Rational Aqueous Electrolytes Design for High-Performance Aluminum Ion Batteries
Bibliographic record
Abstract
Achieving high intrinsic safety, long cycle life, and high energy density is essential for aqueous aluminum-ion batteries (AAIBs) in large-scale energy storage, yet meeting these requirements remains challenging, particularly due to severe hydrogen evolution reaction (HER). Here, we introduce a descriptor-guided screening strategy for designing cosolvent aqueous electrolytes that simultaneously optimize Al3+ solvation and suppress water reduction. Using this framework, N-formylacetamide (NFA) is identified as an optimal cosolvent owing to its high polarity that endows strong coordinating ability and its abundant polar sites that enable effective restructuring of the water hydrogen-bond network. Spectroscopic analyses (Raman, NMR, and FTIR), together with molecular dynamics and first-principles calculations, reveal that NFA preferentially enters the primary solvation shell of Al3+, weakening water coordination, while simultaneously restructuring the hydrogen-bond network to reinforce O-H bonds. The cooperative regulation of the local solvation structure and the aqueous environment substantially increases the dehydrogenation energy barrier, thereby mitigating HER and expanding the electrochemical stability window. As a result, NFA-based electrolytes exhibit enhanced anodic stability, lower HER onset potentials (from -0.7 V to -1.48 V), reduced interfacial polarization, and improved Al stripping/plating behavior relative to conventional aqueous and organic systems. Full-cell tests with MnO2 cathodes demonstrate superior cycling stability (retaining ~63% of capacity after 400 cycles) and higher voltage plateaus (by ~0.25 V), while safety assessments confirm the nonflammability and robust interfacial stability of the NFA electrolyte. This work establishes a generalizable molecular-design framework for tailoring solvent environments in next-generation aqueous metal-ion batteries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".