Stabilizing Metallic Zn Electrode Using Organic Acid Additives in Aqueous Zinc-Ion Batteries
Bibliographic record
Abstract
Aqueous zinc (Zn)-ion batteries (AZIBs) have been paid attention to as safe, economical, and high-energy-density storages through multiple electron transfer. Although conventional alkaline solution-based AZIBs are primary batteries, recent studies demonstrated the reversible Zn2+ deposition and stripping processes enabled in mildly acidic solutions. It promises the development of rechargeable AZIBs and viable applications for energy storage systems (ESSs). However, metallic Zn employed as the negative electrode has suffered from severe corrosion and precipitation of electrolyte salts in this low pH condition. The Zn electrode undergoes a hydrogen evolution reaction (HER) as Zn2+ is dissolved from the Zn surface. The corrosion leads to a pH rise, precipitating electrolyte salt as zinc hydroxide forms. For example, hexagonal plates of zinc hydroxide sulfate hydrate (Zn4SO4(OH)6∙xH2O, indicated as ZHS) grew on the Zn electrode in 1 M ZnSO4 solution when pH approached ~5.4. The insulating and randomly oriented ZHS increased the surface resistance and caused the non-uniform Zn deposition. These challenges are mitigated by adding organic acid to the 1 M ZnSO4 solution. As the HER raised pH, the acid was promptly deprotonated, then coordinated with Zn2+. This process caused the thin film formation consisting of three-dimensional zinc glutarate. The zinc glutarate protected the Zn electrode and impeded the electrode corrosion and the ZHS precipitation. We demonstrated a uniform Zn plating and stripping process with 10 mM of organic acid in contrast with dendritic and dead Zn growth in the absence of the additive. Galvanostatic tests of symmetric Zn cells revealed over 1000 h cycles with the additives at a current density of 1 mA cm-2 and a limited capacity of 1 mAh cm-2. In contrast, the Zn cells with the additive-free electrolyte solution exhibited 15 times lower cyclability as micron-scale ZHS plates covered the electrode surface. Our strategy using the cheap additives is feasible to use grid-scale ESSs and shows significantly improved cycling performances. I will present details of Zn surface reactions using organic acid additive and corresponding electrochemical performances in this presentation.
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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".