Non-Boltzmann stationary distributions and nonequilibrium relations in active baths

Most natural and engineered processes, such as biomolecular reactions, protein folding, and population dynamics, occur far from equilibrium and therefore cannot be treated within the framework of classical equilibrium thermodynamics. Here we experimentally study how some fundamental thermodynamic quantities and relations are affected by the presence of the nonequilibrium fluctuations associated with an active bath. We show in particular that, as the confinement of the particle increases, the stationary probability distribution of a Brownian particle confined within a harmonic potential becomes non-Boltzmann, featuring a transition from a Gaussian distribution to a heavy-tailed distribution. Because of this, nonequilibrium relations (e.g., the Jarzynski equality and Crooks fluctuation theorem) cannot be applied. We show that these relations can be restored by using the effective potential associated with the stationary probability distribution. We corroborate our experimental findings with theoretical arguments. © 2016 American Physical Society

Phenoxazine Based Units- Synthesis, Photophysics and Electrochemistry

A few new phenoxazine-based conjugated monomers were synthesized, characterized, and successfully used as semiconducting materials. The phenoxazine-based oligomers have low ionization potentials or high-lying HOMO levels (~4.7 eV), which were estimated from cyclic voltammetry. Conjugated oligomers offer good film—forming, mechanical and optical properties connected with their wide application. These results demonstrate that phenoxazine-based conjugated mers are a promising type of semiconducting and luminescent structures able to be used as thin films in organic electronics

Objective comparison of methods to decode anomalous diffusion

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers. Deviations from Brownian motion leading to anomalous diffusion are ubiquitously found in transport dynamics but often difficult to characterize. Here the authors compare approaches for single trajectory analysis through an open competition, showing that machine learning methods outperform classical approaches

Objective comparison of methods to decode anomalous diffusion

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers

An electrochromic ionic liquid: design, characterisation and performance in a solid state platform

This work describes the synthesis and characteristics of a novel electrochromic ionic liquid (IL) based on a phosphonium core tethered to a viologen moiety. When integrated into a solid-state electrochromic platform, the viologen modified IL behaved as both the electrolyte and the electrochromic material. Platform fabrication was achieved through in situ photo-polymerisation and encapsulation of this novel IL within a hybrid sol-gel. Important parameters of the platform performance, including its coloration efficiency, switching kinetics and optical properties were characterised using UV/Vis spectroscopy and cyclic voltammetry in tandem. The electrochromic platform exhibits a coloration efficiency of 10.72 cm2C-1, and a varied optical output as a function of the incident current. Despite the rather viscous nature of the material, the platform exhibited approximately two orders of magnitude faster switching kinetics (221 seconds to reach 95 % absorbance) when compared to previously reported electrochromic ILs (18,000 seconds)