On pattern recognition dependency of desorption heat, activation energy, and temperature of polymer-based VOC sensors for the electronic NOSE

Guy Narcisse Tchoupo, Anthony Guiseppi-Elie

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Design of polymer-based, chemo-responsive chemical sensors for a given set of target vapors is a challenging task, as the detailed mechanism of the sensing phenomena is still not well understood. In this work, we propose a novel approach to select gas-sensing materials for a given set of volatile organic compounds (VOC) when the governing phenomenon that drives gas/vapor interaction is adsorption. We do so by varying the activation energy, EA; enthalpy of adsorption, Q; and temperature, T, that directly relate to gas-solid interaction followed by neural network pattern recognition to evaluate to what extent variations in adsorption energetics and operational temperature results in satisfactory or unsatisfactory adsorbate classification. The adsorption of four gases; toluene, benzene, heptane and butanol at six discrete polymer sensors was simulated by varying EA and Q for different gas-solid interaction at normal temperature and pressure (NTP) and by varying T for a fixed set of EA and Q conditions. Data sets thus generated were used to build a feed-forward neural network (NN) model to classify the vapors. Performance of the NN model was evaluated by its ability to correctly identify the various gases. Pattern recognition dependency upon variation of activation energy and desorption heat was evaluated using data generated with perturbed EA. Upper and lower bound variation of the activation energy were found in order to retain within acceptable classification accuracy. It has been found that to be able to remain a classification accuracy of at least 90%, the tolerated perturbation of the activation energy should not exceed, in absolute value, 3% of that used during the training step of the neural networks. Similarly, temperature shifts that are set outside the range -4% to +3% of the training temperature were found to degrade classification accuracy to below acceptable limits of 90%. These results should be taken into consideration when designing and fabricating chemoresistive polymer sensors for use in an e-natural olfactory sensor emulator (e-NOSE™) ANN system.

Original languageEnglish (US)
Pages (from-to)81-88
Number of pages8
JournalSensors and Actuators, B: Chemical
Issue number1
StatePublished - Sep 30 2005


  • Adsorption
  • Langmuir isotherm
  • Neural network
  • Pattern recognition
  • Sensors
  • VOC
  • bioMEMS
  • e-NOSE™

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Condensed Matter Physics
  • Surfaces, Coatings and Films
  • Metals and Alloys
  • Electrical and Electronic Engineering
  • Materials Chemistry


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