Enhanced fluorescence‐based bio‐detection through selective integration of reflectors in microfluidic lab‐on‐a‐chip
Bibliographic record
Abstract
Purpose This paper proposes to examine a simple and cost‐effective method of integrating a reflector surface with a silicon‐based microfluidic channel for enhanced biosensing through the method of fluorescence in a microfluidics and nanofluidics‐based lab‐on‐a‐chip device. Design/methodology/approach Herein, the reflector is integrated with silicon‐based microfluidic channels and fluorescence measurements were carried out using alexafluor 647 particles. Two types of microfluidic channel surfaces were used, with and without reflector integration, for the experiments. Findings The experimental results prove that the proposed technique of partial reflector integration within microfluidic or nanofluidic channel surfaces is highly suitable for fluorescence‐based detection of single molecules and low concentration fluorophore‐tagged receptors. Originality/value It is believed that this is a novel work of integrating a reflector with a microfluidic channel surface for fluorescence‐based biodetection. This method will be very useful for fluorescence‐based biosensors in detecting low concentration fluorophores and single molecules.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".