Finite element modeling of intermittent flow molecular sorting system for optical microfluidics-based biodetection
Bibliographic record
Abstract
The advent of microfluidics has provided a tremendous boost to the field of health care for the development of practical in-situ medical diagnoses and Point-of-Care (POC) testing methods. Optical microfluidics offers a lot of scope for carrying out successful biodetections through different target detection techniques such as optical absorption, fluorescence, etc. Two main issues in carrying out successful biodetection on microfluidic platform are the problem of biomolecule immobilization onto the surface of the microfluidic channel and efficient mixing of the bio-fluids necessary to achieve proper bio-interaction. In most cases, the biodetection involves two or more biological specimens, such as enzyme-substrate, antigen-antibody, protein-protein etc., and therefore, it is necessary to discover a solution which addresses to the needs of both immobilization and multi-molecular interactions. In this work, a novel technique of flow controlled molecular sorting is presented, wherein, by appropriate design of the microfluidic channel and by careful control of fluid flow in the system, optimal interaction of the specimens can be achieved through biomolecular sorting, thereby overcoming the problem of bio-immobilization onto the surface of the microfluidic channel. Herein, Finite Element Modeling (FEM) of flow behavior within the microfluidic channel has been carried out for different channel geometries, which is essential for the appropriate choice of microfluidic system for the present application. The technique of implementing the immobilization-free multi molecular bio-interactions in the proposed microfluidic system is explained and the feasibility of carrying out optical microfluidics based biodetection is demonstrated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".