Non-covalent Interactions Promoted Kinetics in Perylene Diimide-based Aqueous Zn-ion Batteries: an operando ATR-IR Study
Bibliographic record
Abstract
Metallic Zn electrodes for aqueous Zn-ion batteries suffer from dendrite and layered double hydroxide formation, which limit the battery cycle life. This morphologically unstable interface results from inhomogeneous Zn deposition at the Zn electrode. Perylene-based organic anodes, as an alternative, store Zn2+ through a Zn-enolate coordination mechanism following the reduction of carbonyl groups, potentially bypassing challenges associated with Zn anode. However, organic anodes exhibit low electrical conductivity and therefore show low rate performance. Molecular aggregation of conjugated aromatics plays a key role in the electrical conductivity of this class of material, and it is important to understand their impact on the battery rate performance. Herein, we combined electrochemistry and in-situ ATR-IR characterizations to demonstrate the dominating role of aggregates in perylene-based electrodes in the enhancement of the electrode kinetics. We demonstrated the principle of using non-covalent interaction to form a supermolecular network that exhibits more than four orders of magnitude increase in the electron transfer rate content responsible for Zn2+ storage and provides nearly doubled charge storage capacities. The reorganization of perylene units was driven by π-π stacking and hydrogen bonding between the active material and a mediator, ethylene diamine (EDA), introduced as an additive during electrode processing. We showed that in practice this process can occur during the solution process at moderate temperature.
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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".