M. H. Khanmirzaei

Hydroxypropyl Cellulose Based Non-Volatile Gel Polymer Electrolytes for Dye-Sensitized Solar Cell Applications using 1-methyl-3-propylimidazolium iodide ionic liquid

Gel polymer electrolytes using imidazolium based ionic liquids have attracted much attention in dye-sensitized solar cell applications. Hydroxypropyl cellulose (HPC), sodium iodide (NaI), 1-methyl-3-propylimidazolium iodide (MPII) as ionic liquid (IL), ethylene carbonate (EC) and propylene carbonate (PC) are used for preparation of non-volatile gel polymer electrolyte (GPE) system (HPC:EC:PC:NaI:MPII) for dye-sensitized solar cell (DSSC) applications. The highest ionic conductivity of 7.37 × 10−3 S cm−1 is achieved after introducing 100% of MPII with respect to the weight of HPC. Temperature-dependent ionic conductivity of gel polymer electrolytes is studied in this work. XRD patterns of gel polymer electrolytes are studied to confirm complexation between HPC polymer, NaI and MPII. Thermal behavior of the GPEs is studied using simultaneous thermal analyzer (STA) and differential scanning calorimetry (DSC). DSSCs are fabricated using gel polymer electrolytes and J-V centeracteristics of fabricated dye sensitized solar cells were analyzed. The gel polymer electrolyte with 100 wt.% of MPII ionic liquid shows the best performance and energy conversion efficiency of 5.79%, with short-circuit current density, open-circuit voltage and fill factor of 13.73 mA cm−2, 610 mV and 69.1%, respectively.

Studies on biodegradable polymer electrolyte rice starch (RS) complexed with lithium iodide

Biodegradable and natural rice starch (RS) polymer with lithium iodide salt (LiI) was used to prepare polymer electrolytes using solution cast technique. Polymer electrolyte films were characterized by thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), X-ray diffraction (XRD), and scanning electron microscopy (SEM). TGA and DSC thermograms demonstrate that decomposition temperature (Tdc) and glass transition temperature (Tg) for rice starch shift upon complexation with lithium iodide salt. Thermolysis studies using TGA show decomposition temperature decreases with the addition of lithium iodide salt. XRD patterns show increase in amorphous behavior with doping of lithium iodide salt. The morphology studies were observed using SEM in terms of smoothness and miscibility.

The Effect of Incorporation of Multi-Walled Carbon Nanotube into Poly(Ethylene Oxide) Gel Electrolyte on the Photovoltaic Performance of Dye-Sensitized Solar Cell

ABSTRACT Gel polymer electrolytes (GPEs) consist of poly(ethylene oxide) (PEO), sodium iodide (NaI) and different amount of multi-walled carbon nanotubes (MWCNT) were prepared. The conductivity study revealed that the highest ionic conductivity of GPE was 7.02 × 10−3 S cm−1. The structural and complexation between the materials are authenticated via X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy. Under the exposure of AM 1.5, the fabricated DSSCs exhibited the highest photoenergy conversion efficiency of 7.23% with a short circuit current density (JSC) of 18.64 mA cm−2, open circuit voltage (VOC) of 0.590 mV and fill factor (FF) of 65.7%. GRAPHICAL ABSTRACT GRAPHICAL ABSTRACT

Prediction of Conductivity by Adaptive Neuro-Fuzzy Model

Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity.