Hierarchical Carbon Nanosheet Arrays for Lithium Metal Batteries and Electrochemical Water Splitting
Bibliographic record
Abstract
With the highest theoretical capacity and lowest electrochemical potential, lithium (Li) metal is considered as the ideal anode for Li-ion based battery.[1] Unfortunately, the practical application of Li metal battery has been hindered by the growth of dendritic crystals, infinite volume changes, and high reactivity of lithium metal during battery charging and discharging.[2] Despite previous progress, it is still highly needed to develop efficient lithium host to solve aforementioned problems. Electrochemical water splitting, namely hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) has been under intense research.[3] This is because hydrogen is the cleanest fuel and thus considered as the major fuel for future use. Electrochemical HER and OER are sluggish which requires the use of catalysts to boost the reaction rate. However, the best catalysts for HER and OER are still based on noble metals such as Pt and Ir/Ru, which are too expensive to be afforded widely. Developing low-cost, earth-abundant, highly efficient and stable bifunctional catalyst for overall water splitting is highly required but remains challenging.[4] In the past decades, hierarchical arrays have been widely used for energy storage and conversion devices due to the highly exposed surface, fast transportation of ions and matter, intimate contact between current collector and active materials.[5-6] Nevertheless, the report on hierarchical carbon arrays remains rare. Herein, we develope a general method for coating of hierarchical carbon nanosheet arrays on substrates for lithium metal battery and water splitting. For the lithium metal battery part, hierarchical nanosheet arrays are grown onto Cu foil by the self-assembly of polymer, which was converted to carbon arrays after carbonization. Owing to the hierarchical carbon arrays, highly exposed surface area, and electroactive nitrogen dopants, the hierarchical carbon-coated Cu foil can facilitate the growth of horizontal lithium crystals, inhibiting lithium dendrites growth, and thus manifesting high Coulombic efficiency of >98% in ether electrolyte and >90% in carbonate electrolyte as well as long-cycling stability for 500 cycles. As for water splitting, hierarchical polymer nanosheets are grown on Ni-Mo-containing arrays which grow onto Ni foam, which was converted by carburization to Ni-Mo2C-carbon hybrid arrays with high activity and stability for both HER and OER. The catalyst, acting as both cathode and anode, requires only 1.61 V to generate a current density of 10 mA cm-2 towards overall water splitting and the activity can be maintained for more than 150 h. References: (1) Lin, D.; Liu, Y.; Cui, Y. Nature Nanotechnology 2017, 12, 194. (2) Xie, J.; Christensen, J.; Cui, Y, et al.Science Advances 2018, 4. (3) Zhu, C.; Zhang, H.; Fan, H. J. et al Adv. Mater. 2018, 30, 1705516. (4) Zhu, Y. P.; Ma, T. Y.; Jaroniec, M.; Qiao, S. Z., Angew. Chem. Int. Ed. 2017, 56, 1324-1328. (5) Fang, M.; Dong, G. F.; Wei, R. J.; Ho, J. C. Adv. Energy Mater. 2017, 7, 1700559 (6) Zhou, L. M.; Zhang, K.; Hu, Z.; Tao, Z. L.; Mai, L. Q.; Kang, Y. M.; Chou, S. L.; Chen, J. Adv. Energy Mater. 2018, 8, 1701415. Figure 1
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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) | 0.003 | 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".