Charged organic ligands inserting/supporting the nanolayer spacing of vanadium oxides for high-stability/efficiency zinc-ion batteries
Bibliographic record
Abstract
ABSTRACT Given their high safety, environmental friendliness and low cost, aqueous zinc-ion batteries (AZIBs) have the potential for high-performance energy storage. However, issues with the structural stability and electrochemical kinetics during discharge/charge limit the development of AZIBs. In this study, vanadium oxide electrodes with organic molecular intercalation were designed based on intercalating 11 kinds of charged organic carboxylic acid ligands between 2D layers to regulate the interlayer spacing. The negatively charged carboxylic acid group can neutralize Zn2+, reduce electrostatic repulsion and enhance electrochemical kinetics. The intercalated organic molecules increased the interlayer spacing. Among them, the 0.028EDTA · 0.28NH4+ · V2O5 · 0.069H2O was employed as the cathode with a high specific capacity (464.6 mAh g−1 at 0.5 A g−1) and excellent rate performance (324.4 mAh g−1 at 10 A g−1). Even at a current density of 20 A g−1, the specific capacity after 2000 charge/discharge cycles was 215.2 mAh g−1 (capacity retention of 78%). The results of this study demonstrate that modulation of the electrostatic repulsion and interlayer spacing through the intercalation of organic ligands can enhance the properties of vanadium-based materials.
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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".