Smart Textiles for Visible and IR Camouflage Application: State-of-the-Art and Microfabrication Path Forward
Bibliographic record
Abstract
Protective textiles used for military applications must fulfill a variety of functional requirements, including durability, resistance to environmental conditions and ballistic threats, all while being comfortable and lightweight. In addition, these textiles must provide camouflage and concealment under various environmental conditions and, thus, a range of wavelengths on the electromagnetic spectrum. Similar requirements may exist for other applications, for instance hunting. With improvements in infrared sensing technology, the focus of protective textile research and development has shifted solely from providing visible camouflage to providing camouflage in the infrared (IR) region. Smart textiles, which can monitor and react to the textile wearer or environmental stimuli, have been applied to protective textiles to improve camouflage in the IR spectral range. This study presents a review of current smart textile technologies for visible and IR signature control of protective textiles, including coloration techniques, chromic materials, conductive polymers, and phase change materials. We propose novel fabrication technology combinations using various microfabrication techniques (e.g., three-dimensional (3D) printing; microfluidics; machine learning) to improve the visible and IR signature management of protective textiles and discuss possible challenges in terms of compatibility with the different textile performance requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".