High Performance Aluminum Ion Batteries Enabled by the Coordination Between Vanadium‐Based PBAs Cathode and Aqueous Eutectic Electrolyte
Bibliographic record
Abstract
Abstract The practical application of aqueous aluminumion batteries (AAlBs) faced with critical challenges, such as low rate performance and poor cycling stability due to the absence of ideal cathode materials. To address the bottlenecks of low electrochemical activity, structural instability, and narrow voltage window in Prussian blue analogues (PBAs) for AAIBs, this study develops a universal synthesis strategy integrating acid‐assisted method with ligand modulation to prepare high‐performance vanadium‐based PBAs (V‐PBAs) cathodes. Through precise coordination environment control, V and Fe/Co/Ni synergistically enhance multi‐electron redox activity. Density functional theory calculations reveal that the Fe‐doped VFePBA exhibits a narrow bandgap (0.479 eV) and low Al 3+ migration energy barrier (0.586 eV), enabling rapid ion transport. Combined with an Al 2 (SO 4 ) 3 ‐urea eutectic electrolyte (AU15) that expands the operational voltage window to 0.1–2.0 V, the optimized Zn||AU15||VFePBA system achieves a high specific capacity of 161.37 mAh g −1 at 0.1 A g −1 . In‐situ characterizations confirm a suppressed structural distortion via Al‐O coordination and a capacitive‐dominated charge storage mechanism. Flexible pouch cells demonstrate stable operation under mechanical bending and practical device powering capabilities. This work provides a novel paradigm for the systematic assembly of advanced safe, and low‐cost post‐lithium energy storage systems.
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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 | 0.000 | 0.000 |
| 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".