Effect of sodium dodecylbenzene sulphonate additive on the electrochemical performance of aqueous zinc ion batteries
Bibliographic record
Abstract
Abstract Aqueous zinc ion batteries have shown great potential for large‐scale energy storage systems and have attracted widespread market interest. However, side reactions such as passivation of zinc anode hinder its further development towards practical applications. In this paper, sodium dodecylbenzene sulphonate (SDBS) was used as an aqueous electrolyte additive to improve the cycling performance. The experimental results show that SDBS can form a stable protective film on the electrode surface, inhibit the formation and growth of zinc dendrites, and reduce the side reactions of the electrolyte, thus improving the electrochemical performance of the battery. The application of SDBS as an electrolyte additive in Na3V2(PO4)3/Zn (NVP/Zn) full batteries significantly improves the cycling performance and the Coulombic efficiency of the batteries, inhibits the occurrence of the side reactions, and slows down the decay of the reversible specific capacity. The NVP/Zn full cell with the addition of SDBS achieved 81% capacity retention after 100 cycles at 0.5C magnification, and the cell polarization was significantly reduced. This work provides a simple and feasible method for aqueous zinc ion batteries to reduce polarization and increase the diffusion rate of Zn2+.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".