Effects of Various Valence Ions on an Aqueous Rechargeable Zn//Polyaniline‐coated ZnMn<sub>2</sub>O<sub>4</sub> Battery
Bibliographic record
Abstract
Abstract Zinc corrosion and dendrite formation are the main issues which impede the performance of aqueous zinc ion batteries (ZIBs) after certain times. In this work, we systematically investigated the effects of three different valence ions (e. g., Na+, Mg2+, Al3+) as electrolyte additives on the suppression of zinc corrosion and the inhibition of dendrite growth. By combining experiments and theoretical calculations, it has been found that the existence of Na+ ions effectively suppressing the zinc dendrite growth because Na+ possessess high adsorption energy approximately −0.39 eV. Moreover, Na+ ions could lengthen the zinc dendrite formation duration up to 500 h. On the other hand, the PANI/ZMO cathode materials showed the small band gap approximately 0.097 eV, signifying that the PANI/ZMO possessed the semiconductor characteristics. Furthermore, an assembled Zn//PANI/ZMO/GNP full battery using Na+ ions as electrolyte additive displayed capacity retention of 90.2 % after 500 cycles at 0.2 A g−1, whereas the capacity retention of the control battery using pure ZnSO4 electrolyte was only 58.2 %. This work could provide a reference for the selection of electrolyte additives in future 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 | 0.000 | 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".