Mechanical cleaning of graphene using in situ electron microscopy

Avoiding and removing surface contamination is a crucial task when handling specimens in any scientific experiment. This is especially true for two-dimensional materials such as graphene, which are extraordinarily affected by contamination due to their large surface area. While many efforts have been made to reduce and remove contamination from such surfaces, the issue is far from resolved. Here we report on an in situ mechanical cleaning method that enables the site-specific removal of contamination from both sides of two dimensional membranes down to atomic-scale cleanliness. Further, mechanisms of re-contamination are discussed, finding surface-diffusion to be the major factor for contamination in electron microscopy. Finally the targeted, electron-beam assisted synthesis of a nanocrystalline graphene layer by supplying a precursor molecule to cleaned areas is demonstrated

Data assimilation to improve models used for the automatic control of rivers or canals

International audienceThe dams and the hydropower plants on the Rhône River, managed by the Companie Nationale du Rhône (CNR), are controller in real-time by Model Predictive Controllers (MPC) since the early 2000s. The control objectives and constraints are manyfold: optimize electrical production, allow navigation, protect the banks from erosion, prevent or reduce the damages during flood events, supply water to industries, cities and irrigation districts. In case the outputs of the embedded model used by MPC do not fit the field measurements, some questions are raised on: how to interpret this, and what can be done to solve this problem? We will present recent developments, carried out and illustrated on the Rhône River allowing to address these issues. The framework we will use is the one of Kalman filtering. We will see that this framework is very powerful to solve the above described problems. But, in some cases the obtained solution is not the one we would expect. The conditions of success can be expressed and checked from some mathematical tests, and linked to some physical properties (number and location of sensors, uncertainties of the measurements and of the model, hydraulic configuration of the hydraulic system)