Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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1.080 Topics available

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693.932 PEOPLE
693.932 People People

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Hughes, Robert R.

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University of Bristol

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (6/6 displayed)

  • 2023Modelling and evaluation of carbon fibre composite structures using high-frequency eddy current imaging24citations
  • 2023Modelling a Dynamic Magneto-Agglutination Bioassay1citations
  • 2023Modelling a Dynamic Magneto-Agglutination Bioassay1citations
  • 2021Exploring high-frequency eddy-current testing for sub-aperture defect characterisation using parametric-manifold mapping3citations
  • 2018Characterisation of carbon fibre-reinforced polymer composites through radon-transform analysis of complex eddy-current data48citations
  • 2016Investigating electrical resonance in eddy-current array probes5citations

Places of action

Chart of shared publication
Yi, Qiuji
1 / 1 shared
Wilcox, Pd
1 / 20 shared
Kiely, Janice
2 / 5 shared
Luxton, Richard W.
2 / 2 shared
Fishman, Aaron S.
2 / 2 shared
Lamb-Riddell, Kathryn
2 / 3 shared
Sleigh Muñoz, Valentina
1 / 2 shared
Champneys, Alan R.
1 / 1 shared
Muñoz, Valentina Sleigh
1 / 1 shared
Champneys, Alan
1 / 4 shared
Drinkwater, Bw
2 / 25 shared
Smith, Robert A.
1 / 19 shared
Dixon, S.
1 / 5 shared
Fan, Y.
1 / 10 shared
Chart of publication period
2023
2021
2018
2016

Co-Authors (by relevance)

  • Yi, Qiuji
  • Wilcox, Pd
  • Kiely, Janice
  • Luxton, Richard W.
  • Fishman, Aaron S.
  • Lamb-Riddell, Kathryn
  • Sleigh Muñoz, Valentina
  • Champneys, Alan R.
  • Muñoz, Valentina Sleigh
  • Champneys, Alan
  • Drinkwater, Bw
  • Smith, Robert A.
  • Dixon, S.
  • Fan, Y.
OrganizationsLocationPeople

article

Modelling a Dynamic Magneto-Agglutination Bioassay

  • Kiely, Janice
  • Muñoz, Valentina Sleigh
  • Luxton, Richard W.
  • Hughes, Robert R.
  • Fishman, Aaron S.
  • Lamb-Riddell, Kathryn
  • Champneys, Alan
Abstract

The process of developing an end-to-end model of a magneto-immunoassay is described, simulating the agglutination effect due to the specific binding of bacteria to paramagnetic particles. After establishing the properties of the dose-specific agglutination through direct imaging, a microfluidic assay was used to demonstrate changes in the magnetophoretic transport dynamics of agglutinated clusters via transient inductive magentometer measurements. End-to-end mathematical modelling is used to establish the physical processes underlying the assay. First, a modification of the Becker--Döring nucleation kinetic equations is used to establish a relationship between analyte dose and average cluster size. Next, Stokes flow equations are used to establish a relationship between cluster size and speed of motion within the fluid chamber. This predicts a cluster-size dynamic profile of concentration of PMPs versus time when the magnetic field is switched between the two actuated magnets. Finally, inductive modelling is carried out to predict the response of the magnetometer circuit in response to the dynamics of magnetic clusters. The predictions of this model are shown to agree well with the results of experiments, and to predict the shape of the dose-response curve.

Topics
  • impedance spectroscopy
  • cluster
  • experiment