Dendritic Solid Polymer Electrolytes: A New Paradigm for High‐Performance Lithium‐Based Batteries
Bibliographic record
Abstract
Li-ions battery is widely used and recognized, but its energy density based on organic electrolytes has approached the theoretical upper limit, while the use of organic electrolytes also brings some safety hazards (leakage and flammability). Polymer electrolytes (PEs) are expected to fundamentally solve the safety problem and improve energy density. Therefore, Li-ions battery based on solid PE has become a research hotspot in recent years. However, low ionic conductivity and poor mechanical properties, as well as a narrow electrochemical window limit its further development. Dendritic PEs with unique topology structure has low crystallinity, high segmental mobility, and reduced chain entanglement, providing a new avenue for designing high-performance PEs. In this review, the basic concept and synthetic chemistry of dendritic polymers are first introduced. Then, this story will turn to how to balance the mechanical properties, ionic conductivity, and electrochemical stability of dendritic PEs from synthetic chemistry. In addition, accomplishments on dendritic PEs based on different synthesis strategies and recent advances in battery applications are summarized and discussed. Subsequently, the ionic transport mechanism and interfacial interaction are deeply analyzed. In the end, the challenges and prospects are outlined to promote further development in this booming field.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".