Permanently Magnetizable Composite Polymer Ink for Textile-Based Wearables
Bibliographic record
Abstract
We present the fabrication and characterization of magnetic nanoparticle polymer composites that can be patterned on textiles. The resulting structures can be permanently magnetized and used to develop wearable devices such as sensors and actuators for multiple applications that may include physical marker data collection for tracking; wearable microfluidics-based systems; haptic devices; and device-to-device alignment and interconnect. The development of permanently magnetizable polymer composite inks allows for simple and inexpensive system construction on the garment itself without the need for transfer or other separate microfabrication processes. In this paper, different compositions of an in-house developed NdFeB-based magnetic ink are investigated. Characterization is performed primarily on samples that are fabricated using an inexpensive and simple screen-printing process on cotton fabric using base inks that are designed specifically to adhere to textiles, but that we have embedded with magnetic particles at 5 different weight percentages (wt-%) ranging from$29.8 \text{wt}-\%$to$69.9 \text{wt}-\%$. We screen-print patterns 3 mm by 10 mm in size, with penetration depths of the inks into the textile surface up to 0.438 mm and magnetize them under a 3T field. We perform quantitative adhesion tests and magnetic characterization. For the magnetic characterization, magnetized samples produce magnetic fields ranging from 0.066 mT to 0.496 mT at the center of the rectangular samples and 0.045 mT to 0.371 mT at the sides of the rectangles. We also characterize the material using a SQUID with an applied magnetic field ranging from -7.0 T to 7.0 T at room temperature (300 K), resulting in magnetization curves that generally show higher magnetic saturation at higher wt-% of particles. Overall, compositions with a lower concentration (wt-%) of magnetic particles perform better in adhesion and permeation tests, but compositions with higher concentrations of magnetic particles have superior magnetic performance.
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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".