(Invited) Rational Design of Efficient Bifunctional Electrocatalysts for Rechargeable Zn-Air Batteries
Bibliographic record
Abstract
Rechargeable zinc-air batteries (ZABs) have been considered as highly promising alternatives for the next-generation energy storage technologies due to their advantages of high energy density, low cost, high safety, and environmental friendliness. Oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are the cornerstones of rechargeable ZABs. The exploration and of high-performance, durable and non-precious metal bifunctional oxygen electrocatalysts is highly desired for the large-scale application of rechargeable ZABs. In this talk, I will introduce our recent progresses on the rational design of a series of high performance bifunctional oxygen electrocatalysts for ZABs. First, we developed various bimetallic and trimetallic nitrides-based nanostructures for high performance bifunctional ORR/OER catalyst in realistic rechargeable ZABs systems. The novel nanostructures exhibit excellent energy density and ultra-long cycling lifetime over 850 cycles (850 h) which are among the best ORR/OER catalysts reported so far. These novel catalysts demonstrate outstanding power density (326 mW cm-2) and excellent charging/discharging performance, far better than those of commercial Pt/C and Ru/Ir-based catalysts. We also employed the in situ XAFS to investigate the origins of the excellent structural stabilities of the bifunctional catalysts. The utilization of bio-mass waste as valuable materials is of strategic importance to our future. We developed a simple and inexpensive metal-free catalyst by employing the bio-waste reed as the single precursor for both carbon and silicon. This reed waste derived Si-N-C metal-free catalyst exhibits better ORR activity and stability than the Pt/C in alkaline media. Moreover, this Si-N-C catalyst exhibits excellent performance as an air cathode for a Zn-air battery device. DFT calculation indicate that the coexistence of Si and N is essential for the high ORR activity. These new findings will not only open an avenue for the rational design of highly active ORR electrocatalysts, but also symbolize an exciting area for sustainable and low-cost materials for advanced clean energy. References 1. Wu, G. Zhang, S. Sun, et al. Nano Energy 2019, 61, 86. 2. Wu, G. Zhang, S. Sun, et al. Energy Storage Materials, 2019, j.ensm.2019.08.009. 3. Wu, G. Zhang, S. Sun, et al. Energy Storage Materials, 2019, 21, 253. 4. Yu, Q. Wei, S. Sun, et al. Journal of Power Sources, 2018, 396, 754. 5. Wu, Q. Wei, S. Sun, et al. Advanced Energy Materials, 2018, 1801836. 6. Wei, Y. Fu, G. Zhang, S. Sun. Current Opinion in Electrochemistry, 2017, 4, 45. 7. Qiu, J. Gao, S. Sun, et al. Applied Catalysis B: Environmental, 2019, 118431. 8. Wei, M. Cherif, S. Sun, et al. Nano Energy, 2019, 62, 700. 9. Wei, G. Zhang, S. Sun, et al. J. Mater. Chem. A , 2018, 6, 4605.[Cover Page] Figure 1
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) | 0.002 | 0.001 |
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".