Modular Centrifugal Microfluidics for Sample Preparation
Bibliographic record
Abstract
Sample preparation is often a critical and labor-intensive step in molecular biology and analytical chemistry. It bottlenecks biological assays, where liquid-handling speed and technique influence the outcome. While automation improves efficiency, traditional systems such as robotic platforms remain costly, complex, and resource-intensive to manufacture. Centrifugal microfluidic devices provide liquid-handling operations at the microliter scale by using microfluidic channels and chambers engraved on disks (Lab-On-A-Disk, LOAD). However, their monolithic design limits flexibility and demands microfluidic expertise, thereby increasing prototyping time and costs, while discouraging broader adoption. To address these limitations, we introduce modular microfluidic chips that are integrable and functional on both LOAD platforms and commercial centrifuges, enabling broad laboratory use without additional equipment. These interchangeable modules perform specific functions─dispensing, metering, mixing, pooling, and collection─without requiring extra components for leak-proof interconnection. Their detachability from the rotating support allows fluid control through "flipping" relative to the centrifugal force. Additionally, they are compatible with multiwell plates and stackable in swinging-bucket centrifuges, enabling high-throughput sample preparation. As a proof of concept, an enzymatic assay was performed by using several assemblies of modules in parallel. After the reagents were mixed and transferred into a well plate, absorbance was measured at three antibiotic concentrations, confirming accurate volume control and reproducible measurements. This modular approach enhances miniaturization, compatibility, and affordability while reducing the reliance on expensive and bulky robotic systems. By simplifying workflows and improving flexibility, this provides an efficient alternative for rapid and scalable sample preparation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".