A Cellulose Encapsulated Composite Electrolyte Design: Towards Chemically and Mechanically Enhanced Solid-sodium Batteries
Bibliographic record
Abstract
Replacing liquid electrolytes with solid ionic conductors attracts increasing attention due to the potential of improved battery safety. Solid-state batteries show potential for further increased energy/power density by eliminating the use of packaging accessories for unit cells. Sulfide- and halide-based ceramic ionic conductors exhibit comparable ionic conductivity with liquid electrolytes. These materials, however, are inherently brittle, making them unfavorable for applications. Here, we report a mechanically enhanced composite Na+ conductor that contains 92.5 wt% of sodium thioantimonate (Na3SbS4, NSS) and 7.5 wt% of sodium carboxymethylcellulose (CMC); the latter serves as the binder and an electrochemically inert encapsulation layer. The ceramic and binder constituents were integrated at the particle level, providing ceramic NSS-level Na+ conductivity in the NSS-CMC composite, the more than five-fold decrease of electrolyte thickness obtained in NSS-CMC composite electrolytes provided a five-fold increase in Na+ conductance compared to NSS ceramic pellets. Resulting from the CMC encapsulation, this NSS-CMC composite shows increased moisture resistivity and electrochemical stability, which significantly promotes the cycling performance of NSS- based solid-state batteries, and improves ductility of the material. This work demonstrates a well-controlled, orthogonal process of ceramic-rich, composite electrolyte processing – independent streams for ceramic particle formation along and binder encapsulation in a solvent-assisted environment. This work provides insights into the interplay among the solvent, the polymeric binder, and the ceramic particles in composite electrolyte synthesis, and implies the critical importance of identifying the appropriate solvent/binder system for the precise control of this complicated process. Finally, the work also provides valuable insights into the potential of designing mechanically tailorable battery components via fundamental understanding of the effect of the constituents on the overall composite ductility.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".