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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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2007EIS multianalyte sensing with an automated SIA system-An electronic tongue employing the impedimetric signal41citations

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Alonso-Lomillo, M. Asunción
1 / 1 shared
Cortina-Puig, Montserrat
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Del Valle, Manel
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Berbel, Xavier Munoz
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2007

Co-Authors (by relevance)

  • Alonso-Lomillo, M. Asunción
  • Cortina-Puig, Montserrat
  • Del Valle, Manel
  • Berbel, Xavier Munoz
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article

EIS multianalyte sensing with an automated SIA system-An electronic tongue employing the impedimetric signal

  • Alonso-Lomillo, M. Asunción
  • Cortina-Puig, Montserrat
  • Muñoz-Pascual, Francisco J.
  • Del Valle, Manel
  • Berbel, Xavier Munoz
Abstract

In this work, the simultaneous quantification of three alkaline ions (potassium, sodium and ammonium) from a single impedance spectrum is presented. For this purpose, a generic ionophore - dibenzo-18-crown-6 - was used as a recognition element, entrapped into a polymeric matrix of polypyrrole generated by electropolymerization. Electrochemical impedance spectroscopy (EIS) and artificial neural networks (ANNs) were employed to obtain and process the data, respectively. In fact, EIS detected the ions exchanged between the medium and the sensing layer whereas ANNs, after an appropriated training process, could turn the impedance spectrum into concentrations values. A sequential injection analysis (SIA) system was employed for operation and to automatically generate the information required for the training of the ANN. Best results were obtained by using a backpropagation neural network made up by two hidden layers: the first one contained three neurons with the radbas transfer function and the second one ten neurons with the tansig transfer function. Three commercial fertilizers were tested employing the proposed methodology on account of the high complexity of their matrix. The experimental results were compared with reference methods. © 2006 Elsevier B.V. All rights reserved.

Topics
  • Sodium
  • Potassium
  • electrochemical-induced impedance spectroscopy