Sharif Digital Repository

An Atomic Force Microscope (AFM) is a capable tool to manipulate nanoparticles by exerting pushing force on the nanoparticles located on the substrate. In reality, the substrate cannot be considered as a smooth surface particularly at the nanoscale. Hence, the particle may encounter a step on the substrate during a manipulation. In this study, dynamics of the nanoparticle on a stepped substrate and critical pushing force in the manipulation are investigated. There are two possible dynamic modes that may happen in the manipulation on the stepped substrate. In one mode, the nanoparticle may slide on the step edge and then climb up to the step which is a desired mode. Another possible mode is... 

Pure polypyrrole (PPy), PPy/Al2O3 and PPy/SiO2 nanocomposites synthesized galvanostatically using different polymerization charges. Electrochemical impedance measurements of pure polypyrrole revealed that the film thickness affected the impedance responses. The characteristic frequencies vs. film thickness that obtained using Cole-Cole plots showed that electrodeposition mechanism changed from 3D to 1D after nuclei overlapping for pure PPy. Analysis of the nanocomposites impedance spectrums revealed no changes during the film growth, i.e. deposition continues 3D even after nuclei overlapping. Scanning electron microscopy (SEM) confirmed the impedance results and showed that the morphology of... 

A coupled cellular automata and finite element model has been proposed to evaluate static recrystallization kinetics during non-isothermal annealing of cold deformed low carbon steels. The effects of various factors including heating rate, annealing temperature, and initial microstructures have been considered in the model to accurately predict the static recrystallization kinetics and the final microstructures. In order to examine the employed algorithm, the predicted results have been compared with the experimental observations and a good agreement was found between the two sets of results  

This paper presents a new scheme to apply artificial neural network (ANN) in prediction of static recrystallization (SRX) kinetics. Firstly, based on empirical data of SRX kinetics, a mathematical model is suggested to construct a neural network. Then, an appropriate neural network is trained on the basis of the first and the second derivatives of recrystallized fraction with respect to time. Finally, a thermo-mechanical finite element analysis is coupled with the proposed ANN to predict static recrystallization kinetics in hot rolling process of AA5083. To verify the model, the predicted results and the experimental data are compared and it is observed that there is a good consistency... 

A thermo-mechanical model has been developed to establish a coupled heat conduction and plastic flow analysis in hot-rolling process. This model is capable of predicting temperature, strain, and strain rate distributions during hot rolling as well as the subsequent static recrystallization fraction and grain size changes after hot deformation. Finite element and neural network models are coupled to assess recrystallization kinetics after hot rolling. A new algorithm has been suggested to create differential data sets to train the neural network. The model is then used to predict histories of various deformation variables and recrystallization kinetics in hot rolling of AA5083. Comparison... 

The twist extrusion process is a rather new route to produce highly strained materials. In this process, severe plastic deformation is applied by large shear strains. In this regard, prediction of materials behaviour and extrusion pressure is of importance to die designers and engineers. In this article, utilizing an admissible velocity field together with the upper bound technique, the required energy for twist extrusion is predicted. The proposed model is capable of considering the effect of various parameters such as die geometry and friction conditions. In addition, the model anticipates the critical die design parameters. To verify the proposed model, the predictions are compared with... 

Hydrogen storage capacities of raw, oxidized, purified and Fe-doped multi-walled carbon nanotubes (MWCNTs) were studied by electrochemical method. Based on transmission electron microscopy and Raman spectroscopic data, thermal oxidation removed defective graphite shells at the outer walls of MWCNTs. The analysis results indicated that the acid treatment dissolved most of the catalysts and opened some tips of the MWCNTs. Thermal gravimetric analysis and differential scanning calorimetry results illustrated that by oxidation and purification of MWCNTs, the weight loss peak shifts toward a higher temperature. N2 adsorption isotherms of the purified and oxidized MWCNTs showed an increase in N2...