Molecular Scale Coating and Nanoscale Carbon Cage Confined Sulfur Cathodes Applied in Lithium Sulfur Batteries
Bibliographic record
Abstract
Lithium sulfur (Li-S) batteries have been intensively concerned in recent years as next generation Li batteries1. Compared with state-of-the-art Li-ion batteries, Li-S batteries own ultrahigh theoretical capacity and energy density, which are considered as most promising candidates applied to electric vehicles and hybrid electric vehicles. However, insulation of sulfur and dissolution of polysulfides are two main issues in sulfur cathodes which hinder the cycle life and application safety of Li-S batteries. To overcome these issues, developing conductive hosts and coating materials are prevailing approaches to improve the performance of Li-S batteries. Herein, we report molecular layer deposited (MLD) alucone coating and nanoscale metal organic framework derived carbon (MOF-C) confined sulfur cathodes in Li-S batteries. Surface coating with carbon or metal oxides has been proven to be a promising approach towards mitigating the shuttle effect in Li-S batteries. An ideal coating material should both cover on based material completely to prevent sulfur migration and also allow Li-ions and electrons transferring through smoothly. Based on these requirements, atomic and molecular layer deposition (ALD and MLD) are ideal technique to synthesize ultrathin and conformal coatings due to the self-limiting nature2 , 3. For the first time, we demonstrate that an MLD alucone coating directly on sulfur electrodes can dramatically improve the cycling stability and capability of Li-S cells. Furthermore, the alucone coated sulfur cathode delivers a discharge capacity of 710 mAh g-1, which is over two times higher than the bare sulfur cathode after 100 cycles4. The alucone coating demonstrated long durability during cell cycling, which explores a new direction in the protection of sulfur cathodes. Porous structured materials are prevailing in sulfur cathode to confine sulfur molecule in host materials. Herein, we developed a new carbon family, metal organic framework derived carbon materials (MOF-Cs) with tunable porous structure via in-situ ammonia treatment5. The ammonia treated MOF-C as carbon host shows an impressive improvement on sulfur cathodes, which performed twice higher discharge capacity retention than that of the pristine MOF-C. This research sheds light to design MOF-C materials with controlled nanostructure not only for Li–S batteries, but also for expanded applications in different energy storage systems. References 1 Bruce, P. G., Freunberger, S. A., Hardwick, L. J. & Tarascon, J. M. Li-O2 and Li-S batteries with high energy storage. Nature materials11, 19-29, (2012). 2 Meng, X., Yang, X. Q. & Sun, X. Emerging applications of atomic layer deposition for lithium-ion battery studies. Adv Mater24, 3589-3615, (2012). 3 Li, X. et al. Atomic layer deposition of solid-state electrolyte coated cathode materials with superior high-voltage cycling behavior for lithium ion battery application. Energy & Environmental Science7, 768, (2014). 4 Li, X., Lushington, A., Liu, J., Li, R. & Sun, X. Superior stable sulfur cathodes of Li-S batteries enabled by molecular layer deposition. Chem Commun (Camb)50, 9757-9760, (2014). 5 Li, X. et al. Tunable porous structure of metal organic framework derived carbon and the application in lithium–sulfur batteries. J Power Sources 302, 174-179, (2016). 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) | 0.001 | 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".