Superparamagnetic Nanoparticle−Polystyrene Bead Conjugates as Pathogen Capture Mimics: A Parametric Study of Factors Affecting Capture Efficiency and Specificity
Bibliographic record
Abstract
There is currently significant interest in the miniaturization of disease detection platforms. As detection platforms decrease in size there is a need for the development of sample preparation protocols by which cells or biomarkers of interest can be concentrated from large volumes down to volumes more amenable to analysis within microfluidic devices. To address this issue, we present a series of magnetic confinement assays for polystyrene (PS) beads mediated through their covalent modification with a series of superparamagnetic nanoparticles, where the PS beads have many properties similar to bacteria, but are not pathogenic. The magnetic confinement of the PS beads is investigated as a function of (1) the overall nanoparticle size, (2) the loading of superparamagnetic content within the nanoparticle matrix, and (3) the viscosity and volume of the dispersion medium. We demonstrate that the time required for the magnetic capture of the PS beads by the superparamagnetic nanoparticles (1) decreases as the loading of superparamagnetic material into the nanoparticles increases and (2) increases as the viscosity and volume of the dispersion medium are increased. However, limitations in the magnetic confinement efficiency for the PS beads labeled with nanoparticles comprised of low loadings of superparamagnetic material can be overcome through the use of magnetic columns. These magnetic columns provide a practical and fast mode of sample preparation that should facilitate the magnetic concentration of cells and biomarkers from large volumes to volumes more amenable to incorporation into a microfluidic-based analysis system, where they can be analyzed/detected.
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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".