Microfluidic ELISA on non-passivated PDMS chip using magnetic bead transfer inside dual networks of channels
Bibliographic record
Abstract
Achieving efficient passivation of micro-channels against non-specific adsorption of biomolecules is a critical aspect in the development of microfluidic ELISA systems. Usual surface treatments such as pre-coating of the channels with serum albumin, exposure to oxygen plasma, polyethylene glycol grafting however exhibit a lack of long-term stability, with procedures that can be time-consuming, complex or associated with costly materials and instruments. In this paper, we present a new fluidic design combined with an original strategy of manipulating magnetic beads in order to reduce assay noise in bead-based microfluidic ELISA without the need for prior channel pre-treatment. The novelty of the system relies on the physical separation of the immune complex formation phase and the enzymatic reaction phase into two independent networks of channels. These networks are linked by fluidic bridges, whose openings are controlled by pressure valves, and through which the beads are magnetically transferred. A standard curve for the quantification of a model antibody was obtained within 30 minutes. A detection limit of 100 pg mL(-1) (660 fM) and good linearity of the signal up to 4 ng mL(-1) were observed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".