In-Situ Characterization Techniques for Nanomaterials 2018
DOI: 10.1007/978-3-662-56322-9_3
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Wide-Field Surface Plasmon Resonance Microscopy for In-Situ Characterization of Nanoparticle Suspensions

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Cited by 3 publications
(2 citation statements)
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“…In this respect, machine learning methodologies (e.g. deep learning [130][131][132]) should soon help mining more massively the data acquired per (optical) image and accelerating their time-demanding processing, while removing some subjectivity bias. Together with the spatial information on the localization of the event, kinetic information is also reached.…”
Section: [422] Probing the Electrode-electrolyte Interfacementioning
confidence: 99%
“…In this respect, machine learning methodologies (e.g. deep learning [130][131][132]) should soon help mining more massively the data acquired per (optical) image and accelerating their time-demanding processing, while removing some subjectivity bias. Together with the spatial information on the localization of the event, kinetic information is also reached.…”
Section: [422] Probing the Electrode-electrolyte Interfacementioning
confidence: 99%
“…This is in contrast to many other experimental techniques which are often limited to measuring the average response across a nanoparticle population. Optical microscopy can not only allow the position of each particle to be identified [25][26][27][28][29] but also enable changes in the chemical identity of solid particles to be monitored on the basis of the differences in their spectral Figure 1 Schematic of a redox-driven dissolution of metal nanoparticles by electron transfer over macroscopic distances. Molecular oxidising agents notably dissolved oxygen in trace amounts may oxidise the nanoparticles by electron transfer via a conductive substrate with charge injection from the latter at any point on the electrode surface.…”
Section: Introductionmentioning
confidence: 99%