Integration of complementary split-ring resonators into digital microfluidics for manipulation and direct sensing of droplet composition
Bibliographic record
Abstract
This paper demonstrates the integration of complementary split-ring resonators (CSSRs) with digital microfluidics (DMF) sample manipulation for passive, on-chip radio-frequency (RF) sensing. Integration is accomplished by having the DMF and the RF-sensing components share the same ground plane: by designing the RF-resonant openings directly into the ground plane of a DMF device, both droplet motion and sensing are achieved, adding a new on-board detection mode for use in DMF. The system was modelled to determine basic features and to balance various factors that need to be optimized to maintain both functionalities (DMF-enabled droplet movement and RF detection) on the same chip. Simulated and experimental results show good agreement. Using a portable measurement setup, the integrated CSSR sensor was used to effectively identify a series of DMF-generated drops of ethanol-water mixtures of different compositions by measuring the resonant frequency of the CSSR. In addition, we show that a binary solvent system (ethanol/water mixtures) results in consistent changes in the measured spectrum in response to changes in concentration, indicating that the sensor can distinguish not only between pure solvents from each other, but also between mixtures of varied compositions. We anticipate that this system can be refined further to enable additional applications and detection modes for DMF systems and other portable sensing platforms alike. This proof-of-principle study demonstrates that the integrated DMF-CSSR sensor provides a new platform for monitoring and characterization of liquids with high sensitivity and low consumption of materials, and opens the way for new and exciting applications of RF sensing in microfluidics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".