A New Rapid Microfluidic Detection Platform Utilizing Hydrogel‐Membrane under Cross‐Flow
Bibliographic record
Abstract
Abstract Hydrogel‐based biosensing, based on antigen–antibody binding, has been utilized for various biomedical applications such as cancer monitoring. Hydrogels offer highly sensitive detection with the prevention of nonspecific binding because of 3D porous structure and hydrophilicity. However, these hydrogel‐based biosensing platforms require a time scale of hours to complete immunoassays because binding events are diffusion‐limited, where target biomolecules must diffuse into and throughout the 3D porous network. Here, a new rapid microfluidic platform is introduced utilizing a cross‐flow induced advective‐transportation of targets into a hydrogel membrane with fluorescent reporting. This flow enhanced delivery of target analytes significantly reduces their detection time to under 15 min. This flow effect is also numerically investigated on the detection process. Both numerical and experimental results show an exponential decrease in the detection time. More importantly, the cross‐flow configuration in our platform provides an additional size‐based filtration feature that effectively selects against larger components in a blood sample, such as red blood cells, during the detection process. This addition, not seen in conventional biosensing platforms, eliminates the need for blood sample prefiltration.
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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) | 0.001 | 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 | 0.001 | 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".