Cleaning historical silk textiles is a particularly sensitive operation that requires precise control to prevent mechanical or chemical damage. In this study, we investigate using flexible PVA–borax-based gels to remove soot from silk, i.e., polyvinyl alcohol–borax (PVA-B) gels and polyvinyl alcohol–borax–agarose double network gels (PVA-B/AG DN) loaded with different cleaning agents—namely, 30% ethanol and 1% Ecosurf EH-6—in addition to plain gels loaded with water. These gel formulations were tested on simplified model systems (SMS) and were applied using two methods: placing and tamping. The cleaning results were compared with a traditional contact-cleaning approach; micro-vacuuming followed by sponging. Visual inspection, 3D opto-digital microscopy, colorimetry, and machine-learning-assisted (ML) soot counting were exploited for the assessment of cleaning efficacy. Rheological characterization provided information about the flexibility and handling properties of the different gel formulations. Among the tested systems, the DN gel containing only water, applied by tamping, was easy to handle and demonstrated the highest soot-removal effectiveness without leaving residues, as confirmed by micro-Fourier Transform Infrared (micro-FTIR) analysis. Scanning electron microscope (SEM) micrographs proved the structural integrity of the treated silk fibers. Overall, this work allows us to conclude that PVA–borax-based gels offer an effective, adaptable, and low-risk cleaning strategy for historical silk fabrics.

In the context of the assessment and validation of plasma-generated atomic oxygen (AO), within the framework of EU Horizon MOXY project, this study compared three non-invasive methodologies for the semi-quantitative evaluation of cleaning efficacy: CIELAB colorimetry, statistical analysis of image brightness histograms and a supervised machine learning method (TWS). The research focuses on assessing the strengths and limitations of these methods when applied to complex, highly textured substrates such as textiles. A benchmark set of simplified model systems (SMSs), consisting of pongee silk swatches artificially soiled with soot and treated with AO alongside seven alternative cleaning methods, was used as case study. Among the evaluated techniques, the machine-learning-based approach demonstrated high reliability and versatility for the selective detection of heterogeneous soiling on highly reflective surfaces. Due to its open-access design and user-friendly interface, the method has strong potential for wider use in systematically evaluating cleaning efficacy across various conservation contexts.

This study provides the first laboratory-scale evidence that atmospheric-pressure, helium-plasma-generated near-thermal atomic oxygen (AO) can reverse sulfide-induced darkening of lead white through a non-contact gas-phase process. Using artificially blackened historical stack-process lead white paints bound with gelatin and gum arabic, we show that ultra-high-flux, ultra-low-energy AO generated by the MOXY prototype selectively oxidizes PbS within the paint while preserving the lead-carbonate matrix and substrate. Reflectance spectroscopy, microscopy, SEM, FTIR-ATR, XRPD, µ-XRD, and XPS reveal optical recovery, PbS depletion in the optically active near-surface region, formation of transient PbOₓ-like intermediates, and re-equilibration toward bright lead-carbonate phases with minor localized sulfation. A hybrid XRPD-XPS model links bulk phase composition and surface chemistry to colour formation. Compared with aqueous oxidants such as H₂O₂, AO offers a solvent-free, surface-selective, and lower-impact route for sustainable conservation.

Plasma-generated atomic oxygen (AO) is a highly reactive and chemically selective species with strong potential for the precise removal of carbonaceous contaminants from heritage materials. However, its application to organic substrates is limited by competing oxidation processes that may induce chain scission, mechanical degradation, and mass loss. In this study, the parametric dependence of AO-induced soot removal from undyed silk model substrates was systematically investigated using a prototype developed within the MOXY project. The effects of power and total cleaning time were evaluated to quantify their influence on cleaning efficiency and thermal load. Cleaning performance was assessed using optical and morphological techniques, including 3D optical microscopy, profilometry, drop tests, and hyperspectral imaging, while infrared thermography enabled in situ temperature monitoring. Results show that increasing power or treatment time enhances soot removal, but excessive conditions lead to alterations in fiber morphology and mechanical properties. By correlating cleaning efficiency with thermo-mechanical responses, a safe operating window was identified. Tensile testing, thermogravimetric analysis, dynamic mechanical analysis, and dynamic vapor sorption revealed sub-critical damage thresholds. These findings demonstrate that AO plasma treatment can be optimized for fragile proteinaceous substrates, providing conservators with reliable and controlled cleaning parameters for textile heritage objects.

Climate-change-driven increases in wildfire and fire incidents make fire-borne soot an escalating risk for tangible cultural heritage. Water-sensitive, porous gypsum plaster presents one of the most difficult surfaces to clean after flame-generated soot contamination because conventional methods can under-clean, abrade the surface, or drive soot into the pore network. Here we develop an atmospheric-pressure atomic oxygen (AO) cleaning technology and dose-controlled treatment framework for targeted, non-contact removal of soot from gypsum plaster. AO was generated by a non-equilibrium 13.56 MHz helium plasma source and delivered remotely as a chemically ablating effluent, enabling selective oxidation without direct plasma-surface contact. Compared with representative cleaning methods, AO removed carbonaceous contamination by preferential oxidation to volatile products. Under optimized low-power, high-flux conditions, AO achieved near-complete visual recovery with minimal substrate impact and preserved submicron topography. The results provide a reproducible, solvent-free framework for cleaning highly sensitive heritage surfaces.