Integrated Digital Microfluidic Platform for Voltammetric Analysis
Bibliographic record
Abstract
Digital microfluidics (DMF) is an emerging technique for manipulating small volumes of liquids. DMF is particularly well suited for analytical applications as it allows automated handling of discrete samples, and it has been integrated with several inline analysis techniques. However, examples of the integration of DMF with electroanalytical methods are notably scarce, and those that have been reported rely on external electrodes that impose limitations on complexity. To combine the full capabilities of DMF with voltammetry, we designed a platform featuring a three-electrode electrochemical cell integrated in a microfabricated DMF device, removing the need for external electrodes and allowing for complete droplet control. The performance of the DMF/voltammetry system is comparable to that of a commercial screen printed electrode, and the new platform was applied to generating a calibration series for acetaminophen with a limit of detection of 76 μM and good precision (4% average RSD), all with minimal human intervention. We propose that this platform and variations thereof may be a useful new tool for microscale electroanalysis and will be a complementary system to existing inline analysis techniques for DMF.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".