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.
Intracavity Optical Trapping
Fatemeh Kalantarifard, Parviz Elahi, Ghaith Makey, Onofrio M. Maragò, F. Ömer Ilday & Giovanni Volpe
Standard optical tweezers rely on optical forces that arise when a focused laser beam interacts with a microscopic particle: scattering forces, which push the particle along the beam direction, and gradient forces, which attract it towards the high-intensity focal spot. Importantly, the incoming laser beam is not affected by the particle position because the particle is outside the laser cavity. Here, we demonstrate that intracavity nonlinear feedback forces emerge when the particle is placed inside the optical cavity, resulting in orders-of-magnitude higher confinement along the three axes per unit laser intensity on the sample. We present a toy model that intuitively explains how the microparticle position and the laser power become nonlinearly coupled: The loss of the laser cavity depends on the particle position due to scattering, so the laser intensity grows whenever the particle tries to escape. This scheme allows trapping at very low numerical apertures and reduces the laser intensity to which the particle is exposed by two orders of magnitude compared to a standard 3D optical tweezers. We experimentally realize this concept by optically trapping microscopic polystyrene and silica particles inside the ring cavity of a fiber laser. These results are highly relevant for many applications requiring manipulation of samples that are subject to photodamage, such as in biological systems and nanosciences.
High-Performance Reconstruction of Microscopic Force Fields from Brownian Trajectories Laura Pérez García, Jaime Donlucas Pérez, Giorgio Volpe, Alejandro V. Arzola & Giovanni Volpe
The accurate measurement of microscopic force fields is crucial in many branches of science and technology, from biophotonics and mechanobiology to microscopy and optomechanics. These forces are often probed by analysing their influence on the motion of Brownian particles. Here, we introduce a powerful algorithm for microscopic Force Reconstruction via Maximum-likelihood-estimator (MLE) Analysis (FORMA) to retrieve the force field acting on a Brownian particle from the analysis of its displacements. FORMA yields accurate simultaneous estimations of both the conservative and non-conservative components of the force field with important advantages over established techniques, being parameter-free, requiring ten-fold less data and executing orders-of- magnitude faster. We first demonstrate FORMA performance using optical tweezers. We then show how, outperforming any other available technique, FORMA can identify and characterise stable and unstable equilibrium points in generic extended force fields. Thanks to its high performance, this new algorithm can accelerate the development of microscopic and nanoscopic force transducers capable of operating with high reliability, speed, accuracy and precision for applications in physics, biology and engineering.
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.
Phototactic Robot Tunable by Sensorial Delays
Maximilian Leyman, Freddie Ogemark, Jan Wehr & Giovanni Volpe
The presence of a delay between sensing and reacting to a signal can determine the long-term behavior of autonomous agents whose motion is intrinsically noisy.
In a previous work [M. Mijalkov, A. McDaniel, J. Wehr, and G. Volpe, Phys. Rev. X 6, 011008 (2016)], we have shown that sensorial delay can alter the drift and the position probability distribution of an autonomous agent whose speed depends on the illumination intensity it measures. Here, using theory, simulations, and experiments with a phototactic robot, we generalize this effect to an agent for which both speed and rotational diffusion depend on the illumination intensity and are subject to two independent sensorial delays. We show that both the drift and the probability distribution are influenced by the presence of these sensorial delays. In particular, the radial drift may have positive as well as negative sign, and the position probability distribution peaks in different regions depending on the delay.
Furthermore, the presence of multiple sensorial delays permits us to explore the role of the interaction between them.