Performance and Wearability of Electronic and Infrared Stealth Textiles
Bibliographic record
Abstract
The functionality of smart textiles continues to make progress, but its wearability is often not guaranteed at the same time. The rough and porous fabric surface, the added materials (eg. electronic materials) not possessing characteristics like breathability and drape, and inadequate research are the main reasons leading to this problem. In this work, two kinds of smart textiles, electronic textiles (e-textiles) and infrared stealth fabrics, are studied to improve their performance and ensure their wearability meanwhile. This work provides ideas and theoretical guidance for the development of these and similar smart textiles in the future. \n \nUsing thermoplastic polyurethane (TPU) film as an intermediate layer for printing e-textiles is very common as it provides a smooth surface for device deposition, leading to improved device performance. However, at the same time, the TPU interferes with many desirable properties of the fabric, which makes textiles less comfortable to wear. In order to reduce the impact of TPU film on the wearability of e-textiles, the effects of different TPU types and processing conditions on electronic textile properties are investigated for the first time. It is found that the increase of TPU film thickness can improve the electrical conductivity and stretchability of e-textiles. On the other hand, the drape, water vapor permeability (WVP) and thermal conductivity of textiles decreases. Lower density TPU types are better because they have improved WVP and heat transfer, while electrical conductivity and stretchability are unaffected. Compared to single-layered TPU films, double-layered TPU can greatly improve the electrical conductivity and stretchability of e-textiles because they have better deformation resistance and can isolate the conductive layer and the fabric, reducing the impact of the fabric on the conductive layer. Increasing the curing temperature can improve the electronic performance of the e-textiles, but higher temperatures cause the TPU films to melt and curl. Finally, increasing the laminating temperature and laminating time can effectively improve the electrical properties of e-textiles, but the rigidity of e-textiles becomes larger. These results provide guidance to achieve a more seamless integration of electronics into textiles. \n \nDue to the high surface roughness of fabric, most of the coatings that exert good infrared stealth performance on a flat substrate have greatly weakened performance on fabric. Worse still, these materials severely interfere with the original properties of fabrics after coating. To solve this problem, silver nanowires (AgNWs) are considered for the first time in the preparation of infrared stealth fabrics and found to be very suitable. First of all, due to its metallic characteristics, it can provide a low infrared emissivity for the coating. And compared with other forms of silver structures, it has the advantages of low gloss, fitting degree with fabric, and high transparency in the visible light region. In the optimization of AgNW parameters, it is found that AgNWs with smaller diameters have better infrared stealth effect. AgNW array agglomeration and arrangement phenomenon reduce the infrared stealthing performance of the coating. Adding resin to AgNW solution may better disperse AgNWs and reduce agglomeration and arrangement phenomenon. But the resin's absorption of infrared rays is also noteworthy. It is found that increasing curing time has no significant effect on the infrared reflectance of AgNW array but can improve the electrical conductivity of AgNW array. This shows that instead of electron movement between nanowires, the vibration of electrons in single nanowires determines their stealth properties.
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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 | 0.001 |
| 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 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".