Advanced Electrospun Membrane for Comprehensive Strain Detection
Bibliographic record
Abstract
In contemporary times, the heightened concern surrounding air pollution, with its substantial annual death toll, has spurred industries to tackle the issue by creating large-scale air filtration systems for public spaces. Detecting air filter clogging is crucial for maintenance, reducing system energy consumption by timely cleaning or replacement. The intriguing field of e-textiles, widely applied in medical, safety, military, and clogging detection scenarios, integrates components seamlessly into soft textile materials based on specific usage requirements. Nanofibers, with their advantageous properties like porosity, lightweight, and high surface area, stand out as a prominent textile structure. To achieve conductive nanofibers, the research focuses on employing in situ conductivity using conductive particles and surface conductivity through immersion and printing methods. Utilizing an electrospinning system, thermoplastic polyurethane nanofibers are produced, with carbon ink printed in various patterns to make them suitable for textile sensor applications. The study concludes by testing membranes with different printed patterns in a ventilation tunnel under varying velocities, assessing the impact of patterns on electrical properties and pressure drop. Ultimately, these conductive membranes show promise as effective strain sensors for detecting air filter clogging.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".