Quantifying and Suppressing Proton Intercalation to Enable High‐Voltage Zn‐Ion Batteries
Bibliographic record
Abstract
Abstract Rechargeable Zn‐ion batteries (ZIBs) are widely regarded as promising candidates for large‐scale energy storage applications. Like most multivalent battery systems (based on Zn, Mg, Ca, etc.), further progress in ZIB development relies on the discovery and design of novel cathode hosts capable of reversible Zn2+ (de)intercalation. Herein, this work employs VPO4F as a ZIB cathode and explores ensuing intercalation mechanisms along with interfacial dynamics during cycling to quantify the water dynamics in concentrated electrolytes and/or hybrid aqueous‐non aqueous (HANEs) electrolyte(s). Like most oxide‐based cathode materials, proton (H+) intercalation dominates electrochemical activity during discharge of ZnxHyVPO4F in aqueous media due to the hydroxylated nature of the interface. Such H+ electrochemistry diminishes low‐rate and/or long‐term electrochemical performance of ZIBs which inhibits implementation for practical applications. Thus, quantification of the water dynamics in various electrolytes is demonstrated for the first time. Detailed investigations of water mobility in various concentrated electrolytes and HANEs systems enable the design of an electrolyte that enhances aqueous anodic stability and suppresses water/proton activity during discharge. Tuning Zn2+/H+ intercalation kinetics simultaneously allows for a high voltage (1.9 V) and long‐lasting aqueous zinc‐ion battery: Zn|Zn(OTf)2·nH2O‐PC|ZnxHyVPO4F.
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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) | 0.000 | 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 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".