Revisiting the Roles of Carbon in the Catalysis of Lithium–Sulfur Batteries
Bibliographic record
Abstract
Carbon materials are the key hosts for the sulfur cathode to improve the conductivity and confine the lithium polysulfides (LiPSs) in lithium–sulfur batteries (LSBs), owing to their high electronic conductivity and strong confinement effect. However, physical or chemical trapping methods have limitations in preventing the dissolution and accumulation of LiPSs in the electrolyte. Catalysis has emerged as a fundamental solution to accelerate the sluggish redox kinetics, and carbon materials acting as catalyst supports or direct catalysts significantly impact the reaction efficiency. Herein, the roles of carbon in the catalysis of LSBs are systematically discussed, focusing on the influence of surface area, pore structure, and surface chemistry on sulfur conversion. Then, two modification strategies, vacancy defects and heteroatom doping, that endow carbon with catalytic activity are summarized. Finally, the remaining challenges and solutions are outlined in terms of the preparation and characterization of the functional carbon in LSBs. This perspective provides essential insights and guidance for the rational design of carbon‐based catalysts in 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".