Nanocellulose‐Derived Hierarchical Carbon Framework‐Supported P‐Doped MoO <sub>2</sub> Nanoparticles for Optimizing Redox Kinetics in Lithium–Sulfur Batteries
Bibliographic record
Abstract
Abstract The integration of nanocatalysts into the separators of lithium–sulfur batteries (LSBs) boosts the polysulfide conversion efficiency. However, the aggregation of catalyst nanoparticles diminishes the active surface area. Moreover, densely packed catalyst‐modified layers often hinder ion transport rates and impede access to the catalytic sites. To overcome these challenges, a strategy is reported for modifying commercial separators, using wood nanocellulose as a building block to construct hierarchical P‐doped MoO 2−x nanoparticles anchored on N, P co‐doped porous carbon (P‐MoO 2−x /NPC). The web‐like entangled nanocellulose forms a framework for the in situ polymerization of polyaniline, providing abundant anchoring sites for MoO 2 nanoparticles. The addition of P atoms optimizes the d‐band center of MoO 2 and enhances the catalytic activity of polysulfide conversion. The LSBs assembled using a P‐MoO 2−x /NPC coated polypropylene separator display an initial discharge capacity of 1621 mAh g −1 and rate performance of 774 mAh g −1 at 5 C. Even with a sulfur loading of 8.1 mg cm −2 and lean electrolyte conditions, the cell achieves an initial areal capacity of 11.3 mAh cm −2 at 0.1 C. This work provides a biopolymer nanofiber solution for constructing LSB separators with advanced electrochemical reactivity.
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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) | 0.000 | 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".