Cathie Ventalon, Oksana Kirichuk, Yotam Navon, Yan Chastagnier, Laurent Heux, Ralf P. Richter, Lionel Bureau and Delphine Débarre*,
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In this paper, we demonstrate two optical sectioning methods that effectively reduce such background and can be readily implemented in a conventional RICM setup: line confocal detection and structured illumination microscopy. We characterize experimentally the benefits to image quality and demonstrate the use of the methods for quantitative imaging of complex biological and biomimetic samples: cellular membranes, thin organic films, biofunctional surfaces. We then discuss the benefits of each method and provide guidelines to arbitrate between sectioning and signal-to-noise ratio. Finally, we provide a detailed description of our experimental setup and a home-written image acquisition and processing software that should allow the interested reader to duplicate such a setup on a home-built or commercial microscope.</p>","PeriodicalId":50,"journal":{"name":"Langmuir","volume":"41 15","pages":"10040–10051 10040–10051"},"PeriodicalIF":3.9000,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optical Sectioning for Reflection Interference Microscopy: Quantitative Imaging at Soft Interfaces\",\"authors\":\"Cathie Ventalon, Oksana Kirichuk, Yotam Navon, Yan Chastagnier, Laurent Heux, Ralf P. 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Optical Sectioning for Reflection Interference Microscopy: Quantitative Imaging at Soft Interfaces
Reflection interference contrast microscopy (RICM, also known as interference reflection microscopy) and related techniques have become of wide interest to the biophysical, soft matter, and biochemistry communities owing to their exquisite sensitivity for characterizing thin films or individual nanoscopic objects adsorbed onto surfaces, or for monitoring cell–substrate interactions. Over the recent years, striking progress has been made to improve the sensitivity and the quantitative analysis of RICM. Its use in more complex environments, with spurious reflections stemming from a variety of structures in the sample, remains however challenging. In this paper, we demonstrate two optical sectioning methods that effectively reduce such background and can be readily implemented in a conventional RICM setup: line confocal detection and structured illumination microscopy. We characterize experimentally the benefits to image quality and demonstrate the use of the methods for quantitative imaging of complex biological and biomimetic samples: cellular membranes, thin organic films, biofunctional surfaces. We then discuss the benefits of each method and provide guidelines to arbitrate between sectioning and signal-to-noise ratio. Finally, we provide a detailed description of our experimental setup and a home-written image acquisition and processing software that should allow the interested reader to duplicate such a setup on a home-built or commercial microscope.
期刊介绍:
Langmuir is an interdisciplinary journal publishing articles in the following subject categories:
Colloids: surfactants and self-assembly, dispersions, emulsions, foams
Interfaces: adsorption, reactions, films, forces
Biological Interfaces: biocolloids, biomolecular and biomimetic materials
Materials: nano- and mesostructured materials, polymers, gels, liquid crystals
Electrochemistry: interfacial charge transfer, charge transport, electrocatalysis, electrokinetic phenomena, bioelectrochemistry
Devices and Applications: sensors, fluidics, patterning, catalysis, photonic crystals
However, when high-impact, original work is submitted that does not fit within the above categories, decisions to accept or decline such papers will be based on one criteria: What Would Irving Do?
Langmuir ranks #2 in citations out of 136 journals in the category of Physical Chemistry with 113,157 total citations. The journal received an Impact Factor of 4.384*.
This journal is also indexed in the categories of Materials Science (ranked #1) and Multidisciplinary Chemistry (ranked #5).