Evaluating Solid Electrolyte Interphase Engineering for Rechargeable Aqueous Aluminum Metal Batteries
Bibliographic record
Abstract
Amongst various electrochemical energy storage technologies for renewable energy power grids, the aluminum metal battery (AMB) is arguably the most attractive given the high abundance and high theoretical volumetric/gravimetric capacity of metallic aluminum. The current generation of AMBs, however, utilizes a chloroaluminate ionic liquid (IL) that is intrinsically capacity limiting, highly corrosive, and hygroscopic. These limitations severely hinder the practicality of AMBs. To overcome these drawbacks, aqueous electrolytes are considered as attractive alternatives to ILs. However, their employment is troubled by the spontaneous formation of an ionically-passivating oxide film and hydrogen evolution on aluminum. Impressively, two solid electrolyte interphase (SEI) engineering methods have recently been proposed that seemingly resolves such problems to enable rechargeable aqueous AMBs. The first involves a 5 m (mol kg-1) Al(OTF)3 (aluminum trifluoromethanesulfonate) water-in-salt electrolyte that appears to delay hydrogen evolution by a SEI formed from the reduction of OTF- anions. The second involves an IL pretreatment process for the aluminum electrode, which is argued to be capable of simultaneously removing the native oxide layer and preventing its subsequent formation in aqueous solutions through an organic artificial SEI. Despite the promising results derived from the above methods, there is a lack of understanding for their underlying mechanisms and whether truly reversible aqueous AMBs were achieved. In this study, we reveal the (electro)chemical processes involved in each SEI-building method through a combination of computational, electrochemical and spectroscopic characterizations. We show that both methods unfortunately lack the ability to form stable and effective SEIs and enhancements to electrochemical performances observed in previous studies were largely misinterpretations of the data. Overall, hydrogen evolution remains as the sole cathodic reaction and no aluminum deposition can be achieved. This is the fundamental reason explaining the lower-than-expected voltages and cyclabilities of currently reported aqueous AMBs. To promote future research into enabling truly reversible aqueous AMBs, we offer suggestions for the design of more reliable electrolytes and interphases utilizing the insights gained in our investigation.
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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 | 0.000 |
| 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 | 0.001 | 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".