Harnessing Metal Ion-Tannic Acid Complexes to Engineer an Artificial SEI for Ultra-Stable Zinc Metal Batteries
Bibliographic record
Abstract
A facile and efficient method for preparing a protective layer on the Zn anode is highly desirable in rechargeable aqueous zinc metal batteries. This coating layer should meet the requirements for suppressing dendritic Zn proliferation and inhibiting parasitic side reactions. We engineer an artificial SEI composed of a metal ion-tannic acid (TA) complex on the Zn anode. The synergistic effect of TA and metal ions enables a dual confinement mechanism with abundant zincophilic sites, thereby enhancing the ability to capture Zn ions and facilitating rapid ion transfer. This complex layer can suppress the hydrogen evolution reaction and have anticorrosion properties. Theoretical calculations demonstrate that the protective coating exhibits a high affinity for Zn2+, significantly reducing the nucleation barrier. Furthermore, it regulates Zn 2+ flux to enhance uniform Zn deposition and restrain dendritic growth. As a result, Zn symmetric cells using the metal ion-TA complex coating exhibit exceptional stability and a lifespan of 2300 h at 1 mA cm –2 /1 mAh cm –2, significantly exceeding that of pristine Zn. Furthermore, coupled with a vanadium-based cathode, the cell retains ∼100% of its initial specific capacity (176.75 mAh g –1 ) at 5 A g –1 through 2500 cycles without observable degradation. This work proposes a promising method for protecting Zn metal anodes with a large-scale implementation potential.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".