Stockholm university

Roja RahmaniPhD Student

About me

I was born in 1993 in Sari, Iran. In 2013, I started my bachelor's studies in Applied Chemistry at the University of Tabriz, Iran, where I worked (as an undergraduate student researcher) on high-performance supercapacitors based on chitosan/Fe-Co-graphene oxide-manganese oxide-MWCNT/polyaniline. After receiving my bachelor’s degree in 2016, I continued my studies with a master's degree in Physical Chemistry at K. N. Toosi University of Technology, Tehran, Iran where I graduated in 2019 with a thesis on the interaction of ZnS nanosheets and nanotubes with amino acids using advanced sampling techniques and free energy calculations. In 2020, I started to work as a Ph.D. student at Stockholm University in the group of Professor Alexander Lyubartsev.

Teaching

Teaching assistant in the course Physical chemistry: quantum mechanics and spectroscopy (KZ4020)

Teaching assistant in the course Chemical Bonding (KZ4012)

Research

My research focuses on modeling the nanotoxicity of inorganic nanomaterials regarding their physicochemical properties such as the shape and coating of nanomaterials and their interactions with biomolecules. This is part of a NanoSolveIT project (funding by the European Union Horizon 2020 Programme (H2020)) which aims to find a set of descriptors to predict nanomaterials properties, functionality, and hazard by developing and integrating advanced nanoinformatics methods and tools.

Publications

A selection from Stockholm University publication database

  • Biomolecular Adsorprion at ZnS Nanomaterials: A Molecular Dynamics Simulation Study of the Adsorption Preferences, Effects of the Surface Curvature and Coating

    2023. Roja Rahmani, Alexander P. Lyubartsev. Nanomaterials 13 (15)

    Article

    The understanding of interactions between nanomaterials and biological molecules is of primary importance for biomedical applications of nanomaterials, as well as for the evaluation of their possible toxic effects. Here, we carried out extensive molecular dynamics simulations of the adsorption properties of about 30 small molecules representing biomolecular fragments at ZnS surfaces in aqueous media. We computed adsorption free energies and potentials of mean force of amino acid side chain analogs, lipids, and sugar fragments to ZnS (110) crystal surface and to a spherical ZnS nanoparticle. Furthermore, we investigated the effect of poly-methylmethacrylate (PMMA) coating on the adsorption preferences of biomolecules to ZnS. We found that only a few anionic molecules: aspartic and glutamic acids side chains, as well as the anionic form of cysteine show significant binding to pristine ZnS surface, while other molecules show weak or no binding. Spherical ZnS nanoparticles show stronger binding of these molecules due to binding at the edges between different surface facets. Coating of ZnS by PMMA changes binding preferences drastically: the molecules that adsorb to a pristine ZnS surface do not adsorb on PMMA-coated surfaces, while some others, particularly hydrophobic or aromatic amino-acids, show high binding affinity due to binding to the coating. We investigate further the hydration properties of the ZnS surface and relate them to the binding preferences of biomolecules.

    Read more about Biomolecular Adsorprion at ZnS Nanomaterials
  • Biomolecular Adsorption on Nanomaterials: Combining Molecular Simulations with Machine Learning

    2024. Marzieh Saeedimasine, Roja Rahmani, Alexander P. Lyubartsev. Journal of Chemical Information and Modeling

    Article

    Adsorption free energies of 32 small biomolecules (amino acids side chains, fragments of lipids, and sugar molecules) on 33 different nanomaterials, computed by the molecular dynamics - metadynamics methodology, have been analyzed using statistical machine learning approaches. Multiple unsupervised learning algorithms (principal component analysis, agglomerative clustering, and K-means) as well as supervised linear and nonlinear regression algorithms (linear regression, AdaBoost ensemble learning, artificial neural network) have been applied. As a result, a small set of biomolecules has been identified, knowledge of adsorption free energies of which to a specific nanomaterial can be used to predict, within the developed machine learning model, adsorption free energies of other biomolecules. Furthermore, the methodology of grouping of nanomaterials according to their interactions with biomolecules has been presented.

    Read more about Biomolecular Adsorption on Nanomaterials

Show all publications by Roja Rahmani at Stockholm University