The role of plasticiser loss in the degradation of plastic objects in heritage collections

点击翻译按钮获取中文摘要

As determined by previous studies, analysis of plastic objects in heritage collections has revealed that physical signs such as bloom and 'sweating' are indicative of plastic degradation with shrinkage, cracking and breakage of cellulosic plastic materials being the longer term manifestations of such damage. The presence and characterisation of plasticisers on artefact surfaces has been determined by a range of analytical techniques, including FT-IR and Py-GC/MS, whilst their volatile nature has allowed their detection in gaseous emission products by SPME-GC. However, degradation via plasticiser loss currently becomes apparent to conservators only when these changes in physical appearance occur. Early detection methods or predictive models of degradation which take into account a material's properties and storage environment, and could enable preventative conservation methods to begin at an earlier stage, thus slowing degradation mechanisms before observable changes result. In order to explore the role of plasticiser loss in plastic degradation, the project will seek to prepare multiple identical samples of cellulose acetate and PVC which can be subjected to artificial ageing studies and monitored for resulting degradation. Collaboration with the Smithsonian Museum Conservation Institute will enable the preparation of samples which closely resemble the composition of materials held within heritage collections. Modelling of plasticizer transport will be attempted and validated using (semi-)quantitative methods of imaging, such as near infrared hyperspectral imaging, a method that will need to be developed specifically for the purpose of the project. The project supports the CDT Science and Engineering in Arts, Heritage and Archaeology through alignment with two of its strands: Data Creation and Data to Knowledge. It also aligns with the EPSRC priorities 'Measurement and Sensing' and 'Materials Characterisation' and 'Data to Knowledge'.