Electrochemical Stability Windows of Imidazolium-Based Ionic Liquids for Aluminum Batteries: Computational Insights into Cation Functionalization and Fluoride-Containing Anions
Bibliographic record
Abstract
Abstract Understanding and tuning the electrochemical stability window (ECW) of ionic liquids (ILs) are essential for advancing energy storage technologies. In this study, density functional theory combined with the thermodynamic cycle method is employed to systematically investigate the ECWs of imidazolium-based cations paired with a range of fluorinated and chlorinated anions with potentials referenced to an aluminum electrode. A broad set of cation structures, including alkyl, methoxy–ethoxy, vinyl, and alkyl-bridged derivatives, is explored alongside common and hydrogen fluoride-containing anions, [F(HF)n]− (n = 0 – 3). The results show that while simple alkyl substitution has minimal redox impact, electron-donating and π-conjugated groups lower oxidation potentials via HOMO delocalization. Fluorinated anions confer high redox stability, whereas HF-containing anions limit both the oxidative and reductive boundaries. Notably, [im+-C3-im]+[BF4]− presents the widest ECW (5.813 V), while HF-containing anions yield narrower ECWs due to the coexistence of [F]− and [F(HF)]− entities. Accurate ECW estimation further requires proper consideration of anion redox pathways as the choice of reaction mechanisms strongly influences predicted stability limits. Comparative analysis with the HOMO–LUMO and adiabatic AIE-AEA methods confirms that the thermodynamic cycle approach delivers superior accuracy while remaining computationally efficient, making it well-suited for high-throughput screening. Furthermore, the solvent dielectric constant is found to significantly modulate redox boundaries, emphasizing the importance of solvation effects in predictive modeling. These insights provide a robust foundation for the design of ILs with tailored electrochemical performance in high-voltage rechargeable batteries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".