Impact of functional groups on lithium salt dispersion and mobility in polymer electrolytes
Bibliographic record
Abstract
Abstract Solid Polymer electrolytes are versatile, highly processible and electrochemically compatible with solid electrode materials. The versatility of these materials is a result of the existence of many possible conductive polymer‐salt, polymer‐polymer and salt‐salt combinations. Despite the wide array of available lithium salts, most polymer electrolyte materials are made using lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) due to its long history of achieving relatively high ionic conductivities in polymer electrolyte systems with the most famous being poly(ethylene oxide) (PEO). It is however possible that better ionic conductivities can be achieved with different salts and/or in polymer matrices containing different functional groups. This is because ionic conductivity in polymer electrolytes is partially based on the ability of the polymer matrix to dissolve and bond to the salt. These interactions impact local‐scale ion mobility which can be measured via NMR spectroscopy using pulsed field gradient experiments. In this work, polymer electrolytes are prepared using PEO, hydrogenated nitrile butadiene rubber and poly(propylene) carbonate. Ion mobility, lithium conductivity and salt‐polymer interactions are investigated to compare interactions between LiTFSI and lithium cyano(trifluorosulfonyl)imide in polymers with common salt‐dissociating functional groups such as ethyl, nitrile and carbonate to determine the impact of these interactions on ionic conductivity.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".