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Identifier 000461816
Title Computational design of metal nanoparticles for energy applications
Alternative Title Υπολογιστικός σχεδιασμός μεταλλικών νανοσωματιδίων για ενεργειακές εφαρμογές
Author Περβολαράκης, Εμμανουήλ
Thesis advisor Ρεμεδιάκης, Ιωάννης
Reviewer Κοπιδάκης, Γεώργιος
Στούμπος, Κωνσταντίνος
Abstract The need to switch to renewable energy sources is greater than ever and consequently it is of great importance to improve the efficiency and/or to lower the cost of already existing technologies which can be facilitated by computational materials design. In this thesis we employ Density Functional Theory calculations and statistical methods like Machine Learning to investigate various materials design issues related to energy. The first of those issues involve the challenges of predicting the equilibrium shape of small Au nanoparticles and determining the limit of the current conventional methods. The Wulff construction method, which is the standard tool for predicting nanoparticle shapes, is extended to take into account edge energies, in addition to the surface energies of the material, The edge energies are determined by machine-learning analysis of a database of calculated total energies for a variety of nanostructures. In this context, we discuss shape-dependent optical properties of gold. For the last part of the thesis, we consider three other classes of materials for energy and environmental applications, namely high-entropy alloys, alloyed Mn oxides and halide perovsikites. In particular, we focus on OH adsorption on surfaces of CoCuFeNiPt High-Entropy Alloys, the stability and band gaps of Zn-Mn-Ni spinel- and hausmanite-like oxides and a machinelearning tool that accurately predicts the band gaps of halide perovsiktes. Thus, by combining several levels of theory, that span the full range from quantum-mechanical DFT calculations to data science and machine learning, we can provide valuable insights into the mechanisms that govern the performance of nanomaterials for energy applications.
Language English
Subject Simulation
Προσομοιώσεις
Issue date 2023-03-22
Collection   School/Department--School of Sciences and Engineering--Department of Materials Science and Technology--Doctoral theses
  Type of Work--Doctoral theses
Permanent Link https://elocus.lib.uoc.gr//dlib/0/0/d/metadata-dlib-1705910616-235990-18493.tkl Bookmark and Share
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