PMMA Microfluidics Devices Fabrications and its Application in Electrophoresis
Bibliographic record
Abstract
It is believed that Lab-on-chip microfluidics technology is the key to powerful new diagnostic instruments. These microfluidics devices are conventionally made from glass and silicon. However, polymers such as Polymethylmethacrylate (PMMA) have several advantages such as much lower fabrication costs and complexity. In addition to the lower cost of the raw material these plastic substrates can be patterned using a wide variety of methods, including laser ablation, hot embossing, reactive ion etching and deep UV lithography. Therefore, there is a great demand to develop plastic diagnostics devices that would allow early diagnosis of disease by using easy to collect body fluids. This project deals with characterising the electrophoretic protein separation on laser machined PMMA (Plexiglas) microfluidic channels. This microfluidic unit will serve as a foundation for the protein analysis. Detection of proteins in microfluidic systems will be done by using electrophoretic separation process with fluorescent detection. To achieve this separation, a strong electric field has to be applied using a high voltage power supply. We plan to use the commercially available disposable camera flash units as the supply of this high voltage instead of voltage amplifier systems. In this presentation, we would like to talk about the pinch injection of fluorescent electrolytes into a PMMA microfluidic unit and how it will be used for protein separation.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".