Co4N nanoparticles embedded in N-doped carbon pores: Advanced interlayer material for lithium-sulfur batteries
Bibliographic record
Abstract
Advanced interlayer materials for lithium-sulfur batteries are highly demanded. In this study, we propose the use of Co 4 N nanoparticles embedded in N-doped carbon pores as an interlayer material for lithium-sulfur batteries. Co 4 N, a metal compound, demonstrates strong chemical adsorption of polysulfide and catalytic activity in polysulfide conversion. Moreover, the N-doped carbon pores not only physically and chemically suppress polysulfide dissolution, but also effectively prevent the agglomeration of Co 4 N nanoparticles during synthesis and battery cycling. Importantly, the synergistic effect of N-doping and Co 4 N electrocatalyst greatly enhances redox catalysis. As a result, the interlayer composed of N-doped carbon pores and Co 4 N nanoparticles retains a capacity of 604.6 mAh/g after 1000 cycles at a current density of 1 C, with a minimal capacity decay of only 0.026% per cycle. Furthermore, even at a higher current density of 5 C, a capacity of 604 mAh/g is still maintained. Finally, we demonstrate the feasibility of achieving a high mass loading with 5.5 mg/cm 2 . • Co 4 N effectively adsorbs polysulfides and catalyzes polysulfides’ conversion • N-doped carbon pores prevent polysulfide dissolution and agglomeration of Co 4 N • Synergic effect of N-doping and Co 4 N electrocatalyst accelerates the redox catalysis
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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".