Doctoral theses
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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
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Περβολαράκης, Εμμανουήλ
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Thesis advisor
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Ρεμεδιάκης, Ιωάννης
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Reviewer
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Κοπιδάκης, Γεώργιος
Στούμπος, Κωνσταντίνος
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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.
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Language |
English |
Subject |
Simulation |
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Προσομοιώσεις |
Issue date |
2024-03-22 |
Collection
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School/Department--School of Sciences and Engineering--Department of Materials Science and Technology--Doctoral theses
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Type of Work--Doctoral theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/0/0/d/metadata-dlib-1705910616-235990-18493.tkl
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Views |
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