Optimizing Adsorption‐Catalysis Synergy to Accelerate Sulfur Conversion Kinetics in Room‐Temperature Na‐S Batteries
Bibliographic record
Abstract
Abstract Room‐temperature sodium‐sulfur (RT Na‐S) batteries are expected to become the next‐generation energy storage system due to their ultrahigh theoretically energy density of 1274 Wh kg −1 , abundant sulfur resource, and low cost. However, practical application is hindered by challenges of severe shuttle effect and sluggish S conversion kinetics. In this study, a series of nano‐sized nickel‐based chalcogenides are designed and fabricated as electrocatalysts for S cathode. The p orbitals originated from different anions show great effect on the partial‐filled d orbital of the metal Ni site, which further regulates the electronic states of the catalytic site. Theoretical and experimental results confirm the excellent electrocatalytic performance of NiSe electrocatalyst with low reaction energy barriers, moderate adsorption capability, and strong catalytic conversion ability, consistent with Sabatier's principle. The optimized NiSe catalyst presents a high reversible capacity of 720.4 mAh g −1 with excellent durability over 200 cycles at 0.2 A g −1 retained a capacity of 401.4 mAh g −1 after 1000 cycles at 2 A g −1 in RT Na‐S batteries. This work presents the balancing of adsorption and catalytic conversion toward polysulfides via the modulation of d/p orbitals of active sites.
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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".