Steric hindrance and orientation polarization by a zwitterionic additive to stabilize zinc metal anodes
Bibliographic record
Abstract
Abstract Zinc metal stands out as a promising anode material due to its exceptional theoretical capacity, impressive energy density, and low redox potential. However, challenges such as zinc dendrite growth, anode corrosion, and side reactions in aqueous electrolytes significantly impede the practical application of zinc metal anodes. Herein, 3‐(1‐pyridinio)‐1‐propanesulfonate (PPS) is introduced as a zwitterionic additive to achieve long‐term and highly reversible Zn plating/stripping. Due to the orientation polarization with the force of electric field, PPS additive with π–π conjugated pyridinio cations and strong coordination ability of sulfonate anion tends to generate a dynamic adsorption layer and build a unique water–poor interface. PPS with steric hindrance effect and strong coordination ability can attract solvated Zn2+, thereby promoting the desolvation process. Moreover, by providing a large number of nucleation sites and inducing zinc ion flow, the preferred orientation of the (002) crystal plane can be achieved. Therefore, the interfacial electrochemical reduction kinetics is regulated and uniform zinc deposition is ensured. Owing to these advantages, the Zn//Zn symmetrical cell with PPS additive exhibits remarkable cycling stability exceeding 2340 h (1 mA cm−2 and 1 mA h cm−2). The Zn//V2O5 full cell also delivers stable cycling for up to 6000 cycles.
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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".