A Review of Inactive Materials and Components of Flexible Lithium‐Ion Batteries
Bibliographic record
Abstract
Abstract Flexible Li‐ion batteries (LIBs) have a strong oncoming consumer market demand for use in wearable electronics, flexible electronics, and implantable medical devices. This market demand necessitates research on flexible LIBs to fulfill the energy requirements of these devices. One of the main areas of research of flexible LIBs is the active and inactive materials used in manufacturing these batteries. Active materials are those used in the battery electrodes to store lithium in their structure. The remaining materials in flexible LIBs, which do not directly contribute to energy storage, are inactive materials. Inactive materials and components—including electrode conductive materials, binders, separator, current collectors, electrolyte, and casing/packaging—make up almost 60% of the total weight of a LIB. Thus, they are important in the determination of energy and power density of flexible LIBs. This study reviews the inactive materials and components of flexible LIBs from two aspects. First, inactive materials and components used in flexible LIBs and their properties are compared. Then, the compatibility and stability of inactive materials and components are discussed. Overall, this article gives an extensive insight to researchers on inactive materials and components employed so far for flexible LIBs.
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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) | 0.003 | 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".