Improvement of electrolytes for aluminum ion batteries: A molecular dynamics study
Bibliographic record
Abstract
The aluminum ion battery (AIB) is a promising technology, but there is a lack of understanding of the desired nature of the batteries' electrolytes. The ionic charge carriers in these batteries are not simply Al3+ ions but the anionic AlCl4- and Al2Cl7-, which form in the electrolyte. Using computational analysis, this study illustrates the effect of mole ratios and organic solvents to improve the AIB electrolytes. To this end, molecular dynamics simulations were conducted on varying ratios forming acidic, neutral, and basic mixtures of the AlCl3 salt with 1-ethyl-3-methylimidazolium chloride (EMImCl) ionic liquid (IL) and an organic solvent electrolyte [dichloromethane (DCM) or toluene]. The data obtained from diffusion calculations indicates that the solvents could improve the transport properties. Both DCM and toluene lead to higher diffusion coefficients, and higher conductivity. Detailed calculations demonstrated solvents can effectively improve the formation of AlCl3⋯Cl (AlCl4-) and AlCl4-···AlCl4- (Al2Cl7-) especially in acidic mixtures. The densities, around 1.25 g/cm3 for electrolyte mixtures of AlCl3-EMImCl, were consistent with experiment. These results, in agreement with experimental findings, strongly suggest that DCM in acidic media with AlCl3 and EMImCl might provide a promising basis for battery development.
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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".