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@PHDTHESIS{Nevolianis:1022617,
author = {Nevolianis, Thomas},
othercontributors = {Leonhard, Kai and Mitsos, Alexander},
title = {{H}arnessing the power of quantum chemistry and machine
learning for improved synthesis and characterization of
functional microgels; 1. {A}uflage},
volume = {58},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {Wissenschaftsverlag Mainz GmbH},
reportid = {RWTH-2025-10126},
isbn = {978-3-95886-552-5},
series = {Aachener Beiträge zur technischen Thermodynamik},
pages = {1 Online-Ressource : Illustrationen},
year = {2026},
note = {Druckausgabe: 2026. - Auch veröffentlicht auf dem
Publikationsserver der RWTH Aachen University. -
Schreibfehler im Übersetzungstitel des Dokuments:
funktionelen; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2025},
abstract = {Microgels are versatile soft polymer networks with
applications in catalysis, drug delivery, and enzyme
immobilization. Their functionality and behavior are closely
linked to synthesis conditions, which determine the
conditions under which they swell and the extent of that
swelling. This thesis advances the predictive modeling of
microgel synthesis by integrating quantum chemical
calculations and machine learning, providing quantitative,
model-based guidance grounded in computational and
experimental data for designing microgels with tailored
swelling behavior and other desired properties, thereby
enhancing their utility in both industrial and research
settings. First, it is demonstrated that the monomer
distribution within N-vinylcaprolactam-based microgels
functionalized with glycidyl methacrylate can be tuned using
an existing synthesis model that integrates quantum chemical
calculations with experimental measurements, enabling
precise control over the microgel’s structure and
properties. Second, deuteration-induced changes in the Flory
interaction parameter are quantified using quantum chemical
calculations to predict swelling behavior in deuterated
microgels, and these predictions are validated against
experimental data, providing insights into the differences
in responsiveness, structure, and softness between
deuterated and hydrogenated microgels. Third, new methods
are developed that significantly improve the accuracy of
solvation free energy predictions for ionic solutes; crucial
for modeling reaction kinetics and thermochemistry in ionic
microgel domains and thereby facilitating the design of
functional microgels with tailored ionic domains. Finally,
multi-fidelity learning approaches are introduced that
leverage quantum chemical and experimental data to improve
the predictability of toluene/water partition coefficients;
these approaches are transferable to other solvent systems,
crucial for designing microgels with hydrophobic domains.
Together, this work demonstrates how integrating
computational chemistry with experimental validation can
optimize microgel synthesis and functionality, paving the
way for advances in various applications.},
cin = {412110},
ddc = {620},
cid = {$I:(DE-82)412110_20140620$},
pnm = {SFB 985 B04 - Synthese von Mikrogelen: Kinetik,
Partikelbildung und Reaktordesign (B04) (221473487) / SFB
985: Funktionelle Mikrogele und Mikrogelsysteme (191948804)},
pid = {G:(GEPRIS)221473487 / G:(GEPRIS)191948804},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2025-10126},
url = {https://publications.rwth-aachen.de/record/1022617},
}
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