Preparation, Micro‐Patterning and Electrical Characterization of Functionalized Carbon‐Nanotube Polydimethylsiloxane Nanocomposite Polymer
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Bibliographic record
Abstract
Abstract Summary : We present the preparation, improved micro‐patterning, and electrical property characterization of COOH‐ functionalized mutli‐walled carbon nanotube (MWCNT) and polydimethylsiloxane (PDMS) conductive nanocomposite polymers that can be employed for lab on a chip applications. The nanocomposites are prepared by mixing functionalized MWCNTs into an uncured PDMS matrix and employing high frequency ultrasonics (∼ 42‐50 kHz) using a horn tip probe. The prepared nanocomposites are micromolded using soft lithography techniques down to a feature size of 25 µm against a micropatterned SU‐8 polymer master. An array of peg like microstructures have been fabricated with a radii of 25 µm and height of 100 µm, that are embedded on a non‐conductive PDMS substrate using novel and improved fabrication techniques. The percolation threshold of the prepared nanocomposite is achieved at 1.5 weight percentage (wt.%) of COOH‐ functionalized MWCNT in the PDMS matrix. Resistivity levels at 2 wt.% of functionalized MWCNTs are 62 Ω‐cm or better, which is an improvement over our previously reported nanocomposite resistivity value of 100 Ω‐cm at 2 wt.% of nonfunctionalized MWCNT's in a PDMS matrix. The nanocomposites also have fairly uniform dispersion and no agglomeration of COOH‐ functionalized MWCNT as shown by SEM analysis. Furthermore, the nanocomposites show a negative temperature coefficient of resistivity (NTCR), making them ideal candidates for micropatternable temperature microsensors for lab on a chip systems.
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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