Projects

From Intelligent Materials and Systems Lab

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Molecular dynamics studies of poly(ethylene oxide) based electrolytes
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Solid electrolytes for the Lithium-Ion Polymer Battery can be produced by mixing a lithium salt, typically LiPF6 or LiBF4, into poly(ethylene oxide) (PEO), -(CH2CH2O)n-. However, such electrolytes only exhibit adequate ionic conductivity (>10-4 S/cm) at temperatures above 70°C, where the polymer becomes amorphous. The conventional wisdom has been that the high degree of local order (“crystallinity”) is the reason for the poor ionic conductivity at ambient temperatures. Much attention has therefore been devoted to the task of increasing the amorphous content of the PEO electrolyte at ambient temperatures.

Computer-Aided Materials Design for Proton-Conducting Fluoropolymers

Molecular Dynamics (MD) simulations give us a chance to have close insights into Nafion's dynamics and local structure on molecular level. We can study the details of proton-conductivity and find out the mechanisms what can possibly improve this process. We are interested in following research problems:

  • design of the realistic and reliable Molecular Dynamics simulation model for Nafion as an electrolyte in the fuel cell,
  • simulations of proton hopping mechanism between Nafion chain and surrounding water,
  • the effects of Nafion side chains on the proton dynamics.

Ionic Polymer Metal Composites (IPMC) modelling and control

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This research focuses on building EAP devices as well as methods for their control. Particularly, we are focusing at the following research problems:

  • Electromechanical modelling of IPMC materials
  • Design of novel IPMC actuators.
  • Development of position sensors for IPMC actuators.
  • Development of control methods of IPMC actuators to achieve less energy consumption at large output force and torque.
  • Development of feedback control methods for IPMC actuators.
  • Design of autonomous IPMC actuators and devices.

Robot Learning

Learning and adaptation are inherent capabilities in dynamic and partially unknown environments. Properties of such kind of environments are not known in advance and therefore it is not possible to model the correctly. It is also likely that information about the environment contains noise and ambiguity.