Integrated micro-total analysis system (μTAS) for biophotonic enzymatic detections
Bibliographic record
Abstract
Lab-on-a-chip or Micro total analysis systems (μTAS) technologies offer a lot of potential applications for biosensing and biomedical detections. This paper presents the design, fabrication and characterization of a fully integrated siliconpolymer based biophotonic Micro-Total Analysis System for the real-time detection of enzymes and antigens. This device uses optical detection methods i.e, optical absorption, Laser induced fluorescence and evanescence measurement technique to detect the presence, concentration and the activity of biomolecules. The main components of the proposed system are microfluidic unit and micromechanical fluid actuation system, integrated with the optical detection systems. An Echelle grating based Spectrometer-on-Chip on Silica-on-Silicon (SOS) is integrated with the opto-microfluidic assembly for fluorescence detection. On-Chip fabrication and integration of valveless micropump has been carried out in order to facilitate the transportation of fluid within the system. The important advantages of the proposed μTAS are functional independence of each module of the system, simultaneous multi-analyte detection, rapid, precise and discriminating results, low background/high signal-to-noise ratio, lack of moving parts, robust, portability, and feasibility of bulk fabrication.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".