Li-Ion-Permeable and Electronically Conductive Membrane Comprising Garnet-Type Li<sub>6</sub>La<sub>3</sub>Ta<sub>1.5</sub>Y<sub>0.5</sub>O<sub>12</sub> and Graphene Toward Ultrastable and High-Rate Lithium Sulfur Batteries
Bibliographic record
Abstract
State-of-the-art lithium sulfur (Li–S) batteries suffer from serious systemic issues, which are mainly derived from polysulfide shuttling effect, poor sulfur utilization, and low Coulombic efficiency. These fundamental challenges impede the practical use of sulfur cathode in commercial battery, albeit its higher theoretical storage capacity compared to intercalation electrodes based Li ion batteries, including graphite-LiCoO 2, graphite–LiFePO 4, and graphite-Li(Ni,Mn,Co)O 2 cells. In this Article, we designed a multifunctional membrane, comprising a graphene nanosheet and Li-stuffed garnet solid-state electrolyte (SSE) composite, to synergistically enhance both cycle stability and rate capability of general sulfur cathode in a facile and effective way. With the synergistic contribution of graphene nanosheet and SSE, the sulfur cathode exhibited a superior capacity of 1165 mAh g –1 at 0.5 C and retained an excellent discharge capacity of 947.03 mAh g –1 (81% of initial capacity) over 200 cycles when a Gr/SSE-separator was employed. In addition, the sulfur cathode with a Gr/SSE-separator delivered a remarkable discharge capacity of 643 mAh g –1 even at 4 C. These results are attributed to three main benefits of Gr/SSE layer: (a) synergistically enhanced electrical and Li-ion conductivity of interlayer, (b) improved electrolyte wettability, and (c) well-entangled architecture of graphene nanosheet and SSE powder.
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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 | 0.001 | 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".