A review of digital microfluidics as portable platforms for lab-on a-chip applications
Bibliographic record
Abstract
Following the development of microfluidic systems, there has been a high tendency towards developing lab-on-a-chip devices for biochemical applications. A great deal of effort has been devoted to improve and advance these devices with the goal of performing complete sets of biochemical assays on the device and possibly developing portable platforms for point of care applications. Among the different microfluidic systems used for such a purpose, digital microfluidics (DMF) shows high flexibility and capability of performing multiplex and parallel biochemical operations, and hence, has been considered as a suitable candidate for lab-on-a-chip applications. In this review, we discuss the most recent advances in the DMF platforms, and evaluate the feasibility of developing multifunctional packages for performing complete sets of processes of biochemical assays, particularly for point-of-care applications. The progress in the development of DMF systems is reviewed from eight different aspects, including device fabrication, basic fluidic operations, automation, manipulation of biological samples, advanced operations, detection, biological applications, and finally, packaging and portability of the DMF devices. Success in developing the lab-on-a-chip DMF devices will be concluded based on the advances achieved in each of these aspects.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".