panjepour@iut.ac.ir Office Department of Materials Engineering Phone +98 311 3917539 Fax +98 311 3912752 Positions Associate Professor of Materials Department Research Interests Extractive and process Metallurgy Recycling and Reuse of Materials and Energy as a Sustainable practice Fuel Cell and Lithium-Ion Battery Sustainable Materials and Energy Thermal Analysis of Materials Thermodynamics and Kinetics of Materials Advance Materials Carbon Materials Catalyst design and Fabrication Molecular dynamics (MD) simulations and DFT Transport Phenomena Numerical Simulation of Fluid Flow Heat and Mass Transfer in Porous Media Artificial Intelligence and Machine Learning More ArticlesJournal Papers Title DOI type Synthesis, characterization, and evaluation of polyaniline-modified (FeCoNiCrMn)3O4 high-entropy oxide as an anode material for lithium-ion batteries 10.1016/j.matchemphys.2024.130322 Journal An investigation into the impact of cobalt replacement by silicon in FeCrMnNiCo non-stoichiometric high entropy alloys 10.1016/j.jallcom.2024.178164 Journal Advanced modelling and optimization of steam methane reforming: From CFD simulation to machine learning - Driven optimization 10.1016/j.ijhydene.2024.11.352 Journal Kinetic analysis of fullerene C <sub>60</sub> thermal degradation via deconvolution method 10.1080/1536383X.2024.2390576 Journal Exploring mass transfer mechanisms in sintering processes through molecular dynamics simulation 10.1016/j.mtcomm.2024.108048 Journal A New Generation of Metal Chillers to Control the Solidification Structure of Al-4.5wt%Cu Alloy # Journal Theoretical and Experimental Study on the Formation Mechanisms of TiAl: Using the Method of Molecular Dynamics Simulation and SPS Method 10.1007/s11837-023-06095-9 Journal Lotus-type porous magnesium production via in situ pyrolysis of viscose rayon fiber in a melting process 10.1007/s10853-023-08563-8 Journal Magnesium Metal Foam Production Using Polypropylene Fibers as an Active Hydrogen Source 10.1007/s10904-023-02649-1 Journal Pressure gradient prediction for different metallic foams using a combination of CFD and Machine Learning methods 10.1615/JPorMedia.2023043975 Journal
