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<dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:invenio="http://invenio-software.org/elements/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:identifier>doi:10.3390/mi13122253</dc:identifier><dc:language>eng</dc:language><dc:creator>Pons, Carolina</dc:creator><dc:creator>Galindo, Josué M.</dc:creator><dc:creator>Martín, Juan C.</dc:creator><dc:creator>Torres-Moya, Iván</dc:creator><dc:creator>Merino, Sonia</dc:creator><dc:creator>Herrero, M. Antonia</dc:creator><dc:creator>Vázquez, Ester</dc:creator><dc:creator>Prieto, Pilar</dc:creator><dc:creator>Vallés, Juan A.</dc:creator><dc:title>Propagation Losses Estimation in a Cationic-Network-Based Hydrogel Waveguide</dc:title><dc:identifier>ART-2022-132428</dc:identifier><dc:description>A method based on the photographic recording of the power distribution laterally diffused by cationic-network (CN) hydrogel waveguides is first checked against the well-established cut-back method and then used to determine the different contributions to optical power attenuation along the hydrogel-based waveguide. Absorption and scattering loss coefficients are determined for 450 nm, 532 nm and 633 nm excitation. The excellent optical loss values obtained (0.32–1.95 dB/cm), similar to others previously described, indicate their potential application as waveguides in different fields, including soft robotic and light-based therapies.</dc:description><dc:date>2022</dc:date><dc:source>http://zaguan.unizar.es/record/123858</dc:source><dc:doi>10.3390/mi13122253</dc:doi><dc:identifier>http://zaguan.unizar.es/record/123858</dc:identifier><dc:identifier>oai:zaguan.unizar.es:123858</dc:identifier><dc:relation>info:eu-repo/grantAgreement/ES/DGA-FEDER/E44-20R</dc:relation><dc:relation>info:eu-repo/grantAgreement/EC/H2020/881603/EU/Graphene Flagship Core Project 3/GrapheneCore3</dc:relation><dc:relation>This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 881603-GrapheneCore3</dc:relation><dc:identifier.citation>Micromachines 13, 12 (2022), 2253 [9 pp.]</dc:identifier.citation><dc:rights>by</dc:rights><dc:rights>http://creativecommons.org/licenses/by/3.0/es/</dc:rights><dc:rights>info:eu-repo/semantics/openAccess</dc:rights></dc:dc>

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