Patterning Multiplex Protein Microarrays in a Single Microfluidic Channel
Bibliographic record
Abstract
The development of versatile biofunctional surfaces is a fundamental prerequisite in designing Lab on a Chip (LOC) devices for applications in biosensing interfaces and microbioreactors. The current paper presents a rapid combinatorial approach to create multiplex protein patterns in a single microfluidic channel. This approach consists of coupling microcontact printing with microfluidic patterning, where microcontact printing is employed for silanization using (3-Aminopropyl) triethoxysilane (APTES), followed by microfluidic patterning of multiple antibodies. As a result, the biomolecules of choice could be covalently attached to the microchannel surface, thus creating a durable and highly resistant functional interface. Moreover, the experimental procedure was designed to create a microfluidic platform that maintains functionality at high flow rates. The functionalized surfaces were characterized using X-ray photoelectron spectroscopy (XPS) and monitored with fluorescence microscopy at each step of functionalization. To illustrate the possibility of patterning multiple biomolecules along the cross section of a single microfluidic channel, microarrays of five different primary antibodies were patterned onto a single channel and their functionality was evaluated accordingly through a multiplex immunoassay using secondary antibodies specific to each patterned primary antibody. The resulting patterns remained stable at shear stresses of up to 50 dyn/cm(2). The overall findings suggest that the developed multiplex functional interface on a single channel can successfully lead to highly resistant multiplex functional surfaces for high throughput biological assays.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".