Integrated biophotonic μTAS for flow cytometry and particle detection
Bibliographic record
Abstract
Recent advancements in the integration of photonic technologies with microfluidics for Micro-Total Analysis Systems (μTAS) have paved way for the realization of a lot of potential applications in the field of biosensing and biomedical detections. Some of the prominent features of these integrated μTAS are improved performance, high sensitivity and signal-to-noise ratio, reduced consumption of samples and reagents, and portability, among others. In this work, a hybrid integrated biophotonic μTAS on silicon-polymer platform is presented. Herein, the optical fibers are directly integrated with the Silicon microfluidic chip and an Echelle grating based Spectrometer-on-Chip on Silica-on-Silicon (SOS) is integrated with the opto-microfluidic assembly. Flow actuation within the system is enabled by a mechanical Piezodriven Valveless Micropump (PVM). Finite Element Analysis (FEA) has been carried out in order to study the behavior of the fluid flow within the microfluidic channels due to the piezo actuation, and the geometry of the bio-detection chamber within the microfluidic system has been optimized accordingly in order to obtain no-stagnation flow conditions. The opto-microfluidic performance and the piezo-actuated valveless micropump were characterized in separate experiments. The integrated μTAS was tested for flow cytometry and particle detection using laser induced fluorescence. The experimental results show that the system is suitable for high throughput biodetections.
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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".