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dc.contributor.authorCutajar, Jan Dariusz
dc.contributor.authorSteindal, Calin Constantin
dc.contributor.authorCaruso, Francesco
dc.contributor.authorJoseph, Edith
dc.contributor.authorFrøysaker, Tine
dc.date.accessioned2024-08-29T08:00:23Z
dc.date.available2024-08-29T08:00:23Z
dc.date.issued2024-08-15
dc.identifier.citationCoatings 14(8) : (2024) // Article ID 1040es_ES
dc.identifier.issn2079-6412
dc.identifier.urihttp://hdl.handle.net/10810/69345
dc.description.abstractDespite advances in conservation–restoration treatments, most surface cleaning tests are subjectively evaluated. Scores according to qualitative criteria are employed to assess results, but these can vary by user and context. This paper presents a range of cleaning efficacy and homogeneity evaluation metrics for appraising cleaning trials, which minimise user bias by measuring quantifiable changes in the appearance and characteristic spectral properties of surfaces. The metrics are based on various imaging techniques (optical imaging by photography using visible light (VIS); spectral imaging in the visible-to-near-infrared (VNIR) and shortwave infrared (SWIR) ranges; chemical imaging by Fourier transform infrared (FTIR) spectral mapping in the mid-infrared (MIR) range; and scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM-EDX) element mapping). They are complemented by appearance measurements (glossimetry and colourimetry). As a case study showcasing the low-cost to high-end metrics, agar gel spray cleaning tests on exposed ground and unvarnished oil paint mock-ups are reported. The evaluation metrics indicated that spraying agar (prepared with citric acid in ammonium hydroxide) at a surface-tailored pH was as a safe candidate for efficacious and homogenous soiling removal on water-sensitive oil paint and protein-bound ground. Further research is required to identify a gel-based cleaning system for oil-bound grounds.es_ES
dc.description.sponsorshipThis work was funded by the Horizon 2020 research and innovation programme under the H2020 Marie Skłodowska Curie Actions grant agreement no. 813789. F.C. acknowledges his Maria Zambrano fellowship from UPV/EHU, funded by the Spanish Ministry of Universities and the European Union NextGenerationEU/PRTR, and support by grant TED2021-129299A-I00, funded by MCIU/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/813789es_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/TED2021-129299A-I00es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/es/
dc.subjecttreatment evaluationes_ES
dc.subjectsurface cleaninges_ES
dc.subjectsoiling removales_ES
dc.subjectagar geles_ES
dc.subjectexposed groundses_ES
dc.subjectunvarnished oil paintses_ES
dc.subjectEdvard Munches_ES
dc.subjectspectral imaginges_ES
dc.titleSpectral- and Image-Based Metrics for Evaluating Cleaning Tests on Unvarnished Painted Surfaceses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.date.updated2024-08-28T14:00:31Z
dc.rights.holder© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).es_ES
dc.relation.publisherversionhttps://www.mdpi.com/2079-6412/14/8/1040es_ES
dc.identifier.doi10.3390/coatings14081040
dc.contributor.funderEuropean Commission
dc.departamentoesQuímica analítica
dc.departamentoeuKimika analitikoa


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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).
Except where otherwise noted, this item's license is described as © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).