N‐Vacancy Enriched Porous BN Fibers for Enhanced Polysulfides Adsorption and Conversion in High‐Performance Lithium‐Sulfur Batteries
Bibliographic record
Abstract
Abstract Severe shuttle effect of soluble polysulfides and sluggish redox kinetics have been thought of as the critical issues hindering the extensive applications of lithium‐sulfur batteries (LSBs). Herein, one‐dimensional boron nitride (1D BN) fibers with abundant pores and sufficient N‐vacancy defects were synthesized using a thermal crystallization following a pre‐condensation step. The 1D structure of BN facilitates unblocked ions diffusion pathways during charge/discharge cycles. The embedded pores within the polar BN strengthen the immobilization of polysulfides via both physical confinement and chemical interaction. Moreover, the highly exposed active surface area and intentionally created N‐vacancy sites substantially promote reaction kinetics by lowering the energy barriers of the rate‐limiting steps. After incorporating with conductive carbon networks and elemental S, the as‐prepared S/Nv‐BN@CBC cathode of LSBs deliver an initial discharge capacity of up to 1347 mAh g −1 at 200 mA g −1 , while maintaining a low decay rate of 0.03 % per cycle over 1000 cycles at 1600 mA g −1 . This work offers an effective strategy to mitigate the shuttle effect and highlights the significant potential of defect‐engineered BN in accelerating the reaction kinetics of 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".