Scientific Classification

types: ArticleN/

Neutral particle Mass Spectrometry with Nanomechanical Systems

Current approaches to Mass Spectrometry (MS) require ionization of the analytes of interest. For high-mass species, the resulting charge state distribution can be complex and difficult to interpret correctly. In this article, using a setup comprising both conventional time-of-flight MS (TOF-MS) and Nano-Electro-Mechanical-Systems-based MS (NEMS-MS) in situ, we show directly that NEMS-MS analysis is insensitive to charge state: the spectrum consists of a single peak whatever the species charge state, making it significantly clearer than existing MS analysis. In subsequent tests, all charged particles are electrostatically removed from the beam, and unlike TOF-MS, NEMS-MS can still measure masses. This demonstrates the possibility to measure mass spectra for neutral particles. Thus, it is possible to envisage MS-based studies of analytes that are incompatible with current ionization techniques and the way is now open for the development of cutting edge system architectures with unique analytical capability

KHT cold stabilization: A scanning electron microscopy study of the formation of surface deposits on stainless steel in model wines

The incidence of yeast cells and wine polysaccharides and polyphenols in the formation of adherent KHT crystals on stainless steel surfaces during cold stabilization was investigated by scanning electron microscopy. Additives were responsible for differences in the deposit configuration, the crystal shape and size as well as in the KHT crystallization kinetics. Yeast cells act as heterogeneous primary nucleation germs for KHT crystal formation. Colloids from wines interacted with KHT crystal faces and affected growth. It was confirmed that polyphenols strongly inhibit the crystallization and result in small crystals with a unidimensional growth. In contrast, with polyphenols, cubic crystals were obtained when wine polysaccharides were associated with yeast cells

Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning

Flatlandis a simple, lightweight environment for fastprototyping and testing of reinforcement learning agents. It is oflower complexity compared to similar 3D platforms (e.g. Deep-Mind Lab or VizDoom), but emulates physical properties of thereal world, such as continuity, multi-modal partially-observablestates with first-person view and coherent physics. We proposeto use it as an intermediary benchmark for problems related toLifelong Learning.Flatlandis highly customizable and offers awide range of task difficulty to extensively evaluate the propertiesof artificial agents. We experiment with three reinforcementlearning baseline agents and show that they can rapidly solvea navigation task inFlatland. A video of an agent acting inFlatlandis available here: https://youtu.be/I5y6Y2ZypdA

Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning

Flatlandis a simple, lightweight environment for fastprototyping and testing of reinforcement learning agents. It is oflower complexity compared to similar 3D platforms (e.g. Deep-Mind Lab or VizDoom), but emulates physical properties of thereal world, such as continuity, multi-modal partially-observablestates with first-person view and coherent physics. We proposeto use it as an intermediary benchmark for problems related toLifelong Learning.Flatlandis highly customizable and offers awide range of task difficulty to extensively evaluate the propertiesof artificial agents. We experiment with three reinforcementlearning baseline agents and show that they can rapidly solvea navigation task inFlatland. A video of an agent acting inFlatlandis available here: https://youtu.be/I5y6Y2ZypdA