Skin-inspired interface modification strategy toward a structure-function integrated hybrid smart fabric system with self-powered sensing property for versatile applications
Bibliographic record
Abstract
Fabric-based composites with superior mechanical properties and excellent perceptive function are highly desirable. However, it remains a huge challenge to attain structure-function integration, especially for hybrid fabric composites. Herein, a skin-inspired interface modification strategy is proposed toward this target by constructing a hybrid smart fabric system consisting of two types of smart fabrics: carbon nanotube (CNT)/MXene-modified aramid fabrics and zinc oxide nanorod (ZnO NR)-modified carbon fabrics. Based on that, flexible piezoelectric pressure sensors with skin-like hierarchical perception interfaces are fabricated, which demonstrate superb sensitivity of 2.39 V·kPa -1 and are capable of various wearable monitoring tasks. Besides, the interface-modified hybrid fabric reinforced plastics can also be fabricated, which are proven to possess 13.6% higher tensile strength, 10.1% elastic modulus. More impressively, their average energy absorption can be improved by 111.9%, accompanied with inherent damage alert capability. This offers a paradigm to fabricate structure-function integrated hybrid smart fabric composites for the smart clothing and intelligent aerial vehicles.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".