A review of electrochemical sensing in droplet systems: Droplet and digital microfluidics
Bibliographic record
Abstract
BACKGROUND: Microfluidic technologies based on droplets provide discrete volumes within which chemical and/or biological processes can take place. Two major platforms in this space include droplet microfluidics (emulsions within channels) and digital microfluidics (discrete droplet manipulation by electric fields). The integration of electrochemical sensing with both microfluidic platforms offers advantages in miniaturization and portability, as sensors can be integrated directly within the microfluidic devices and instrumentation is relatively compact. RESULTS: This review provides background on droplet and digital microfluidic technologies and electrochemical sensing before moving to methods and applications. A discussion of the various strategies to integrate sensing electrodes with both droplet and digital microfluidics and the merits of each method are included. A review of the many different applications of these integrated systems is provided. SIGNIFICANCE AND NOVELTY: To date, there are no reviews that solely focus on the integration of electrochemical sensing with droplet and digital microfluidics. There are many advantages to combining electrochemical sensing with these platforms, especially for applications where portability or small form factors are paramount. While early reports on integrating electrochemical sensing with droplet and digital microfluidics are more than a decade old, the field is still relatively nascent, offering opportunity for many 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".