Nitrogen-Doped Carbon Material As the Electrocatalyst for Oxygen Reduction Reaction in Rechargeable Zinc-Air Flow Batteries
Bibliographic record
Abstract
Rechargeable Zn-air flow batteries are a promising candidate for grid energy storage applications. Zinc-air batteries (ZABs) have low environmental impact, and low cost, which give this system the potential to be applied for large-scale energy storage [1]. Active and durable electrocatalysts in the positive side of the ZABs are important to catalyze the oxygen reduction reaction (ORR) during discharge. N-doped carbons have been reported as a cost-effective catalyst with higher catalytic activity than Pt/C to catalyze the ORR in metal-air flow batteries [2, 3]. In this study colloid imprinted carbons (CICs) and graphene-based materials were applied as the ORR catalyst to improve the performance of the zinc-air battery. Nitrogen doped CICs (NCICs) were prepared by an electropolymerization / carbonization process. Nitrogen doped graphene (NG) were prepared by an electrochemical exfoliation method. The prepared doped carbon materials were characterized by physicochemical characterization methods including SEM and XPS. To evaluate the electrocatalytic activity and the number of electrons transferred in the ORR reaction, cyclic voltammetry (CV) and rotating disk electrode (RDE) experiments were carried out in a three-electrode cell. CV and RDE experiments indicated that NG and NCICs improved the ORR activity. In this study, a flow-through cell design for the air side was applied to evaluate the performance of the air cathode of the Zn-air flow cell, operating at constant current conditions during discharge. 1]Li, Yanguang, Ming Gong, Yongye Liang, Ju Feng, Ji-Eun Kim, Hailiang Wang, Guosong Hong, Bo Zhang, and Hongjie Dai. "Advanced zinc-air batteries based on high-performance hybrid electrocatalysts." Nature communications 4 (2013): 1805. [2] Lai, Linfei, Jeffrey R. Potts, Da Zhan, Liang Wang, Chee Kok Poh, Chunhua Tang, Hao Gong, Zexiang Shen, Jianyi Lin, and Rodney S. Ruoff. "Exploration of the active center structure of nitrogen-doped graphene-based catalysts for oxygen reduction reaction." Energy & Environmental Science 5, no. 7 (2012): 7936-7942. [3] Dai, Liming, Yuhua Xue, Liangti Qu, Hyun-Jung Choi, and Jong-Beom Baek. "Metal-free catalysts for oxygen reduction reaction." Chemical reviews 115, no. 11 (2015): 4823-4892.
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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 | 0.001 | 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 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".