Low-cost and versatile integration of microwire electrodes and optical waveguides into silicone elastomeric devices using modified xurographic methods
Bibliographic record
Abstract
Microelectrodes are used in microfluidic devices for a variety of purposes such as heating, applying electric fields, and electrochemical sensing. However, they are still manufactured by expensive deposition techniques such as sputtering or evaporation and patterned using photolithography methods. More recently, alternate methods including nanoparticle sintering and use of liquid metal flowing through microchannels have been used to fabricate microelectrodes. These methods are limited in the material choices or require post processing to be integrated into microchannels. Here we developed a low-cost and versatile method to integrate high-quality metal microwires into polydimethylsiloxane (PDMS) using xurography. The microwire integration process includes cutting slit pattern on PDMS substrate and subsequent writing metal microwires into the slit pattern using a specialized tip. Then the microwire-integrated PDMS was sealed/bonded using uncured PDMS prepolymer. This method enables integration of metal microwires of diameter as small as 15 μm into PDMS devices. Integration of multiple microwires with minimum spacing of 150 μm has also been demonstrated. The versatility of this method is demonstrated by the fabrication of metal microwire suspended in the middle of the microchannel, which is difficult to achieve using conventional electrode fabrication methods. This low-cost method avoids expensive clean room fabrication yet producing high-quality electrodes and can be used in a variety of microfluidic and MEMS applications.
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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) | 0.001 | 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 | 0.001 | 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".