On-Chip Transduction of Nucleic Acid Hybridization Using Spatial Profiles of Immobilized Quantum Dots and Fluorescence Resonance Energy Transfer
Bibliographic record
Abstract
The glass surface of a glass-polydimethylsiloxane (PDMS) microfluidic channel was modified to develop a solid-phase assay for quantitative determination of nucleic acids. Electroosmotic flow (EOF) within channels was used to deliver and immobilize semiconductor quantum dots (QDs), and electrophoresis was used to decorate the QDs with oligonucleotide probe sequences. These processes took only minutes to complete. The QDs served as energy donors in fluorescence resonance energy transfer (FRET) for transduction of nucleic acid hybridization. Electrokinetic injection of fluorescent dye (Cy3) labeled oligonucleotide target into a microfluidic channel and subsequent hybridization (within minutes) provided the proximity for FRET, with emission from Cy3 being the analytical signal. The quantification of target concentration was achieved by measurement of the spatial length of coverage by target along a channel. Detection of femtomole quantities of target was possible with a dynamic range spanning an order of magnitude. The assay provided excellent resistance to nonspecific interactions of DNA. Further selectivity of the assay was achieved using 20% formamide, which allowed discrimination between a fully complementary target and a 3 base pair mismatch target at a contrast ratio of 4:1.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".