Velour Fabric as an Island-Bridge Architectural Design for Stretchable Textile-Based Lithium-ion Battery Electrodes
Bibliographic record
Abstract
The advancement of wearable electronics depends on the seamless integration of lightweight and stretchable energy storage devices with textiles. Integrating brittle energy storage materials with soft and stretchable textiles, however, presents a challenging mechanical mismatch. It is critical to protect brittle energy storage materials from strain-induced damage and at the same time preserve the softness and stretchability of the functionalized e-textile. Here, we demonstrate the strategic use of a warp-knitted velour fabric in an "island-bridge" architectural strain-engineering design to prepare stretchable textile-based lithium-ion battery (LIB) electrodes. The velour fabric consists of a warp-knitted framework and a cut pile. We integrate the LIB electrode into this fabric by solution-based metallization to create the warp-knitted framework current collector "bridges" followed by selective deposition of the brittle electroactive material CuS on the cut pile "islands". As the textile electrode is stretched, the warp-knitted framework current collector elongates, while the electroactive cut pile fibers simply ride along at their anchor points on the framework, protecting the brittle CuS coating from strain and subsequent damage. The textile-based stretchable LIB electrode exhibited excellent electrical and electrochemical performance with a current collector sheet resistance of 0.85 ± 0.06 Ω/sq and a specific capacity of 400 mAh/g at 0.5 C for 300 charging-discharging cycles as well as outstanding rate capability. The electrical performance and charge-discharge cycling stability of the electrode persisted even after 1000 repetitive stretching-releasing cycles, demonstrating the protective functionality of the textile-based island-bridge architectural strain-engineering design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".