Porous and Conductive Fiber Woven Textile for Multi‐Functional Protection, Personal Warmth, and Intelligent Motion/Temperature Perception
Bibliographic record
Abstract
Abstract Industrialization and human activities have introduced numerous hazards, including exposure to harsh chemicals, radiation, static electricity, and fire risks, particularly in high‐risk sectors such as engineering, rescue operations, military, and aerospace. This study presents a multi‐functional protective textile developed from a conductive fiber composed of polytetrafluoroethylene (PTFE) and carbon nanotubes (CNT), crucial for ensuring personal safety. The conductive fiber demonstrates remarkable strength (17.3 MPa), high porosity (76%), and significant electrical conductivity (185 S m −1 ), coupled with excellent fineness and flexibility due to its dual‐nanofibrous structure. The resulting textile exhibits exceptional hydrophobicity, chemical resistance, and high electromagnetic interference shielding effectiveness (29 dB in the X‐band), alongside a superior UV protective factor (>3000) and anti‐static properties. Notably, it possesses outstanding electro/photo thermal conversion capabilities, enabling consistent heat generation for personal warmth. Additionally, the textile responds electrically to deformation and temperature changes, facilitating intelligent applications such as motion and temperature monitoring and fire alerts. This work offers a novel strategy for fabricating PTFE‐based composite fibers with porous microstructures and high electrical conductivity, setting a new standard for next‐generation protective clothing with advanced functionalities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".