Uncoordinated chemistry enables highly conductive and stable electrolyte/filler interfaces for solid-state lithium–sulfur batteries
Bibliographic record
Abstract
Composite-polymer-electrolytes (CPEs) embedded with advanced filler materials offer great promise for fast and preferential Li + conduction. The filler surface chemistry determines the interaction with electrolyte molecules and thus critically regulates the Li + behaviors at the interfaces. Herein, we probe into the role of electrolyte/filler interfaces (EFI) in CPEs and promote Li + conduction by introducing an unsaturated coordination Prussian blue analog (UCPBA) filler. Combining scanning transmission X-ray microscope stack imaging studies and first-principle calculations, fast Li + conduction is revealed only achievable at a chemically stable EFI, which can be established by the unsaturated Co–O coordination in UCPBA to circumvent the side reactions. Moreover, the as-exposed Lewis-acid metal centers in UCPBA efficiently attract the Lewis-base anions of Li salts, which facilitates the Li + disassociation and enhances its transference number (t Li + ). Attributed to these superiorities, the obtained CPEs realize high room-temperature ionic conductivity up to 0.36 mS cm −1 and t Li + of 0.6, enabling an excellent cyclability of lithium metal electrodes over 4,000 h as well as remarkable capacity retention of 97.6% over 180 cycles at 0.5 C for solid-state lithium–sulfur batteries. This work highlights the crucial role of EFI chemistry in developing highly conductive CPEs and high-performance solid-state 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 | 0.001 |
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".