Quantification of femtosecond pulsed infrared laser cleaning on cellulose-based surfaces in cultural heritage conservation

Femtosecond (fs) laser cleaning is emerging as a promising method for the conservation of cultural heritage materials, yet challenges remain in quantifying cleaning performance, particularly on porous and morphologically heterogeneous substrates. In this doctoral thesis, conducted at La Sapienza University in Rome and the Institut National de la Recherche Scientifique (INRS) in Montréal, a quantitative and spectroscopy-anchored evaluation framework for near-infrared (NIR) fs-laser cleaning has been developed. The first part introduces fundamental aspects of laser–material interactions, with particular emphasis on mechanisms underpinning selective cleaning, alongside a review of diagnostic techniques such as ATR-FTIR spectroscopy, colorimetry, scanning electron microscopy, and profilometry applied in heritage conservation. The second part quantitatively investigates NIR fs-laser cleaning on cellulose-based mock-up samples replicating historical papers, which were artificially aged and contaminated with graphite and kaolinite as model pollutants. The cleaning performance was assessed by ATR-FTIR spectroscopy coupled with exponential saturation and decay models, demonstrating effective removal of surface contaminants within a defined safe fluence window without compromising substrate integrity. The third part explores fs-laser cleaning of historically prepared mock-up leathers, emphasizing that microstructural features such as porosity influence cleaning outcomes. Optical microscopy-based analyses revealed heterogeneous pore size distributions, while colorimetric measurements indicated substantial reflectance variability, highlighting limitations of area-averaged evaluation methods for textured surfaces. In the fourth part, an uncertainty-integrated modeling approach based on Monte Carlo simulations has been implemented to propagate experimental noise and parameter variability in cleaning performance predictions. This enables confidence-based evaluation of cleaning effectiveness under extrapolated conditions. Overall, this thesis establishes a quantitative, microstructure-informed, and uncertainty-aware framework for NIR fs-laser cleaning evaluation. It advances heritage conservation science by linking morphological features to cleaning dynamics and proposing predictive contamination removal protocols with minimal substrate damage risk. .