Dual‐Doped Graphene Quantum Dots to Promote Long‐Life Aqueous Zn‐ion Batteries
Bibliographic record
Abstract
ABSTRACT As the next generation of advanced energy storage devices, aqueous Zn ions batteries (AZIBs) still face many challenges, especially dendrites on the Zn metal anode and side reactions. Although an interface modification strategy has been applied to optimize the stability of Zn metal anodes and has shown some improvement, they are still far from meeting the requirements for practical applications. There is a lack of consideration for designing a multifunctional solid electrolyte interphase (SEI) which modifies the solvation/desolvation structure of Zn ion at the interface of Zn metal anodes. Herein, we constructed an amphiphilic SEI with hydrophilic and hydrophobic properties: N, S dual‐doped graphene quantum dots (GQDs). The N, S dual‐doped GQDs have been synthesized using a one‐step hydrothermal approach and were utilized for Zn anode surface modification. When regulating the solvation structure of the Zn ion interface by N, S dual‐doped GQDs, it also promotes its desolvation kinetics, optimizes the interfacial behavior of Zn ion deposition to prohibit Zn dendrite growth, and suppresses side reactions in the Zn anode surface. The Zn|Zn symmetric cell has achieved a long cycle life of more than 800 h at 5 mA cm −2 . The Zn|V 2 O 5 battery has achieved an excellent performance of more than 80% capacity retention after 1400 cycles at 1 A g −1 . This provides another novel and cost‐effective path for the SEI design of aqueous Zn‐ion batteries.
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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".