Silicon‐containing liquid polymer electrolytes for application in lithium ion batteries
Bibliographic record
Abstract
Abstract Since the initial discovery of poly(ethylene glycol) (PEG) as a good lithium ion conductor at elevated temperatures, efforts have been made to find ways of incorporating this biocompatible polymer into lithium ion batteries. In areas of research involving implantable medical devices, consumer electronics and automotive power sources, there is a need to develop a ‘safe’ alternative to the solvent‐based, flammable and toxic electrolytes currently employed. However, PEG has been shown to be electrochemically unstable and crystalline at room temperature. In order to overcome these problems, polysiloxanes have been conjugated to PEG in order to improve its conductivity and physical properties. Over the last few years, the group at the University of Wisconsin‐Madison has been involved in collaborations with scientists at Argonne National Laboratories, Grinnell College and Quallion LLC to develop commercially viable silicon‐containing polymer electrolytes. This mini‐review discusses the electrochemical, thermal and physical properties of these electrolytes, and highlights the progress from high molecular weight polysiloxane‐based electrolytes to low‐viscosity, highly conducting oligosiloxane and silane electrolytes. Copyright © 2009 Society of Chemical Industry
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".