ArticleAnalytical chemistry2012
Modeling analyte transport and capture in porous bead sensors.
Article in Analytical chemistry, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- Sensors that Learn: The Evolution from Taste Fingerprints to Patterns of Early Disease Detection.Micromachines · 2019Review
- Investigation of Reagent Delivery Formats in a Multivalent Malaria Sandwich Immunoassay and Implications for Assay Performance.Analytical chemistry · 2016Article
- Programmable bio-nano-chip system: a flexible point-of-care platform for bioscience and clinical measurements.Lab on a chip · 2015Article
- Hot embossed polyethylene through-hole chips for bead-based microfluidic devices.Biosensors & bioelectronics · 2013Article
- Effects of sample delivery on analyte capture in porous bead sensors.Lab on a chip · 2012Article
- Porous bead-based diagnostic platforms: bridging the gaps in healthcare.Sensors (Basel, Switzerland) · 2012Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Porous agarose microbeads, with high surface to volume ratios and high binding densities, are attracting attention as highly sensitive, affordable sensor elements for a variety of high performance bioassays. While such polymer microspheres have been extensively studied and reported on previously and are now moving into real-world clinical practice, very little work has been completed to date to model the convection, diffusion, and binding kinetics of soluble reagents captured within such fibrous networks. Here, we report the development of a three-dimensional computational model and provide the initial evidence for its agreement with experimental outcomes derived from the capture and detection of representative protein and genetic biomolecules in 290 μm porous beads. We compare this model to antibody-mediated capture of C-reactive protein and bovine serum albumin, along with hybridization of oligonucleotide sequences to DNA probes. These results suggest that, due to the porous interior of the agarose bead, internal analyte transport is both diffusion and convection based, and regardless of the nature of analyte, the bead interiors reveal an interesting trickle of convection-driven internal flow. On the basis of this model, the internal to external flow rate ratio is found to be in the range of 1:170 to 1:3100 for beads with agarose concentration ranging from 0.5% to 8% for the sensor ensembles here studied. Further, both model and experimental evidence suggest that binding kinetics strongly affect analyte distribution of captured reagents within the beads. These findings reveal that high association constants create a steep moving boundary in which unbound analytes are held back at the periphery of the bead sensor. Low association constants create a more shallow moving boundary in which unbound analytes diffuse further into the bead before binding. These models agree with experimental evidence and thus serve as a new tool set for the study of bioagent transport processes within a new class of medical microdevices.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.