Quantitative and qualitative assessment of VOCs emitted from cellulose acetate movie films by PTR-ToF-MS

Abstract

• PTR-ToF-MS was used for the first time for real-time full qualitative and quantitative detection of VOCs released by 41 historical movie films on a cellulose acetate base. • Around 100 different organic ions were attributed to VOCs emitted from films. • Acetic acid, acetaldehyde, acetone, butanol, DMF, formic acid, methanol, propanoic acid dominated the VOC mixture composition. • Over 41 films, acetic acid was the most abundant VOC for 27 films reels, butanol for 6, DMF for 3, formic acid for 2, acetaldehyde for 2, acetone for 1. Cellulose acetate (CA) has been widely used since the second half of the 20th century as a transparent support for photographs and movie films in order to replace the flammable cellulose nitrate. Over time, hydrolysis occurs and the deacetylation of the CA produces acetic acid (AA), a well-known phenomenon in film archives, the so-called “vinegar syndrome”. However, beside AA, other off-gassing compounds may be present, and very few studies have been devoted to their quantitative and qualitative assessment. Proton Transfer Reaction “Time-of-Flight” Mass Spectrometer (PTR-ToF-MS) combines high sensitivity with high mass resolution for real-time detection of multiple Volatile Organic Compounds (VOCs). This technique was used to evaluate the air composition from 41 films dated from the second half of the 20th century, which showed different degradation levels (ranked using A-D strips®: level 0–1.5). More than 100 VOCs were detected, and their distribution was different from one film to another. AA was the most abundant VOC in 27 film cans. In others, it was either N,N dimethylformamide (DMF), butanol, acetaldehyde, acetone or formic acid. This study shows that PTR-MS is a powerful tool to monitor in real-time, and non-invasively, objects degradation in a museum environment via the quantitative and qualitative analysis of their VOCs emissions, and that this can be used for hierarchical cluster analysis classification.