(Invited) Electrolytes for Next-Generation Sodium Metal Batteries
Bibliographic record
Abstract
Developing reliable and cost-effective electrical energy storage systems (EESs) for portable electronics, electric vehicles, and grid storage applications is vital. Na-ion batteries are the most promising alternative technology owing to their natural abundance and low sodium cost.[1] Solid-state batteries (SSBs) have garnered extensive attention for their ability to suppress the safety hazards of organic liquid electrolytes by replacing them with solid electrolytes.[2] Among the various solid electrolytes being explored, our group mainly focuses on NASICON (Sodium superionic conductor) type silicate and solid polymer electrolytes. Sodium silicates are a class of materials with composition, NaxMxSixOx (M = rare-earth metals, x = integer (1-10)).[3] They have the advantage of low sintering temperature (~1050 °C) and 3D framework structure containing MO6 octahedra and SiO4 tetrahedra providing facile ionic movement.[4] Solid polymer electrolytes (SPE) are known for their flexibility and electrode compatibility. Highly conductive, filler-free composite solid polymer electrolyte films were prepared using poly(vinylidene fluoride), poly(vinyl pyrrolidone) (PVP), and NaPF6.[5] PVP binder played an essential role in improving Na ion conductivity and excellent plating−stripping performance. This talk will present an overview of these solid electrolytes for next-generation sodium metal batteries. References [1] C. Zhou, S. Bag, V. Thangadurai, ACS Energy Lett., 2018, 3, 2181–2198. [2] V. Thangadurai, B. Chen, Chem. Mater., 2022, 34, 6637–6658. [3] S. Narayanan, S. Butler, S. Reid, S. Bag, V. Thangadurai, US Pat. 2022/0271330 A1, 2022, 2022. [4] A. Sivakumaran, A. J. Samson, V. Thangadurai, Energy Technol., 2023, 2201323. [5] A. A. Bristi, A. J. Samson, A. Sivakumaran, S. Butler, V. Thangadurai, ACS Appl. Energy Mater., 2022, 5, 8812–8822.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.040 |
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".