Rapid and Inexpensive Method for Fabrication and Integration of Electrodes in Microfluidic Devices
Bibliographic record
Abstract
Electrodes are essential components in a number of microfluidic devices for sensing and actuation. Current methods for electrode fabrication in microfluidic devices are mostly derived from the electronic industry and rely on expensive photolithography and sputtering processes. Here, in this paper, we demonstrate that a combination of xurography and cold lamination can be used to fabricate and integrate electrodes in microfluidic devices. This method is fast, inexpensive, direct and does not require any intermediate layer for pattern transfer. This method utilizes thin metal foils that are commercially available at low-cost and with a large variety of materials in the fabrication of electrode structures. We demonstrate that using xurography, electrode sizes down to 66 μm and pitch sizes as small as 25 μm can be obtained using these foils. After patterning, the electrodes were bonded to plastic films using pressure sensitive adhesives and integrated into microfluidic devices which were also fabricated using xurography approach combined with cold lamination. We demonstrate various applications of these electrodes as electrochemical sensors, as heaters for temperature control and as electrokinetic mixers. This simple method is low-cost and produces electrode structures that are ideally suited for application in microfluidic and lab-on-a-chip devices.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".