Multicycle Precursor Infiltration-Thermal Decomposition Achieves High In-Pore Li <sub>2</sub> S Loading in Mesoporous Carbon for High-Performance Lithium Sulfide Batteries
Bibliographic record
Abstract
Lithium sulfide (Li 2 S) is an attractive high-capacity cathode material for lithium–sulfur batteries (LSBs) that enables lithium-anode-free designs with improved safety and simpler manufacturing. However, conventional fabrication methods often yield poor Li 2 S confinement in mesoporous carbon host, restricting electrochemical performance. Here, we present a facile multicycle precursor infiltration-decomposition strategy to synthesize Li 2 S@C nanocomposites with high in-pore Li 2 S loading. Using mesoporous Super P (SP) as the conductive host and lithium trithiocarbonate (Li 2 CS 3 ) as the precursor, sequential infiltration-decomposition cycles progressively increased the pore filling factor (FF) and in-pore Li 2 S loading (IPL), from FF = 38% and IPL = 30% for Li 2 S@SP-1 (one cycle) to FF = 91% and IPL = 73% for Li 2 S@SP-5 (five cycles), while maintaining a total Li 2 S loading of 70 wt %. Structural analyses of Li 2 S@SP-5 by XRD and SEM confirmed reduced crystallite size, suppressed external deposition, and more uniform Li 2 S distribution, contributing to significantly enhanced battery performance relative to Li 2 S@SP-1. Compared to a sulfur-based S@SP counterpart, Li 2 S@SP-5 showed superior performance due to the intrinsic volume contraction of Li 2 S upon charging, which confined sulfur species within the pores and mitigated shuttle effects. Furthermore, full cells paired with Si/C anodes achieved high reversible capacities, demonstrating the viability of lithium-anode-free configurations. This work establishes multicycle infiltration-decomposition as a broadly applicable and scalable strategy to achieve high in-pore Li 2 S loading, offering a promising pathway toward practical, high-energy-density Li 2 S-based batteries.
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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".