A vapor-assisted construction of 3D highly porous boron nitride for anchoring and catalyzing polysulfides in lithium-sulfur batteries
Bibliographic record
Abstract
Lithium-sulfur batteries (LSBs) have been holding a great potential to serve as high energy density power source in advent of “carbon neutrality” era. However, LSBs nowadays still suffer from the notorious shuttle effect of polysulfides and sluggish redox reactions involving multiphase conversion. Herein, three-dimensional boron nitride (3D BN) with hierarchically porous architecture was innovatively constructed and incorporated into carbon cloth (CC) by means of vapor-assisted spatial growth (denoted as 3D-BN@CC). The as-prepared 3D-BN@CC sulfur host with highly exposed BN active sites exhibits strong adsorption and efficient catalysis toward soluble polysulfides . To further block the polysulfides leaking-out, an ultrathin composite layer composed of graphene (Gr) and nanocellulose (NC) is deliberately coated onto commercial separator (labeled as NC@Gr separator). LSB equipped with S/3D-BN@CC cathode and NC@Gr separator delivers a reversible capacity of 850 mAh/g with a high S loading of 5.36 mg cm −2 . The approach developed in this work to create sophisticated 3D BN scaffold for S and sheds lights on the superiority of 3D-BN@CC in anchoring and catalyzing polysulfides to finally lead to high-performance LSBs.
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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".