Large scale micropatterning of multi-walled carbon nanotube/polydimethylsiloxane nanocomposite polymer on highly flexible 12×24 inch substrates
Bibliographic record
Abstract
We present the large scale micro-pattering of an electrically conducting multiwalled carbon nanotube (MWCNT) polydimethylsiloxane (PDMS) nanocomposite polymer prepared by high frequency (42 kHz) ultrasonic agitation of MWCNTs in the PDMS polymer matrix. Large scale micropatterning of the MWCNT-PDMS nanocomposite is achieved via soft lithography employing a 12" × 24" poly-methyl methacrylate (PMMA, commercially known as Plexiglass) micromold. The process has a 20µm minimum feature size. The PMMA micromold is fabricated by laser ablating 5-mm thick, 12" × 24" sheets using the Universal Laser System's VersaLASER© laser cutter system which employs a CO2 laser. We have characterized and compared the resistivity of 1cm × 0.5cm × 0.5cm MWCNT-PDMS structures with varying weight percentage (wt-%) of MWCNT (1-10% wt%) in the PDMS matrix with a result that the percolation threshold is achieved at 2 wt-%. Furthermore, we have demonstrated the ability to fabricate large numbers of microelectrodes with a length of 3.0 cm, width of 500µm, and height of 400µm. The resistivity of these electrodes was found to be equal to 9.93Ωm with a deviation of approximately 10%, indicating uniformity across large areas of the substrate. We have also demonstrated a large scale 12" × 24 hybrid microfabrication process for combining micromolded MWCNT-PDMS nanocomposite microstructures with nonconductive PDMS polymer.
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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".