Rapid bench-top fabrication of poly(dimethylsiloxane)/polystyrene microfluidic devices incorporating high-surface-area sensing electrodes
Bibliographic record
Abstract
The development of widely applicable point-of-care sensing and diagnostic devices can benefit from simple and inexpensive fabrication techniques that expedite the design, testing, and implementation of lab-on-a-chip devices. In particular, electrodes integrated within microfluidic devices enable the use of electrochemical techniques for the label-free detection of relevant analytes. This work presents a novel, simple, and cost-effective bench-top approach for the integration of high surface area three-dimensional structured electrodes fabricated on polystyrene (PS) within poly(dimethylsiloxane) (PDMS)-based microfluidics. Optimization of PS-PDMS bonding results in integrated devices that perform well under pressure and fluidic flow stress. Furthermore, the fabrication and bonding processes are shown to have no effect on sensing electrode performance. Finally, the on-chip sensing capabilities of a three-electrode electrochemical cell are demonstrated with a model redox compound, where the high surface area structured electrodes exhibit ultra-high sensitivity. We propose that the developed approach can significantly expedite and reduce the cost of fabrication of sensing devices where arrays of functionalized electrodes can be used for point-of-care analysis and diagnostics.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".