Development of a Multichannel Microfluidic Analysis System Employing Affinity Capillary Electrophoresis for Immunoassay
Bibliographic record
Abstract
A six-channel microfluidic immunoassay device with a scanned fluorescence detection system is described. Six independent mixing, reaction, and separation manifolds are integrated within one microfluidic wafer, along with two optical alignment channels. The manifolds are operated simultaneously and data are acquired using a singlepoint fluorescence detector with a galvano-scanner to step between separation channels. A detection limit of 30 pM was obtained for fluorescein with the scanning detector, using a 7.1-Hz sampling rate for each of the reaction manifolds and alignment channels (57-Hz overall sampling rate). Simultaneous direct immunoassays for ovalbumin and for anti-estradiol were performed within the microfluidic device. Mixing, reaction, and separation could be performed within 60 s in all cases and within 30 s under optimized conditions. Simultaneous calibration and analysis could be performed with calibrant in several manifolds and sample in the other manifolds, allowing a complete immunoassay to be run within 30 s. Careful chip conditioning with methanol, water, and 0.1 M NaOH resulted in peak height RSD values of 3-8% (N = 5 or 6), allowing for cross-channel calibration. The limit of detection (LOD) for an anti-estradial assay obtained in any single channel was 4.3 nM. The LOD for the cross-channel calibration was 6.4 nM. Factors influencing chip and detection system design and performance are discussed in detail.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".