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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".