Impedance spectroscopy of composite polymeric electrolytes - from experiment to computer modeling.

1. Impedance spectroscopy of composite polymeric electrolytes - from experiment to computer modeling. Maciej Siekierski Warsaw University of Technology, Faculty of Chemistry, ul. Noakowskiego 3, 00-664 Warsaw, POLAND e-mail: alex@soliton.ch.pw.edu.pl, tel (+) 48 601 26 26 00, fax (+) 48 22 628 27 41

2. t R Model of the composite polymeric electrolyte • Sample consists of three different phases: • Original polymeric electrolyte – matrix • Grains • Amorphous grain shells Last two form so called composite grain characterized with the t/R ratio

3. Experimental determination of the material parameters: • The studied system is complicated and its properties vary with both • composition and temperature changes. These are mainly: • Contents of particular phases • Conductivity of particular phases • Ion associations • Ion transference number • d.c. conductivity value • diffusion process study • transport properties of the electrolyte-electrode border area • determination of a transference number of a charge carriers. • Variable experimental techniques are applied to composite polymeric electrolytes: • Molecular spectroscopy (FT-IR, Raman) • Thermal analysis • Scanning electron microscopy and XPS • NMR studies • Impedance spectroscopy

4. Rb Cdl Z” w Z’ Impedance spectrum of the composite electrolyte Equivalent circuit of the composite polymeric electrolyte measured in blocking electrodes system consists of: • Bulk resistivity of the material Rb • Geometric capacitance Cg • Double layer capacitance Cdl Cg

5. Activation energy analysis For most of the semicrystalline systems studied the Arrhenius type of temperature conductivity dependence is observed: σ(T) = n(T)μ(T)ez = σ0exp(–Ea/kT) • Where Ea is the activation energy of the conductivity process. • The changes of the conductivity value are related to the charge carriers: • mobility changes • concentration changes • Finally, the overall activation energy (Ea) can be divided into: • activation energy of the charge carriers mobility changes (Em) • activation energy of the charge carriers concentration changes (Ec) Ea = Em + Ec These two values can give us some information, which of two above mentioned processes is limiting for the conductivity.

6. Almond – West Formalism • The application of Almond-West formalism to composite polymeric electrolyte • is realized in the following steps: • application of Jonsher’s universal power law of dielectric response σ(ω) = σDC + Aωn • calculation of wp for different temperatures ωp = (σDC/A)(1/n) • calculation of activation energy of migration from Arrhenius type equation wp = ωe exp (-Em/kT) • calculation of effective charge carriers concentration K = σDCT/ωp • calculation of activation energy of charge carrier creation Ec = Ea - Em

7. Modeling of the conductivity in composites • Ab initio quantum mechanics • Semi empirical quantum mechanics • Molecular mechanics / molecular dynamics • Effective medium approach • Random resistor network approach • System is represented by three dimensional network • Each node of the network is related to an element • with a single impedance value • Each phase present in the system has its characteristic • impedance values • Each impedance is defined as a parallel RCPE connection

8. Grain Shell 1 Shell 2 Matrix Model creation, stages 1,2 • Grains are located randomly in the matrix • Shells are added on the grains surface • Sample is divided into single uniform cells

9. Model creation, stage 3 • The basic element of the model is the node • where six impedance branches are connected • The impedance elements of the branches are • serially connected to the neighbouring ones • For each node the potential difference towards • one of the sample edges (electrodes) is defined

10. Model creation, stage 4 Finally, the three dimensional impedance network is created as a sample numerical representation

11. U2 U3 Z2 Z3 U Zl Ul U4 Z6 Z4 Z5 U6 U5 Model creation, stage 5 • Path approach: Sample is scanned for continuous percolation paths coming form one edge (electrode) to the opposite. Number of paths found gives us information about the sample conductivity. • Current approach: Current coming through each node is calculated. Model is fitted by iteration algorithm. The iteration progress is related with the number of nodes achieving current equilibrium. Ii = (Ui - U) / Ri Σ Ii = Σ [(Ui - U)/ Ri] = 0

12. Model creation, stage 6 • In each iteration step the voltage value of each node is changed as a function of voltage values of neighbouring nodes. • The quality of the iteration can be tested by either the percent of the nodes which are in the equilibrium stage or by the analysis of current differences for node in the following iterations. • The current differences seem to be better test parameters in comparison with the nodes count. • When the equilibrium state is achieved the current flow between the layers (equal to the total sample current) can be easily calculated. • Knowing the test voltage put on the sample edges one can easily calculate the impedance of the sample according to the Ohm’s law.

13. An example of the iteration progress

14. Changes of node current during iteration

15. Current flow around the single grain • Vertical cross-section • Horizontal cross-section

16. Some more nice pictures • Voltage distribution around the single grain – vertical cross-section • Current flow in randomly generated sample with 20 % v/v of grains – vertical cross-section

17. t R Path approach results Results of the path oriented approach calculations for samples containing grains of 8 units diameter, different t/R values and with different amounts of additive

18. t R Path approach results Results of the path oriented approach calculations for samples containing grains of different diameters, t/R=1.0 and with different amounts of additive

19. % v/v Current approach results The dependence of the sample conductivity on the filler grain size and the filler amount for constant shell thickness equal to 3 mm

20. % v/v Current approach results The dependence of the sample conductivity on the shell thickness and filler amount for the constant filler grain size equal to 5 mm

21. Conclusions • Random Resistor Network Approach is a valuable tool for computer simulation of conductivity in composite polymeric electrolytes. • Both approaches (current-oriented and path-oriented) give consistent results. • Proposed model gives results which are in good agreement with both experimental data and Effective Medium Theory Approach. • Appearing simulation errors come mainly from discretisation limits and can be easily reduced by increasing of the test matrix size. • Model which was created for the bulk conductivity studies can be easily extended by the addition of the elements related to the surface effects and double layer existence. • Various functions describing the space distribution of conductivity within the highly conductive shell can be introduced into the software. • The model can be also extended by the addition of time dependent matrix property changes to simulate the aging of the material or passive layer growth.

22. Acknowledgements Author would like to thank all his colleagues fromthe Solid State Technology Division. Professor Władysław Wieczorek was the person who introduced me into the composite polymeric electrolytes field andis the co-originator of the application of the Almond-West Formalism to the polymeric materials. My students: Piotr Rzeszotarski Katarzyna Nadara realized in practice my ideas on RandomResistor Network Approach.