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Artificial Intelligence Aided Design of Microtextured Surfaces: Application to Controlling Wettability
Artificial intelligence (AI) has emerged as a powerful set of tools for engineering innovative materials. However, the AI-aided design of materials textures has not yet been researched in depth. In order to explore the potentials of AI for discovering innovative biointerfaces and engineering materia...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698866/ https://www.ncbi.nlm.nih.gov/pubmed/33218132 http://dx.doi.org/10.3390/nano10112287 |
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author | Díaz Lantada, Andrés Franco-Martínez, Francisco Hengsbach, Stefan Rupp, Florian Thelen, Richard Bade, Klaus |
author_facet | Díaz Lantada, Andrés Franco-Martínez, Francisco Hengsbach, Stefan Rupp, Florian Thelen, Richard Bade, Klaus |
author_sort | Díaz Lantada, Andrés |
collection | PubMed |
description | Artificial intelligence (AI) has emerged as a powerful set of tools for engineering innovative materials. However, the AI-aided design of materials textures has not yet been researched in depth. In order to explore the potentials of AI for discovering innovative biointerfaces and engineering materials surfaces, especially for biomedical applications, this study focuses on the control of wettability through design-controlled hierarchical surfaces, whose design is supported and its performance predicted thanks to adequately structured and trained artificial neural networks (ANN). The authors explain the creation of a comprehensive library of microtextured surfaces with well-known wettability properties. Such a library is processed and employed for the generation and training of artificial neural networks, which can predict the actual wetting performance of new design biointerfaces. The present research demonstrates that AI can importantly support the engineering of innovative hierarchical or multiscale surfaces when complex-to-model properties and phenomena, such as wettability and wetting, are involved. |
format | Online Article Text |
id | pubmed-7698866 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76988662020-11-29 Artificial Intelligence Aided Design of Microtextured Surfaces: Application to Controlling Wettability Díaz Lantada, Andrés Franco-Martínez, Francisco Hengsbach, Stefan Rupp, Florian Thelen, Richard Bade, Klaus Nanomaterials (Basel) Article Artificial intelligence (AI) has emerged as a powerful set of tools for engineering innovative materials. However, the AI-aided design of materials textures has not yet been researched in depth. In order to explore the potentials of AI for discovering innovative biointerfaces and engineering materials surfaces, especially for biomedical applications, this study focuses on the control of wettability through design-controlled hierarchical surfaces, whose design is supported and its performance predicted thanks to adequately structured and trained artificial neural networks (ANN). The authors explain the creation of a comprehensive library of microtextured surfaces with well-known wettability properties. Such a library is processed and employed for the generation and training of artificial neural networks, which can predict the actual wetting performance of new design biointerfaces. The present research demonstrates that AI can importantly support the engineering of innovative hierarchical or multiscale surfaces when complex-to-model properties and phenomena, such as wettability and wetting, are involved. MDPI 2020-11-18 /pmc/articles/PMC7698866/ /pubmed/33218132 http://dx.doi.org/10.3390/nano10112287 Text en © 2020 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Díaz Lantada, Andrés Franco-Martínez, Francisco Hengsbach, Stefan Rupp, Florian Thelen, Richard Bade, Klaus Artificial Intelligence Aided Design of Microtextured Surfaces: Application to Controlling Wettability |
title | Artificial Intelligence Aided Design of Microtextured Surfaces: Application to Controlling Wettability |
title_full | Artificial Intelligence Aided Design of Microtextured Surfaces: Application to Controlling Wettability |
title_fullStr | Artificial Intelligence Aided Design of Microtextured Surfaces: Application to Controlling Wettability |
title_full_unstemmed | Artificial Intelligence Aided Design of Microtextured Surfaces: Application to Controlling Wettability |
title_short | Artificial Intelligence Aided Design of Microtextured Surfaces: Application to Controlling Wettability |
title_sort | artificial intelligence aided design of microtextured surfaces: application to controlling wettability |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698866/ https://www.ncbi.nlm.nih.gov/pubmed/33218132 http://dx.doi.org/10.3390/nano10112287 |
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