyte composition
Bibliographic record
Abstract
Aluminum-air (Al-air) batteries are promising candidates for high energy-density applications due to their lightweight design, cost-effectiveness, and high theoretical energy output. This study investigates the performance optimization of two rotating disk prototypes, with prototype-1 systematically exploring the combined effects of critical parameters, including anode-cathode distance (ACD), electrolyte flowrate, and temperature- an area previously underexplored. Prototype-1 achieved high peak power densities of up to 155.87 mW/cm 2 and energy densities of 987.17 mWh/g. Insights gained are used to design prototype-2, which features a larger active electrode surface and an electrode cartridge system for improved usability and maintenance. Prototype-2 focused on the impact of electrolyte composition, comparing KOH and NaOH at varying concentrations. KOH achieved a peak power density of 142.4 mW/cm 2 and energy densities of 2778.40 mWh/g, outperforming NaOH, which displayed a peak of 120 mW/cm 2 energy densities of 2385.02 mWh/g. While KOH demonstrated higher energy density and superior discharge stability, NaOH exhibited greater stability at elevated concentrations and slightly better current and energy efficiency at lower concentrations. This study provides a comprehensive understanding of the synergistic effects of operational parameters and electrolyte composition on Al-air battery performance. The findings offer valuable insights for optimizing design and operational strategies, paving the way for the development of high-performance Al-air battery systems.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".