Silica-on-silicon waveguide integrated polydimethylsiloxane lab-on-a-chip for quantum dot fluorescence bio-detection
Bibliographic record
Abstract
Integration of microfluidics and optical components is an essential requirement for the realization of optical detection in lab-on-a-chip (LOC). In this work, a novel hybrid integration of silica-on-silicon (SOS) waveguide and polydymethylsiloxane (PDMS) microfluidics for realizing optical detection based biochip is demonstrated. SOS is a commonly used platform for integrated photonic circuits due to its lower absorption coefficient of silica and the availability of advanced microfabrication technologies for fabricating complicated optical components. However, the fabrication of complex microfluidics circuits on SOS is an expensive process. On the other hand, any complex 3D and high-aspect-ratio microstructures for the microfluidic applications can be easily patterned on PDMS using soft lithography. By exploring the advantages of these two materials, the proposed hybrid integration method greatly simplifies the fabrication of optical LOC. Two simple technologies--namely, diamond machining and soft lithography--were employed for the integration of an optical microfluidic system. Use of PDMS for the fabrication of any complex 3D microfluidics structures, together with the integration of low loss silica-on-silicon photonic waveguides with a straight microfluidic channel, opens up new possibilities to produce low-cost biochips. The performance of SOS-PDMS-integrated hybrid biochip is demonstrated with the detection of laser induced fluorescence of quantum dots. As quantum dots have immense application potential for biodetection, they are used for the demonstration of biodetection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".