Probing Sulfur Deposition onto Carbon Nanomaterials from Aqueous, Elemental Sulfur Sols for Lithium–Sulfur Batteries
Bibliographic record
Abstract
Sulfur cathodes for lithium–sulfur batteries often rely on integrating sulfur with high surface area carbonaceous materials. Nanoscale mixing is typically achieved by a lengthy, high-temperature melt imbibition approach that employs carbon nanomaterials in an aggregated solid form. In this work, we present a simple strategy to coat carbon nanomaterials with sulfur in a cost-effective, room-temperature process using inexpensive elemental sulfur. Our results are based on hydrophobic sulfur sols, which have rarely been examined for use in the preparation of sulfur cathodes. We study the deposition mechanism on different carbon materials and find that sulfur dissolves from the sol into the aqueous phase and coats the surface of reduced graphene oxide (rGO) by heterogeneous nucleation and growth, but that this mechanism is not favored for carbon materials such as Ketjen black (KB) and graphene oxide (GO), for which undesirable homogeneous nucleation of micron-sized, insulating sulfur crystals is observed. High loading (3–4 mg sulfur /cm 2 ) rGO-based cathodes prepared using this approach achieve discharge capacities of 1300 mAh/g sulfur (∼4.8 mAh/cm 2 ) at 0.1C and achieve capacities 7-fold higher than cells prepared via traditional melt imbibition approaches at higher rates of 0.8C and 1C. Cells prepared without the need for added binder or conductive additive achieve projected full cell energy densities of 468 Wh/kg at 0.1C when taking into account all inactive components and assuming no lithium metal degradation, indicating that the deposition of sulfur from hydrophobic sols onto carbon nanomaterials can serve as a simple, aqueous-based, one-step process to prepare high sulfur loading cathodes with high projected energy densities.
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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".