Author, Institution: Tomas Klinavičius, Kaunas University of Technology
Science area, field of science: Technological Sciences, Materials Engineering, T008
Research supervisor: Prof. Dr. Tomas Tamulevičius (Kaunas University of Technology, Technological Sciences, Materials Engineering, T008)
Dissertation Defence Board of Materials Engineering Science Field:
Prof. Dr. Hab. Arvaidas Galdikas (Kaunas University of Technology, Technological Sciences, Materials Engineering, T008) – chairperson
Prof. Dr. Wolfgang Fritzsche (Friedrich Schiller University of Jena, Germany, Technological Sciences, Materials Engineering, T008)
Senior Researcher Dr. Lina Grinevičiūtė (State Research Institute Center for Physical Sciences and Technology, Technological Sciences, Materials Engineering, T008)
Assoc. Prof. Dr. Tomas Iešmantas (Kaunas university of Technology, Natural Sciences, Mathematics, N001)
Assoc. Prof. Dr. Mantas Sriubas (Kaunas University of Technology, Technological Sciences, Materials Engineering, T008)
Dissertation defence meeting will be at Rectorate Hall of Kaunas University of Technology (K. Donelaičio 73-402, Kaunas)
The doctoral dissertation is available at the library of Kaunas University of Technology (Gedimino 50, Kaunas) and on the internet: T. Klinavičius el. dissertation.pdf
© T. Klinavičius, 2025 “The text of the thesis may not be copied, distributed, published, made public, including by making it publicly available on computer networks (Internet), reproduced in any form or by any means, including, but not limited to, electronic, mechanical or other means. Pursuant to Article 25(1) of the Law on Copyright and Related Rights of the Republic of Lithuania, a person with a disability who has difficulties in reading a document of a thesis published on the Internet, and insofar as this is justified by a particular disability, shall request that the document be made available in an alternative form by e-mail to doktorantura@ktu.lt.”
Annotation: Photovoltaic (PV) cells generate electricity directly from sunlight. The drawbacks of PV cells include small energy conversion efficiency and their useful lifetime reduction due to heating caused by the unabsorbed spectral components. One approach of reducing these drawbacks is spectrum splitting to redirect the unusable spectral components away from PV cells and capture them through other means. This can be achieved by carefully designed selectively reflecting optical coatings. Another is to increase the absorption of PVs by embedding specifically sized selectively absorbing plasmonic nanoparticles (NPs) into the active medium of PVs. Here, a simple, cheap, rapid, and accurate characterization of their size is needed. Photonic structure design can be performed by utilizing numerical simulation methods coupled with optimization. Meanwhile deep neural networks are powerful alternatives to both methods. Utilizing these advanced computational methods in designing resonant reflecting optical coatings, as well as the prediction of NP size, is of paramount importance in overcoming the drawbacks of PVs. Therefore, the objective of this dissertation is to devise a computational design method for selectively reflecting solar absorbers and to develop a characterization method for plasmonic noble metal nanoparticle size distribution by using numerical simulation, mathematical optimization, and artificial intelligence. A selectively reflecting absorber which allowed to surpass 50% combined power efficiency was designed. Deep neural network predictions of mean nanoparticle size are accurate down to an error of 1.2% of the mean size achieved for plasmonic silver nanoparticles were achieved.