Integrated wavelength-selective optical waveguides for microfluidic-based laser-induced fluorescence detection
Bibliographic record
Abstract
We demonstrate the fabrication and characterization of a novel, inexpensive microchip capable of laser induced fluorescence (LIF) detection using integrated waveguides with built-in optical filters. Integrated wavelength-selective optical waveguides are fabricated by doping poly(dimethysiloxane) (PDMS) with dye molecules. Liquid-core waveguides are created within dye-doped PDMS microfluidic chips by filling channels with high refractive index liquids. Dye molecules are allowed to diffuse into the liquid core from the surrounding dye-doped PDMS. The amount of diffusion is controlled by choosing either polar (low diffusion) or apolar (high diffusion) liquid waveguide cores. The doping dye is chosen to absorb excitation light and to transmit fluorescence emitted by the sample under test. After 24 h, apolar waveguides demonstrate propagation losses of 120 dB cm(-1) (532 nm) and 4.4 dB cm(-1) (633 nm) while polar waveguides experience losses of 8.2 dB cm(-1) (532 nm) and 1.1 dB cm(-1) (633 nm) where 532 and 633 nm light represent the excitation and fluorescence wavelengths, respectively. We demonstrate the separation and detection of end-labelled DNA fragments using polar waveguides for excitation light delivery and apolar waveguides for fluorescence collection. We demonstrate that the dye-doped waveguides can provide performance comparable to a commercial dielectric filter; however, for the present choice of dye, their ultimate performance is limited by autofluorescence from the dye. Through the detection of a BK virus polymerase chain reaction (PCR) product, we demonstrate that the dye-doped PDMS system is an order of magnitude more sensitive than a similar undoped system (SNR: 138 vs. 9) without the use of any external optical filters at the detector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".