Review—Solid and Polymer Electrolyte Materials and Related Processing Methods Suitable for Three-Dimensional Battery Architectures
Bibliographic record
Abstract
Three-dimensional (3D) battery architectures have been envisioned to enable high energy density electrodes without the associated power drop experienced by planar cells. However, the development of 3D cells is hampered by difficulties producing conformal solid-state electrolytes (SSE), solid polymer electrolytes (SPE) and gel polymer electrolytes (GPE) that are pinhole-free and have adequate ionic conductivities. Fortunately, electrolytes in 3D cells are often utilized at lower thickness, which may compensate the decreased ionic conductivity. Here, we comprehensively review potential 3D SSE, SPE and GPE electrolyte materials by compiling their thickness and room temperature ionic conductivity. We use area specific resistance (ASR) as a metric to compare 3D electrolytes with one another and conventional electrolytes. We find that certain process-material combinations, such as atomic layer deposition of SSEs, electrodeposition of SPEs and GPEs, and initiated chemical vapor deposition of SPEs demonstrate ASRs beneath the interfacial impedances of Li-based systems and approach state-of-the-art electrolytes. We also comment on additional factors, such as electrochemical stability, that should be evaluated when determining 3D electrolyte suitability. Future research should focus on adapting known materials chemistries for conformal deposition techniques to further improve the ionic conductivity, as these techniques are capable of producing the necessary thicknesses and conformality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.006 | 0.005 |
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".