Rapid fabrication of a microfluidic device with integrated optical waveguides for DNA fragment analysis
Bibliographic record
Abstract
The fabrication and performance of a microfluidic device with integrated liquid-core optical waveguides for laser induced fluorescence DNA fragment analysis is presented. The device was fabricated through poly(dimethylsiloxane) (PDMS) soft lithography and waveguides are formed in dedicated channels through the addition of a liquid PDMS pre-polymer of higher refractive index. Once a master has been fabricated, microfluidic chips can be produced in less than 3 h without the requirement for a cleanroom, yet this method provides an optical system that has higher performance than a conventional confocal optical assembly. Optical coupling was achieved through the insertion of optical fibers into fiber-to-waveguide couplers at the edge of the chip and the liquid-fiber interface results in low reflection and scattering losses. Waveguide propagation losses are measured to be 1.8 dB cm(-1) (532 nm) and 1.0 dB cm(-1) (633 nm). The chip displays an average total coupling loss of 7.6 dB due to losses at the optical fiber interfaces. In the electrophoretic separation and detection of a BK virus PCR product, the waveguide system achieves an average signal-to-noise ratio of 570 +/- 30 whereas a commercial confocal benchtop electrophoresis system achieves an average SNR of 330 +/- 30. To our knowledge, this is the first time that a waveguide-based system has been demonstrated to have a SNR comparable to a commercially available confocal-based system for microchip capillary electrophoresis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".