Low‐Cost and High‐Speed Fabrication of Camouflage‐Enabling Microfluidic Devices using Ultrahigh Molecular Weight Polyethylene
Bibliographic record
Abstract
This study demonstrates a multi‐spectrum camouflage control via microfluidic methods within a thermally and visibly semi‐transparent polymer (polyethylene). Microfluidic devices have a high potential for achieving multiband camouflage including both visible and infrared (IR) spectrums because they can manipulate fluids that may be dyed, transparent, or opaque to different parts of the electromagnetic spectrum. However, most traditional polymers used for microfluidics are not very transparent in the thermal IR region (≈8–14 um wavelengths), which limits their effectiveness in this spectrum. It develops a high‐speed, low‐cost robust process to fabricate microfluidic devices entirely made from polyethylene using xurography and thermocompression molding techniques. Moreover, a novel method that thermally bond macro‐scale polyethylene tubing to micro‐scale channels is developed. The simplicity and flexibility of the method allow the fabrication of devices with different channel heights, widths, and patterns. Upon filling the microfluidic devices with dyed liquids and testing them with different backgrounds, the devices show fast and high visible camouflage capabilities. Moreover, the thermal IR appearance of microfluidic systems can be altered without changing temperature by incorporating a metalized surface which can be covered by an IR opaque liquid to alter the apparent temperature when reflecting IR sources.
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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".