Principal Component Analysis to Determine the Surface Properties That Influence the Self-Cleaning Action of Hydrophobic Plant Leaves
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/141022 |
Resumo: | It is well established that many leaf surfaces display self-cleaning properties. However, an understanding of how the surface properties interact is still not achieved. Consequently, 12 different leaf types were selected for analysis due to their water repellency and self-cleaning properties. The most hydrophobic surfaces demonstrated splitting of the nu(s) CH2 and nu CH2 bands, ordered platelet-like structures, crystalline waxes, high-surface-roughness values, high-total-surface-free energy and apolar components of surface energy, and low polar and Lewis base components of surface energy. The surfaces that exhibited the least roughness and high polar and Lewis base components of surface energy had intracuticular waxes, yet they still demonstrated the self-cleaning action. Principal component analysis demonstrated that the most hydrophobic species shared common surface chemistry traits with low intra-class variability, while the less hydrophobic leaves had highly variable surface-chemistry characteristics. Despite this, we have shown through partial least squares regression that the leaf water contact angle (i.e., hydrophobicity) can be predicted using attenuated total reflectance Fourier transform infrared spectroscopy surface chemistry data with excellent ability. This is the first time that such a statistical analysis has been performed on a complex biological system. This model could be utilized to investigate and predict the water contact angles of a range of biological surfaces. An understanding of the interplay of properties is extremely important to produce optimized biomimetic surfaces. |
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Principal Component Analysis to Determine the Surface Properties That Influence the Self-Cleaning Action of Hydrophobic Plant LeavesIt is well established that many leaf surfaces display self-cleaning properties. However, an understanding of how the surface properties interact is still not achieved. Consequently, 12 different leaf types were selected for analysis due to their water repellency and self-cleaning properties. The most hydrophobic surfaces demonstrated splitting of the nu(s) CH2 and nu CH2 bands, ordered platelet-like structures, crystalline waxes, high-surface-roughness values, high-total-surface-free energy and apolar components of surface energy, and low polar and Lewis base components of surface energy. The surfaces that exhibited the least roughness and high polar and Lewis base components of surface energy had intracuticular waxes, yet they still demonstrated the self-cleaning action. Principal component analysis demonstrated that the most hydrophobic species shared common surface chemistry traits with low intra-class variability, while the less hydrophobic leaves had highly variable surface-chemistry characteristics. Despite this, we have shown through partial least squares regression that the leaf water contact angle (i.e., hydrophobicity) can be predicted using attenuated total reflectance Fourier transform infrared spectroscopy surface chemistry data with excellent ability. This is the first time that such a statistical analysis has been performed on a complex biological system. This model could be utilized to investigate and predict the water contact angles of a range of biological surfaces. An understanding of the interplay of properties is extremely important to produce optimized biomimetic surfaces.20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10216/141022eng0743-746310.1021/acs.langmuir.1c00853Fabian SaubadeLisa I. PilkingtonM. LiauwLuciana GomesJake McClementsMarloes PeetersMohamed El MohtadiFilipe J. MergulhãoKathryn A. Whiteheadinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T14:19:28Zoai:repositorio-aberto.up.pt:10216/141022Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:58:54.551722Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Principal Component Analysis to Determine the Surface Properties That Influence the Self-Cleaning Action of Hydrophobic Plant Leaves |
title |
Principal Component Analysis to Determine the Surface Properties That Influence the Self-Cleaning Action of Hydrophobic Plant Leaves |
spellingShingle |
Principal Component Analysis to Determine the Surface Properties That Influence the Self-Cleaning Action of Hydrophobic Plant Leaves Fabian Saubade |
title_short |
Principal Component Analysis to Determine the Surface Properties That Influence the Self-Cleaning Action of Hydrophobic Plant Leaves |
title_full |
Principal Component Analysis to Determine the Surface Properties That Influence the Self-Cleaning Action of Hydrophobic Plant Leaves |
title_fullStr |
Principal Component Analysis to Determine the Surface Properties That Influence the Self-Cleaning Action of Hydrophobic Plant Leaves |
title_full_unstemmed |
Principal Component Analysis to Determine the Surface Properties That Influence the Self-Cleaning Action of Hydrophobic Plant Leaves |
title_sort |
Principal Component Analysis to Determine the Surface Properties That Influence the Self-Cleaning Action of Hydrophobic Plant Leaves |
author |
Fabian Saubade |
author_facet |
Fabian Saubade Lisa I. Pilkington M. Liauw Luciana Gomes Jake McClements Marloes Peeters Mohamed El Mohtadi Filipe J. Mergulhão Kathryn A. Whitehead |
author_role |
author |
author2 |
Lisa I. Pilkington M. Liauw Luciana Gomes Jake McClements Marloes Peeters Mohamed El Mohtadi Filipe J. Mergulhão Kathryn A. Whitehead |
author2_role |
author author author author author author author author |
dc.contributor.author.fl_str_mv |
Fabian Saubade Lisa I. Pilkington M. Liauw Luciana Gomes Jake McClements Marloes Peeters Mohamed El Mohtadi Filipe J. Mergulhão Kathryn A. Whitehead |
description |
It is well established that many leaf surfaces display self-cleaning properties. However, an understanding of how the surface properties interact is still not achieved. Consequently, 12 different leaf types were selected for analysis due to their water repellency and self-cleaning properties. The most hydrophobic surfaces demonstrated splitting of the nu(s) CH2 and nu CH2 bands, ordered platelet-like structures, crystalline waxes, high-surface-roughness values, high-total-surface-free energy and apolar components of surface energy, and low polar and Lewis base components of surface energy. The surfaces that exhibited the least roughness and high polar and Lewis base components of surface energy had intracuticular waxes, yet they still demonstrated the self-cleaning action. Principal component analysis demonstrated that the most hydrophobic species shared common surface chemistry traits with low intra-class variability, while the less hydrophobic leaves had highly variable surface-chemistry characteristics. Despite this, we have shown through partial least squares regression that the leaf water contact angle (i.e., hydrophobicity) can be predicted using attenuated total reflectance Fourier transform infrared spectroscopy surface chemistry data with excellent ability. This is the first time that such a statistical analysis has been performed on a complex biological system. This model could be utilized to investigate and predict the water contact angles of a range of biological surfaces. An understanding of the interplay of properties is extremely important to produce optimized biomimetic surfaces. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 2021-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/141022 |
url |
https://hdl.handle.net/10216/141022 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0743-7463 10.1021/acs.langmuir.1c00853 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799135912001536000 |