Scanning Probe Microscopies allow investigating surfaces at the nanoscale, in the real space and with unparalleled signal-to-noise ratio. However, these microscopies are not used as much as it would be...

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Amorphous selenium coatings have been electroplated in thicknesses up to 30 µ (1.2 mils) on bright nickel cathodes (and thicker coatings can be prepared). Data are given for studies leading to the selection of the optimum bath and operating conditions for the formation of reproducible, uniform, and adherent deposits. It is shown that the plating baths employed and the amorphous deposits obtained are extremely sensitive to a variety of conditions, thus emphasizing the need for further study of the electrodeposition of selenium in the amorphous form.

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Atomic Force Microscopy (AFM) force measurements are a powerful tool for the nano-scale characterization of surface properties. However, the analysis of force measurements requires several processing steps. One is locating different type of events e.g., contact point, adhesions and indentations. At present, there is a lack of algorithms that can automate this process in a reliable way for different types of samples. Moreover, because of their stochastic nature, the acquisition and analysis of a high number of force measurements is typically required. This can result in these experiments becoming an overwhelming task if their analysis is not automated. Here, we propose a Machine Learning approach, the use of one-dimensional convolutional neural networks, to locate specific events within AFM force measurements. Specifically, we focus on locating the contact point, a critical step for the accurate quantification of mechanical properties as well as long-range interactions. We validate this approach on force measurements obtained both on hard and soft surfaces. This approach, which could be easily used to also locate other events e.g., indentations and adhesions, has the potential to significantly facilitate and automate the analysis of AFM force measurements and, therefore, the use of this technique by a wider community.