Structural and Chemical Synergistic Encapsulation of Polysulfides Enables Ultralong-Life Lithium-Sulfur Batteries
Bibliographic record
Abstract
The fast depletion of fossil fuels and deterioration of environment have led to increasing demand for renewable energies and efficient energy storage technologies. Lithium-sulfur (Li-S) batteries have been regarded as one of the most promising high-energy power sources in broad applications ranging from electric vehicles to large-scale grid energy storage. Li-S batteries deliver a theoretical energy of 2600 W h kg-1 that is an order of magnitude higher than that of the current lithium-ion batteries (LIBs), and utilize naturally abundant sulfur as the cathode material which significantly reduces the cost. The major challenge for the practical implementation of Li-S batteries resides in the dramatic capacity decay. The intermediate lithium polysulfides (Li x S n , 3≤n≤8) formed during cycling dissolve in the liquid electrolyte, migrate through the separator, and deposit on Li metal anode, causing “shuttle effect”. In addtion, the utilization of the active material is strongly hampered by the intrinsic insulating of S and its discharge product Li2S, leading to a low capacity and poor rate capability. Furthermore, the S cathode suffers from the volume variation (~80%) during lithiation/de-lithiation, causing the loss of electrical contact and the structure instability. Herein, we demonstrate an innovative strategy to efficiently entrap Li x S n from synergistic effect of structural restriction and chemical encapsulation using metal oxide-decorated hollow sulfur spheres. The significance of this strategy lies in that we purposely design a material architecture with both structural and chemical encapsulation effect, and that the material architecture provides a prolonged cycling stability. MnO2 is selected as a model and the MnO2 nanosheets-decorated hollow S spheres (hollow S-MnO2) nanocomposites are achieved through a facile synthesis. The nanocomposites with unique structure possess several features favoring highly stable S electrodes: i) the hollow spheres with inner void space not only alleviate the volume expansion of S on lithiation but also structurally restrict soluble Li x S n within the spherical structure; ii) the decorated MnO2 nanosheets with large surface area efficiently and chemically minimize the polysulfides dissolution by forming strong bonding; iii) the small dimensions of hollow S-MnO2 nanocomposites facilitate both ion and electron transport, leading to a better utilization of the S. This design presents a new strategy to prevent loss of polysulfides by structural and chemical dual-encapsulation, and can be expanded to other metal oxides or metal hydroxides. The unique material architecture enables high-performance S cathodes with high capacity, high sulfur loading and extremely low capacity decay of only 0.028% per cycle over 1500 cycles at 0.5 C-rate. 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".