Exploring Application of Dendrimer in Energy Storage, Uranyl Extraction and Drug Delivery through Molecular Modelling

Abstract

This thesis explores the potential applications of poly(amidoamine) (PAMAM) dendrimers, a class of highly branched polymers, using atomistic molecular dynamics simulations. We investigate the use of PAMAM dendrimers in three key areas: enhancing energy storage, environmental purification, and targeted drug delivery. The first part of the thesis focuses on how PAMAM dendrimers enhance the energy density of electric double-layer capacitors (EDLCs), also known as supercapacitors. We investigate the potential of PAMAM dendrimers as electrolytes and electrode coating materials in graphene-based supercapacitors. Our findings suggest that the presence of the dendrimer in the electrodes and the electrolyte significantly increases the capacitance, attributed to the enhanced electrostatic screening and reorganization of the double-layer structure. The second part of the thesis explores the use of PAMAM dendrimers for the adsorption and removal of uranyl ions from aqueous solutions. The study reveals that PAMAM dendrimers exhibit a high adsorption capacity for uranyl ions, particularly in acidic solutions. This property is leveraged for the remediation of uranium contamination, a significant environmental and health concern. To further enhance the removal efficiency and improve the stability of the adsorbent, we grafted the dendrimer with CNT and graphene. We found CNT-PAMAM adsorbed more uranyl compared to graphene-PAMAM due to its curvature and more accessible surface area. Finally, we investigate the potential use of galactose functionalized PAMAM dendrimers in enhancing the target specificity of small interfering RNA (siRNA) therapeutics. Our simulations provide atomistic insights into the siRNA/galactose-dendrimer complex, revealing a higher binding affinity and thermodynamic stability at physiological pH. In addition to these specific applications, we explore the potential of machine learning techniques to predict dendrimer cytotoxicity. By analyzing a curated data set of dendrimers and their corresponding toxicity levels, we develop a machine-learning model that can accurately estimate dendrimer cytotoxicity based on their physiochemical features. This approach offers a rapid, cost-effective, and ethically sound alternative to traditional toxicity assessment methods, accelerating the design and optimization of dendrimers for safe and effective therapeutic applications. This thesis underscores the versatility of PAMAM dendrimers and their potential in advancing the fields of energy storage, environmental remediation, and gene delivery.