PEO based polymer-ceramic hybrid solid electrolytes: a review
Bibliographic record
Abstract
Compared with traditional lead-acid batteries, nickel-cadmium batteries and nickel-hydrogen batteries, lithium-ion batteries (LIBs) are much more environmentally friendly and much higher energy density. Besides, LIBs own the characteristics of no memory effect, high charging and discharging rate, long cycle life and high energy conversion rate. Therefore, LIBs have been widely considered as the most promising power source for mobile devices. Commonly used LIBs contain carbonate based liquid electrolytes. Such electrolytes own high ionic conductivity and excellent wetting ability. However, the use of highly flammable and volatile organic solvents in them may lead to problems like leakage, thermo runaway and parasitic interface reactions, which limit their application. Solid polymer electrolytes (SPEs) can solve these problems, while they also bring new challenges such as poor interfacial contact with electrodes and low ionic conductivity at room temperature. Many approaches have been tried to solve these problems. This article is divided into three parts to introduce polyethylene oxide (PEO) based polymer-ceramic hybrid solid electrolyte, which is one of the most efficient way to improve the performance of SPEs. The first part focuses on polymer-lithium salt (LiX) matrices, including their ionic conduction mechanism and impact factors for their ionic conductivity. In the second part, the influence of both active and passive ceramic fillers on SPEs are reviewed. In the third part, composite SPEs' preparation methods, including solvent casting and thermocompression, are introduced and compared. Finally, we propose five key points on how to make composite SPEs with high ionic conductivity for reference.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".