Next‐Generation Embedded Printed Sensors for Near‐Field Monitoring of High‐Performance Composites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Monitoring the structural health of composites during manufacturing and in‐service is desirable to alert against damage or deterioration of conditions beyond an acceptable level. Wireless sensors embedded into materials that can endure the forming and curing of carbon fiber‐reinforced polymer laminates will open the door to automated near‐field detection of key metrics such as temperature, strain, and manufacturing defects. Current sensing technologies are generally too intrusive and fragile to be reliably embedded into laminates or too expensive to be applied commercially. The development of embedded, low‐weight, small‐footprint sensors is reported here, and how these sensors can be used to monitor ply movement during the manufacturing process is demonstrated. These screen‐printed sensors consist of closed‐loop spiral coils excited externally with an AC magnetic field to generate a secondary field, which alerts on the change of relative position of each ply. This proof‐of‐concept work demonstrates how printed coil sensors can be fabricated to generate a high electromagnetic response, while minimizing their footprint in the laminate. It is determined that stacked silver coils, which are subsequently plated with copper to increase the conductance, are capable of producing signals that can be detected through over 3 mm of composite material.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it