Parameterization of the optical constants of polydopamine films for spectroscopic ellipsometry studies
Literature Information
Runtian Qie, Saeed Zajforoushan Moghaddam, Esben Thormann
Bio-inspired polydopamine coatings offer vast possibilities for surface modification of materials. The thickness of such nanometric coatings is usually estimated based on ellipsometry measurements. However, the complex light-absorbing nature of polydopamine is often overlooked when analyzing such data, which can result in inaccurate estimations of the coating thickness as well as the optical properties. In this study, we prepared and characterized three polydopamine coatings where the film thickness and surface roughness are systematically varied. For each case, we developed suitable optical models and showed how an inappropriate optical model can provide inaccurate estimates of the coating properties. AFM height profiles were obtained from scratched areas of each sample to verify the thickness values estimated by ellipsometry. The results confirm that polydopamine coatings, depending on the oxidation conditions, can possess different structural and optical properties, and thus require unique optical models for the ellipsometry analysis.
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.












![1,10-bis(3,5-dimethylphenyl)-12-hydroxy-4,5,6,7-tetrahydroiindeno[7,1-de:1',7'-fg][1,3,2]dioxaphosphocine 12-oxide structure 1,10-bis(3,5-dimethylphenyl)-12-hydroxy-4,5,6,7-tetrahydroiindeno[7,1-de:1',7'-fg][1,3,2]dioxaphosphocine 12-oxide structure](https://static.chemtradehub.com/structs/141/1412439-82-7-b9a9.webp)

