Hybrid Integrated Silicon Microfluidic Platform for Fluorescence Based Biodetection
Bibliographic record
Abstract
The desideratum to develop a fully integrated Lab-on-a-chip device capable ofrapid specimen detection for high throughput in-situ biomedical diagnoses and Point-of-Care testing applications has called for the integration of some of the novel technologiessuch as the microfluidics, microphotonics, immunoproteomics and Micro ElectroMechanical Systems (MEMS). In the present work, a silicon based microfluidic device hasbeen developed for carrying out fluorescence based immunoassay. By hybrid attachment ofthe microfluidic device with a Spectrometer-on-chip, the feasibility of synthesizing anintegrated Lab-on-a-chip type device for fluorescence based biosensing has beendemonstrated. Biodetection using the microfluidic device has been carried out usingantigen sheep IgG and Alexafluor-647 tagged antibody particles and the experimentalresults prove that silicon is a compatible material for the present application given thevarious advantages it offers such as cost-effectiveness, ease of bulk microfabrication,superior surface affinity to biomolecules, ease of disposability of the device etc., and is thussuitable for fabricating Lab-on-a-chip type devices.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".