Automated Dynamic Inlet Microfluidics (ADIM) system: cost-effective biaxial nanoliter droplet on demand generation platform and its application in agglutination assays
Bibliographic record
Abstract
The paper demonstrates an adaptation of a Prusa Mini+ 3D printer through the integration of 3D printed modules, creating a system capable of producing varied droplets from multiple Eppendorf tubes. Building upon our previous model, this system enhances calibration methodology enabling any fused deposition modeling (FDM) printer to produce mono-disperse droplets (coefficient of variance (CV%) <2% for train of 100 droplets) with 6900 assays per hour rate. The cost of the developed system is 85% lower than that of existing droplet generation solutions on the market, and 30% more economical than the previous iteration of the system. Additionally, the system's utility in quantification of agglutination assays is highlighted using image analysis, capable of distinguishing between agglutinated and non-agglutinated samples. By offering significant savings and ease of use, this system aims to lower the barriers to entry for microfluidic research, potentially broadening the scope of scientific exploration and application in this field.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".