Integración del micro(bio)sensores químicos en el desarrollo de lenguas electrónicas

  • del Valle, Manel (Principal Investigator)
  • Bonanni , Alessandra (Scholar)
  • Gutiérrez Capitán, Manuel (Scholar)
  • Cespedes Mulero, Francisco (Researcher on contract)
  • Sánchez Ordóñez, Samuel (Investigator)
  • Cartas Rosado, Raul (Scholar)

Project Details


The present project has as first goal the integration of electrochemical biosensors and optical systems of detection using microelectronics technology and lithographic technology on the basis of PDMS (poly dimethylsiloxane). These sensor systems will be included in microfluidics structures with the purpose of obtaining advanced microsystems for automatic and reproducible analysis of food samples. The electrochemical (bio)sensors will be potentiometric (ISFETs) and voltamperometric (amperometric and UMEAs) thus to extend the range of analytes. The optical detectors, configured as hollow prisms, will be defined by means of soft-lithography techniques using PDMS and will allow to detect absorbance and fluorescence. The incorporation of carbon nanoubes (NCT) activated with specific functional groups and implemented in the microsensors and optical structures will allow the improvement of the sensorial properties of these (basically a greater sensitivity) and the development of biosensors of high specificity. The development of photonic crystals (PC) based on NCT will be evaluated with the objective to increase the optical properties and the response of the optical sensors. The studied sensor systems will be used along with signal processing computer tools for the development of what it is call electronic tongues (ET). Those systems allow simple and direct measurement applications, both for the identification and classification of varieties (such as the taster panel), or for the quantification of chemical species. The chemometric tools applied with them will be chosen according to the proposed objective. Principal Component Analysis (PCA) will be applied for initial identification and classification, Linear Discriminant Analysis (LDA) for the recognition of the patterns shown by samples and Artificial Neural Networks (ANN) for qualitative and quantitative applications (...)
Effective start/end date1/10/0730/09/10

Collaborative partners

  • The Spanish National Research Council (CSIC)
  • Universitat Rovira i Virgili (URV)
  • Universitat Autònoma de Barcelona (UAB) (lead)


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