Fabrication of optically patternable nanocomposite layers for smart polymer structure applications
Bibliographic record
Abstract
We are reporting the development of a conductive nanocomposite for sensing applications. Unlike the previously reported research, our process flow allows for fabrication of nanocomposite structures that can be patterned using typical lithography techniques for fine features. This advantage lets us fabricate embedded micro-sensors within a multi-layer polymer structure. In order to demonstrate the principle, different SU8 structures were impregnated with carbon nanotubes and studied. These nanocomposite structures behave in a manner, resembling results of percolation theory. It is also demonstrated that filler concentration strongly affects the response of the fabricated nanocomposite structures to strain with measured gauge factors ranging from 1.5 to more than 15. The fabrication process is explained in detail and our study is supported by modeling of a random carbon nanotubes network based on our own simulation code. This technology will be used for fabrication of low-cost all-polymer structures with embedded sensors and actuators.
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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".