(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, Na x M x Si x O x (M = rare-earth metals, x = integer (1-10)). [3] They have the advantage of low sintering temperature (~1050 °C) and 3D framework structure containing MO 6 octahedra and SiO 4 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 NaPF 6 . [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 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) | 0.000 | 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) | 0.000 | 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".