Solid‐State Revolution: Assessing the Potential of Solid Polymer Electrolytes in Lithium‐Ion Batteries
Bibliographic record
Abstract
Abstract Lithium‐ion batteries (LIBs) are crucial for achieving sustainable energy goals due to their high energy density and long cycle life. They dominate markets like consumer electronics, electric vehicles, and stationary energy storage systems. However, current LIBs use liquid electrolytes, which are toxic, flammable, and their liquid state does not resist dendrite growth, causing battery capacity decline and failure. Additionally, the limited availability of lithium and other metals makes liquid‐based LIBs less sustainable. On the other hand, solid polymer electrolytes (SPEs) offer a safer alternative as they are non‐volatile and can resist dendrite growth. However, ion transport in solids is much more restricted than in liquids, while imperfect solid‐solid interfaces contribute to interfacial resistance leading to lower ionic conductivity and increasing Ohmic losses or requiring battery operation at elevated temperatures. Chemical and mechanical degradation of these interfaces can also result in battery capacity fade, and poorer cyclic performance compared to liquid electrolytes. Understanding the ionic transport mechanisms in SPEs is critical for designing and optimizing the nanostructure of polymers and polymer/electrode interfaces to overcome these limitations. In this review, the fundamental mechanisms of ion transport in SPEs will first be explored. Various state‐of‐the‐art approaches for addressing the key challenges in SPEs and their solutions are then discussed. Furthermore, the current status of SPEs is analyzed to determine their potential for replacing liquid electrolytes in the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".