Solid Electrolyte, Flexible Li-ion Fiber Batteries for Compliant Energy Storage and Wearable Applications
Bibliographic record
Abstract
In my talk, I would present advances in fabrication of the solid electrolyte-based flexible li-ion fiber batteries, as well as their numerous potential applications in compliant energy storage and wearable devices. In particular, I would detail fabrication of the fiber-shaped lithium ion batteries assembled by twisting a cathode filament together with an anode filament. The cathode filament is fabricated by depositing a LiFePO4 (LFP)-composite layer onto a steel-filled polyester conductive thread (SPCT). As anode filaments, we propose several scenarios including a Li4Ti5O12 (LTO)-composite coated SPCT (dip-and-dry deposition), a tin-coated SPCT (PVD deposition) as well as a bare tin wire. An electrolyte composite layer consisting of LiPF6 and polyethylene oxide is then deposited onto both the anode and cathode filament before the battery assembly. To characterize the electrochemical and mechanical properties of the proposed batteries, we performed charge-discharge tests with different C-rates, electrical impedance tests and mechanical bending tests. The fabrication of the proposed LIBs is simple and cost-effective, as compared to the fiber-shaped LIBs using carbon nanotube fibers. Moreover, the reported LIBs are well suitable for the wearable applications as they feature all-solid electrodes and electrolyte, unlike the majority of other currently existing LIBs that utilize liquid organic solution-based electrolytes that may cause leakage and cause safety concerns. Among other advantages of the proposed LIB are light weight, ease of fabrication, high specific capacitance, high energy density, and good durability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".