Integration and detection of biochemical assays in digital microfluidic LOC devices
Bibliographic record
Abstract
The ambition of lab-on-a-chip (LOC) systems to achieve chip-level integration of a complete analytical process capable of performing a complex set of biomedical protocols is hindered by the absence of standard fluidic components able to be assembled. As a result, most microfluidic platforms built to date are highly specialized and designed to fulfill the requirements of a single particular application within a limited set of operations. Electrowetting-on-dielectric (EWOD) digital microfluidic technology has been recently introduced as a new methodology in the quest for LOC systems. Herein, unit volume droplets are manipulated along electrode arrays, allowing a microfluidic function to be reduced to a set of basic operations. The highly reprogrammable architecture of these systems can satisfy the needs of a diverse set of biochemical assays and ensure reconfigurability, flexibility and portability between different categories of applications and requirements. While important progress was made over past years in the fabrication, miniaturization and function programming of the basic EWOD fluidic operations, the success of this technology will in great part depend on the ability of researchers to couple or integrate digital microfluidics to detection approaches that can make the system competitive for LOC applications. The detection techniques should be able to circumvent the limitations of hydrophobic surfaces and exploit the advantages of the array format, high droplet transport speeds and rapid mixing schemes. This review provides an in-depth look at recent developments for the coupling and integration of detection techniques with digital microfluidic platforms for bio-chemical 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".