| dc.description.abstract | As a result of factors such as responsibility for aesthetics and habitability conditions, facades are one of the building’s most relevant subsystems, which have high maintenance and construction costs. The biological growth and its deposition on surfaces can negatively affect the building aesthetic such as its materials biodeterioration. Besides the decorative aspect of facades, it has been noticed an increasing tendency for ink technology investments regarding its application as a protective coating of building surfaces against weathering. Thus, this work aimed to continue the prior research developed by Labres (2019), evaluating the performance of 2 commercial inks on 4 external facades considering north and south orientations in relation to fungal proliferation between 30 and 400 days of monitoring. The fungi found on exposed surfaces were collected, cultivated, and posteriorly identified by microscopic and DNA sequencing techniques. As result, it was microscopically identified 12 fungi genders. From sequenced samples, it was acquired a correction percentage of 75.86% when comparing the image identifications with the sequencing technique. Additionally, through DNA molecular analysis by sequencing, it was detected 6 new genders not previously identified. Over 400 days of surface monitoring after painting, it was found the following fungi genders: Acremonium, Alternaria, Aspergillus, Beauveria, Candida, Cercospora, Cladosporium, Curvularia, Fusarium, Mycelia Sterilia, Neocucurbitaria, Nigrospora, Phanerochaete, Pithomyces, Penicillium, Talaromyces, Trichoderma e Tripospermum. The highest relative abundances were observed in summer, and each collect showed similar species richness. When comparing sort of buildings, orientations, and seasons, it was noticed significant differences with more gender abundance between treatments IF (Ink-free) and IB (Ink B). Amongst the highest diversity indexes observed, the lowest levels occurred in the winter under south orientation for treatments IF and IB. The best generalized linear models also showed the lowest diversities for IB, indicating better performance in relation to IA (Ink A). | en |
