The Role of Carbon Electrodes Pore Size Distribution on the Formation of the Cathode–Electrolyte Interphase in Lithium–Sulfur Batteries
Bibliographic record
Abstract
Abstract The use of sulfur is advantageous in next generation lithium batteries replacing scarce metals and leading to enhanced specific energy compared to established energy storage devices. Herein, the utilization of microporous (d<2 nm) carbon/sulfur composites using a carbonate‐based electrolyte is studied to obtain more insights into the formation of a solid‐electrolyte interphase (SEI) layer on the cathode surface (cathode‐electrolyte interphase, CEI) in Li‐S cells. The sulfur was confined in model carbide‐derived carbon (CDC) system with uniform micropores as well as in commercial microporous activated carbon materials. The molecular structure of sulfur in the pores is characterized by means of EXAFS spectroscopy. The electrochemical evaluation of confined sulfur cathodes revealed quasi solid‐state transformation mechanism as well as the generation of a protective SEI layer on the cathode surface. This cathode‐electrolyte interphase (CEI) allows common microporous carbon/sulfur cathodes to reversibly cycle in carbonate‐based electrolyte, while mesoporous systems show rapid capacity fade due to permeable CEIs. Based on this result, the influence of the porosity of the porous carbon host materials on the essential cathodic SEI layer formation is discussed for carbonate‐based electrolytes in Li‐S batteries.
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".