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<metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/"><dc:title>Characterisation and degradation of poly(vinyl chloride) in historical and contemporary objects</dc:title><dc:creator>Rijavec,	Tjaša	(Avtor)
	</dc:creator><dc:creator>Kralj Cigić,	Irena	(Mentor)
	</dc:creator><dc:subject>-</dc:subject><dc:description>Poly(vinyl chloride) (PVC) objects make up a significant part of contemporary heritage 
collections. PVC was identified as one of the polymeric materials that are prone to degradation, 
making it necessary to conduct further studies for an improved understanding of the 
mechanisms of degradation. PVC objects can become part of collections long after their 
lifetime, as defined by industrial standards, has ended. The aim of this work was to first develop 
analytical methods for a more comprehensive characterisation of PVC objects. So far, the
chemical composition (identity and content of plasticizers, polymer molar mass and branching)
remains largely unknown for objects in heritage collections. To overcome this gap, analytical 
methods were developed, validated, and then applied to many diverse PVC objects found in a 
reference collection at the Faculty of Chemistry and Chemical Technology, University of 
Ljubljana. Gas chromatography with a mass spectrometric detector or a flame ionisation 
detector (GC-MS and GC-FID) was used for a qualitative and quantitative determination of 
plasticizersin more than 100 objects. Near-infrared and ATR mid-infrared spectra of the objects
were collected non-destructively to create classification and regression models for a quick
characterisation of plasticizers in collection objects. Machine learning-supported classification 
algorithms (Linear Discriminant Analysis (LDA), Naïve Bayes Classification (NBC), Support 
Vector Machines (SVM), k-nearest neighbours (kNN), decision trees (DT), and Extreme 
gradient boosted decision trees (XGBDT)), were successfully used to identify the plasticizers
in the object. Partial least squares regression was used to determine the content of di(2-
ethylhexyl) phthalate and dioctyl terephthalate in PVC objects. Accurate determination of 
PVC’s molar mass by size-exclusion chromatography with multi-angle light scattering
detection required optimising the sample preparation procedure. The optimised method was 
used to describe the ranges and the distribution of molar mass of historical and contemporary 
PVC objects and used to investigate changes in molar mass due to accelerated degradation. The 
main goal of my research was to model the degradation of PVC by developing a damage 
function, which evaluates the contribution of variables to the yellowing of transparent PVC 
objects. Elimination of hydrogen chloride from PVC leads to the formation of polyene 
sequences, resulting in yellowing of the material. Objects from a reference collection were 
exposed to accelerated degradation experiments, characterised with the developed analytical 
methods, and the yellowing was expressed as a rate of increasing the value of the b* coordinate 
in the CIEL*a*b* colour space. Higher temperature and relative humidity increased the 
degradation rate, while higher plasticizer content and polymer molar mass decreased it. The 
activation energy of the yellowing was calculated as (86 ± 3) kJ/mol. The lifetime of a PVC 
object, based on perceptible colour change, can be predicted from its properties and the 
environmental conditions of storage. Understanding the contribution of variables to degradation 
can help prioritise conservation efforts and resource allocation in heritage collections of modern 
materials.
</dc:description><dc:date>2024</dc:date><dc:date>2024-01-11 14:25:13</dc:date><dc:type>Doktorsko delo/naloga</dc:type><dc:identifier>153517</dc:identifier><dc:identifier>VisID: 22601</dc:identifier><dc:identifier>COBISS_ID: 181569283</dc:identifier><dc:language>sl</dc:language></metadata>
