A Nanocrystallite CuS/Nitrogen‐Doped Carbon Host Improves Redox Kinetics in All‐Solid‐State Li<sub>2</sub>S Batteries
Bibliographic record
Abstract
Abstract All‐solid‐state Li–S batteries are a promising energy storage system that can solve the shuttle effects of polysulfides in liquid Li–S batteries. However, sluggish solid‐state reaction kinetics and the low conductivity of cathode materials have impeded their development. Here, a N‐doped carbon embedded with CuS nanoparticles (CuSNC) is reported as a host for Li 2 S in all‐solid‐state batteries that addresses some of these issues. Electrochemical studies, supported by a combination of X‐ray diffraction (XRD), X‐ray photoelectron spectroscopy (XPS), X‐ray absorption spectroscopy (XAS), electron microscopy, and density functional theory (DFT) calculations reveal that CuSNC provides good affinity to Li 2 S. This lowers the activation barrier for the conversion of Li 2 S to sulfur on the charge, suggesting an electrocatalytic effect on the CuS surface. Li + diffusion in the cathode and the reaction kinetics are enhanced compared to N‐doped graphene. The CuSNC/Li 2 S cathode reaches an areal capacity of 1.8 mAh cm −2 and a retention rate of 94% after 100 cycles. At a 1.0 mA cm −2 current density, CuSNC/Li 2 S maintains stable performance over 500 cycles with a low decay rate (0.05% per cycle); at a higher Li 2 S loading, delivers a capacity of 9.6 mAh cm −2 , albeit with more limited cycling. This study provides a promising way to design Li 2 S cathodes to achieve improved reaction kinetics and better electrochemical performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".