Nano-Integrated Suspended Polymeric Microfluidics (SPMF) Platform for Ultra-Sensitive Bio-Molecular Recognition of Bovine Growth Hormones
Bibliographic record
Abstract
The development of sensitive platforms for the detection of biomolecules recognition is an extremely important problem in clinical diagnostics. In microcantilever (MC) transducers, surface-stress is induced upon bimolecular interaction which is translated into MC deflection. This paper presents a cost-effective and ultra-sensitive MC-based biosensing platform. To address these goals, the need for costly high-resolution read-out system has been eliminated by reducing the cantilever compliance through developing a polymer-based cantilever. Furthermore a microfluidic system has been integrated with the MC in order to enhance sensitivity and response time and to reduce analytes consumption. Gold nanoparticles (AuNPs) are synthesized on the surface of suspended microfluidics as the selective layer for biomolecule immobilization. The biosensing results show significant improvement in the sensitivity of the proposed platform compared with available silicon MC biosensor. A detection limit of 2 ng/ml (100pM) is obtained for the detection of bovine growth hormones. The results validated successful application of suspended polymeric microfluidics (SPMF) as the next generation of biosensing platforms which could enable femtomolar (fM) biomolecular recognition detection.
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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 | 0.000 | 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".