Dual‐single‐atoms of Pt–Co boost sulfur redox kinetics for ultrafast Li–S batteries
Bibliographic record
Abstract
Abstract Applications of lithium–sulfur (Li–S) batteries are still limited by the sluggish conversion kinetics from polysulfide to Li 2 S. Although various single‐atom catalysts are available for improving the conversion kinetics, the sulfur redox kinetics for Li–S batteries is still not ultrafast. Herein, in this work, a catalyst with dual‐single‐atom Pt‐Co embedded in N‐doped carbon nanotubes (Pt&Co@NCNT) was proposed by the atomic layer deposition method to suppress the shuttle effect and synergistically improve the interconversion kinetics from polysulfides to Li 2 S. The X‐ray absorption near edge curves indicated the reversible conversion of Li 2 S x on the S/Pt&Co@NCNT electrode. Meanwhile, density functional theory demonstrated that the Pt&Co@NCNT promoted the free energy of the phase transition of sulfur species and reduced the oxidative decomposition energy of Li 2 S. As a result, the batteries assembled with S/Pt&Co@NCNT electrodes exhibited a high capacity retention of 80% at 100 cycles at a current density of 1.3 mA cm −2 (S loading: 2.5 mg cm −2 ). More importantly, an excellent rate performance was achieved with a high capacity of 822.1 mAh g −1 at a high current density of 12.7 mA cm −2 . This work opens a new direction to boost the sulfur redox kinetics for ultrafast Li–S batteries.
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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".