New technologies for large-scale micropatterning of functional nanocomposite polymers
Bibliographic record
Abstract
We present a review of different micropatterning technologies for flexible elastomeric functional nanocomposites with a particular emphasis on mold material and processes for production of large size substrates. The functional polymers include electrically conducting and magnetic materials developed at the Micro-instrumentation Laboratory at Simon Fraser University, Canada. We present a chart that compares many of these different conductive and magnetic functional nanocomposites and their measured characteristics. Furthermore, we have previously reported hybrid processes for nanocomposite polymers micromolded against SU-8 photoepoxy masters. However, SU-8 is typically limited to substrate sizes that are compatible with microelectronics processing as a microelectronics uv-patterning step is typically involved, and de-molding problems are observed. Recently, we have developed new processes that address the problems faced with SU-8 molds. These new technologies for micropatterning nanocomposites involve new substrate materials. A low cost Poly(methyl methacrylate) (PMMA) microfabrication technology has been developed, which involves fabrication of micromold via either CO2 laser ablation or deep UV. We have previously reported this large-scale patterning technique using laser ablation. Finally, we compare the two processes for PMMA producing micromolds for nanocomposites.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".