THE PROJECT

PlasCO2 - Green Technologies for the Cleaning of Plastics in Museums and Heritage Collections is a 3-year research project (2018-2021) financed by the Portuguese Foundation for Science and Technology (FCT - Fundação para Ciência e a Tecnologia).

PlasCO2 explores the use of Carbon Dioxide (CO2) - in liquid or supercritical phase - for the preservation of modern and contemporary works of art. The final aim is to design environmentally friendly cleaning and conservation protocols based on CO2 technology for selected plastic materials, namely Polyurethanes (PUR), foam/rubber-based materials, poly(methyl methacrylate) (PMMA) and others.
Extensive trials are performed using a variety of parameters (CO2 phase, density) and conditions (pressure, temperature, use of co-solvent, exposure time); their impact and suitability for the selected materials are assessed from the macro to the nanoscale using different analytical techniques.

A Machine Learning model will be used in conjunction with the experimental work to correlate the physical and chemical properties of these materials, the conditions of the CO2 treatment procedures and the observed outcomes, with the ultimate aim of providing a predictive tool for the treatment conditions for cultural heritage items.

THE PROJECT

PlasCO2 - Green Technologies for the Cleaning of Plastics in Museums and Heritage Collections is a 3-year research project (2018-2021) financed by the Portuguese Foundation for Science and Technology (FCT - Fundação para Ciência e a Tecnologia).

PlasCO2 explores the use of Carbon Dioxide (CO2) - in liquid or supercritical phase - for the preservation of modern and contemporary works of art. The final aim is to design environmentally friendly cleaning and conservation protocols based on CO2 technology for selected plastic materials, namely Polyurethanes (PUR), foam/rubber-based materials, poly(methyl methacrylate) (PMMA) and others.
Extensive trials are performed using a variety of parameters (CO2 phase, density) and conditions (pressure, temperature, use of co-solvent, exposure time); their impact and suitability for the selected materials are assessed from the macro to the nanoscale using different analytical techniques.

A Machine Learning model will be used in conjunction with the experimental work to correlate the physical and chemical properties of these materials, the conditions of the CO2 treatment procedures and the observed outcomes, with the ultimate aim of providing a predictive tool for the treatment conditions for cultural heritage items.