Stable NaTFSI-Based Highly Concentrated Electrolytes for Na-Ion and Na–O<sub>2</sub> Batteries
Bibliographic record
Abstract
Sodium bis(trifluoromethanesulfonyl)imide (NaTFSI) and adiponitrile (ADN) have attractive high stability and safety properties for application as electrolytes in Na-ion batteries, but are unusable in modern cells due to significant Al corrosion by NaTFSI, and spontaneous ADN degradation by Na metal. Herein, the electrochemical properties of NaTFSI–ADN electrolytes are investigated as a function of concentration. The ionic conductivity and phase diagram of NaTFSI–ADN is measured, and molecular dynamics (MD) simulations give insight into the solution structure of the electrolyte. The reductive stability of the electrolyte is found to increase drastically with concentration in cyclic voltammetry (CV) experiments, and the parasitic dissolution of Al by TFSI decreases with concentration in linear sweep voltammetry (LSV) and chronoamperometry (CA) tests. In Na-ion cells, the dual effect of reductive stability enhancement and Al corrosion suppression allows the 4.4 M electrolyte to reversibly intercalate high voltage Na 3 V 2 (PO 4 ) 2 F 3 (NVPF) cathodes for multiple cycles, while no NVPF intercalation is observed with the standard 1.0 M electrolyte. Na–O 2 cells also benefit from the highly concentrated electrolyte, showing significantly longer lifetimes in CV experiments on Na–O 2 coin cells. Concentrating NaTFSI–ADN electrolytes offers practical benefits to Na batteries and should be implemented with further stabilizing strategies going forward.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".