ABSTRACT
Cultural heritage stone materials frequently experience significant discoloration induced by copper corrosion products, especially calcareous stones associated with bronze or copper statues and architectural elements. This alteration originates from the corrosion of unprotected copper, resulting in the formation of various Cu minerals and the migration of soluble ions to adjacent stone materials. Traditional cleaning methods involve mechanical, chemical, and laser techniques, which are generally time-consuming, costly, not ecological, or can possibly damage original materials. The loading of highly effective chelating agents, such as ethylenediaminetetraacetic acid (EDTA), into hydrogels has recently been exploited. However, the preference for synthetic hydrogels has been prominent until now, although they lack renewability and biodegradability and require high costs. This study explores for the first time the potential to clean copper corrosion with bacterial nanocellulose (BC) loaded with EDTA as a biologically based, sustainable, and biodegradable hydrogel. The BC hydrogel was characterised by field emission-scanning electron microscopy (FE-SEM), X-ray diffraction analysis (XRD), attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), simultaneous thermal analysis (TG-DSC), and tensile testing. It revealed a nano-fibrous structure with high crystallinity and purity and mechanical properties suitable for cultural heritage applications. The EDTA-loaded hydrogel effectively removed copper stains from marble after 120 min of application. Micro-Raman and colorimetric analyses assessed the cleaning efficacy. The study introduces bacterial nanocellulose as a green and effective alternative for heritage conservation, aligning with sustainable methodologies in stone conservation.
ABSTRACT
Global Sensitivity Analysis (GSA) plays a significant role in quantifying the tangible impact of model inputs on the uncertainty of response variable. As GSA results are strongly affected by correlated inputs, several studies have considered this issue, but most of them are computationally expensive, labor-intensive, and difficult to implement. Accordingly, this paper puts forward a novel regression-based strategy based on the Supervised Principal Component Analysis (SPCA), benefiting from the Reproducing Kernel Hilbert Space. Indeed, by conducting one kind of variance-based sensitivity analysis, a renowned method exclusively customized for models with orthogonal inputs, on SPCA regression, the impact of the correlation structure of input variables is considered. The ability of the suggested technique is evaluated with five test cases as well as three hydrologic and hydraulic models, and the results are compared with those obtained from the correlation ratio method; Taken as a benchmark solution, which is a robust but quite complicated approach in terms of programming. It is found that the proposed method satisfactorily identifies the sensitivity ordering of model inputs. Furthermore, it is proved in this study that the performance of the proposed approach is also supported by the total contribution index in the derived covariance decomposition equation. Moreover, the proposed method compared with the correlation ratio method, is found to be computationally efficient and easy to implement. Overall, the proposed scheme is appropriate for high dimensional, quite strong nonlinear or expensive models with correlated inputs, whose coefficient of determination between the original model and regression-based SPCA model is larger than 0.33.
