Microcontact Printing-Based Fabrication of Digital Microfluidic Devices
Bibliographic record
Abstract
Digital microfluidics is a fluid manipulation technique in which discrete droplets are actuated on patterned arrays of electrodes. Although there is great enthusiasm for the application of this technique to chemical and biological assays, development has been hindered by the requirement of clean room fabrication facilities. Here, we present a new fabrication scheme, relying on microcontact printing (microCP), an inexpensive technique that does not require clean room facilities. In microCP, an elastomeric poly(dimethylsiloxane) stamp is used to deposit patterns of self-assembled monolayers onto a substrate. We report three different microCP-based fabrication techniques: (1) selective etching of gold-on-glass substrates; (2) direct printing of a suspension of palladium colloids; and (3) indirect trapping of gold colloids from suspension. In method 1, etched gold electrodes are used for droplet actuation; in methods 2 and 3, colloid patterns are used to seed electroless deposition of copper. We demonstrate, for the first time, that digital microfluidic devices can be formed by microCP and are capable of the full range of digital microfluidics operations: dispensing, merging, motion, and splitting. Devices formed by the most robust of the new techniques were comparable in performance to devices formed by conventional methods, at a fraction of the fabrication time. These new techniques for digital microfluidics device fabrication have the potential to facilitate expansion of this technology to any research group, even those without access to conventional microfabrication tools and facilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".