Scalable Decoration of Ultrafine Metallic Grains via Hydrolysis‐Induced Deposition for Enhanced Sulfur Utilization in Lithium–Sulfur Batteries
Bibliographic record
Abstract
Abstract Lithium‐sulfur batteries (LSBs) have been arousing great interests for their overwhelming superiority in theoretical specific energy, yet still suffering from facile diffusion and sluggish conversion of polysulfides. To strengthen chemical confinement and catalytic conversion toward polysulfides, ultrafine metallic species are frequently employed as active centers in LSBs cathodes. However, the rigorous procedures to narrow metallic grain size leave formidable challenges to efficiently deposit highly exposed metallic centers. Herein, a strategy of hydrolysis‐induced deposition within a cellulosic circumstance is proposed to enable a scalable decoration of ultrafine metallic grains via mild reaction conditions. The as‐obtained Ru atomic clusters serve as active centers to strongly localize the soluble polysulfides, and meanwhile significantly accelerate the kinetic conversion of polysulfides with the aid of unique Ru‐cellulose coordinated configuration. LSBs with the optimal h‐Ru@NC/CNTs hosts exhibit superior rate performance (712 mAh g −1 at 5.0C) and durable lifetime (773 mAh g −1 after 1000 cycles at 0.8C). This work paves the avenue of efficiently metallizing cellulose with highly exposed metallic surface and properly coordinated electronic structure to promote the utilization of sulfur species in LSBs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".