Defect‐Enriched Nitrogen Doped–Graphene Quantum Dots Engineered NiCo<sub>2</sub>S<sub>4</sub> Nanoarray as High‐Efficiency Bifunctional Catalyst for Flexible Zn‐Air Battery
Bibliographic record
Abstract
Abstract Flexible Zn‐air batteries have recently emerged as one of the key energy storage systems of wearable/portable electronic devices, drawing enormous attention due to the high theoretical energy density, flat working voltage, low cost, and excellent safety. However, the majority of the previously reported flexible Zn‐air batteries encounter problems such as sluggish oxygen reaction kinetics, inferior long‐term durability, and poor flexibility induced by the rigid nature of the air cathode, all of which severely hinder their practical applications. Herein, a defect‐enriched nitrogen doped–graphene quantum dots (N‐GQDs) engineered 3D NiCo2S4 nanoarray is developed by a facile chemical sulfuration and subsequent electrophoretic deposition process. The as‐fabricated N‐GQDs/NiCo2S4 nanoarray grown on carbon cloth as a flexible air cathode exhibits superior electrocatalytic activities toward both oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), outstanding cycle stability (200 h at 20 mA cm−2), and excellent mechanical flexibility (without observable decay under various bending angles). These impressive enhancements in electrocatalytic performance are mainly attributed to bifunctional active sites within the N‐GQDs/NiCo2S4 catalyst and synergistic coupling effects between N‐GQDs and NiCo2S4. Density functional theory analysis further reveals that stronger OOH* dissociation adsorption at the interface between N‐GQDs and NiCo2S4 lowers the overpotential of both ORR and OER.
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 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".