Microscopic Engine Powered by Critical Demixing
Falko Schmidt, Alessandro Magazzù, Agnese Callegari, Luca Biancofiore, Frank Cichos & Giovanni Volpe
SPIE Nanoscience + Engineering, Optical trapping and Optical Manipulation XV, San Diego (CA), USA
19-23 August 2018
During the last few decades much effort has gone into the miniaturization of machines down to the microscopic scale with robotic solutions indispensable in modern industrial processes and play a central role in many biological systems. There has been a quest in understanding the mechanism behind molecular motors and several approaches have been proposed to realize artificial engines capable of converting energy into mechanical work. These current micronsized engines depend on the transfer of angular momentum of light, are driven by external magnetic fields, due to chemical reactions or by the energy flow between two thermal reservoirs. Here we propose a new type of engine that is powered by the local, reversible demixing of a critical binary liquid. In particular, we show that an absorbing, optically trapped particle performs revolutions around the optical beam because of the emergence of diffusiophoresis and thereby produces work. This engines is adjustable by the optical power supplied, the temperature of the environment and the criticality of the system.
Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease
Gustav Mårtensson, Joana B. Pereira, Patrizia Mecocci, Bruno Vellas, Magda Tsolaki, Iwona Kłoszewska, Hilkka Soininen, Simon Lovestone, Andrew Simmons, Giovanni Volpe & Eric Westman
Scientific Reports 8, 11592 (2018)
Graph analysis has become a popular approach to study structural brain networks in neurodegenerative disorders such as Alzheimer’s disease (AD). However, reported results across similar studies are often not consistent. In this paper we investigated the stability of the graph analysis measures clustering, path length, global efficiency and transitivity in a cohort of AD (N = 293) and control subjects (N = 293). More specifically, we studied the effect that group size and composition, choice of neuroanatomical atlas, and choice of cortical measure (thickness or volume) have on binary and weighted network properties and relate them to the magnitude of the differences between groups of AD and control subjects. Our results showed that specific group composition heavily influenced the network properties, particularly for groups with less than 150 subjects. Weighted measures generally required fewer subjects to stabilize and all assessed measures showed robust significant differences, consistent across atlases and cortical measures. However, all these measures were driven by the average correlation strength, which implies a limitation of capturing more complex features in weighted networks. In binary graphs, significant differences were only found in the global efficiency and transitivity measures when using cortical thickness measures to define edges. The findings were consistent across the two atlases, but no differences were found when using cortical volumes. Our findings merits future investigations of weighted brain networks and suggest that cortical thickness measures should be preferred in future AD studies if using binary networks. Further, studying cortical networks in small cohorts should be complemented by analyzing smaller, subsampled groups to reduce the risk that findings are spurious.