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As a simple way to enhance catalytical performance, it has long been believed that catalyst loading increases the number of sites available for reaction. The overall reaction rate will increase and saturate eventually as catalyst loading increases. Here, we report a counterintuitive optimal catalyst loading for electrocatalytic carbon dioxide reduction (eCO_{2}R) on nanocatalysts. Numerical analysis based on a comprehensive kinetic model reveals that nanocurved electric field (NEF) resulted from the comparable size of nanocatalysts and the electric double layer can essentially affect eCO_{2}R. While NEF induces extra mass transfer raising the overall reaction rate for low catalyst loading, such extra mass transfer is sharply reduced due to the overlap of NEF for high catalyst loading. These findings challenge the conventional physical picture of loading-dependent overall catalytical performance, and provide a new concept for design of nanocatalysis.

As biochemical systems may frequently suffer from limited energy resources, so that internal molecular fluctuation has to be utilized to induce random rhythm, it is still a great theoretical challenge to understand the elementary principles for biochemical systems with limited energy resources to maintain phase accuracy and phase sensitivity. Here, we address the issue by deriving the energy-accuracy and the sensitivity-accuracy trade-off relations for a general biochemical model, analytically and numerically. We find that biochemical systems have a much lower energy cost by using noise-induced oscillations to keep almost equal efficiency to maintain precise processes compared with the energy cost using normal oscillations, clearly elucidating a survival mechanism when energy resources are limited. Moreover, an optimal system size is predicted where both the highest sensitivity and highest accuracy can be reached at the same time, providing a new strategy for the design of biological networks with limited energy sources.

Although the corresponding carbon-metal interactions can be very different, a similar nonlinear growth behavior of graphene has been observed for different metal substrates. To understand this interesting experimental observation, a multiscale "standing-on-the-front" kinetic Monte Carlo study is performed. An extraordinary robust geometry effect is identified, which solely determines the growth kinetics and makes the details of carbon-metal interaction not relevant at all. Based on such a geometry-determined mechanism, the epitaxial growth behavior of graphene can be easily predicted in many cases. As an example, the orientation-sensitive growth kinetics of graphene on an Ir(111) surface has been studied. Our results demonstrate that the lattice mismatch pattern at the atomic level plays an important role in macroscopic epitaxial growth. [ABSTRACT FROM AUTHOR]

We discuss stochastic thermodynamics (ST) for delayed Langevin systems in this paper. By using the general principles of ST, the first-law-like energy balance and trajectory-dependent entropy s(t) can be well defined in a way that is similar to that in a system without delay. Because the presence of time delay brings an additional entropy flux into the system, the conventional second law <Δstot> 0 no longer holds true, where Δstot denotes the total entropy change along a stochastic path and <•> stands for the average over the path ensemble. With the help of a Fokker-Planck description, we introduce a delay-averaged trajectory-dependent dissipation functional η[χ(t)] which involves the work done by a delay-averaged force F(x,t) along the path χ(t) and equals the medium entropy change Δsm[x(t)] in the absence of delay. We show that the total dissipation functional R=Δs+η, where Δs denotes the system entropy change along a path, obeys ⩾0, which could be viewed as the second law in the delayed system. In addition, the integral fluctuation theorem =1 also holds true. We apply these concepts to a linear Langevin system with time delay and periodic external force. Numerical results demonstrate that the total entropy change <Δstot> could indeed be negative when the delay feedback is positive. By using an inversing-mapping approach, we are able to obtain the delay-averaged force F(x,t) from the stationary distribution and then calculate the functional R as well as its distribution. The second law ⩾0 and the fluctuation theorem are successfully validated. [ABSTRACT FROM AUTHOR]

We have studied spatial and temporal dynamic heterogeneity (DH) in a system of hard-sphere particles, subjected to active forces with constant amplitude and random direction determined by rotational diffusion with correlation time τ. We have used a variety of observables to characterize the DH behavior, including the deviation from standard Stokes-Einstein (SE) relation, a non-Gaussian parameter α2(Δt) for the distribution of particle displacement within a certain time interval Δt, a four-point susceptibility χ4(Δt,ΔL) for the correlation in dynamics between any two points in space separated by distance ΔL within some time window Δt, and a vector spatial-temporal correlation function Svec(R,Δt) for vector displacements within time interval Δt of particle pairs originally separated by R. By mapping the particle motion into a continuous-time random walk with constant jump length, we can obtain the average waiting time ⟨tx⟩∝Ds-1 and persistence time ⟨tp⟩∝η, with Ds the self-diffusion coefficient and η the shear viscosity, such that the observable λ=⟨tp⟩/⟨tx⟩∝Dsη can be calculated as a function of the control parameter τ to show how it deviates from its SE value λ0. Interestingly, we find λ/λ0 shows a nonmonotonic behavior for large volume fraction φa, wherein λ/λ0 undergoes a minimum at a certain intermediate value of τ, indicating that both small and large particle activity may lead to strong DH. Such a reentrance phenomenon is further demonstrated in terms of the non-Gaussian parameters α2, four-point susceptibility χ4, and vector spatiotemporal correlation functions Svec, respectively. Detail analysis shows that it is the competition between the dual roles of particle activity, namely, activity-induced higher effective temperature and activity-induced clustering, that leads to such nontrivial nonmonotonic behaviors. In addition, we find that DH may also show a maximum level at an intermediate value of φa if τ is large enough, implying that a more crowded system may be less heterogeneous than a less crowded one for a system with high particle activity. [ABSTRACT FROM AUTHOR]

We use a kinetic mode-coupling theory (MCT) combining with generalized Langevin equation (GLE) to study the diffusion and conformational dynamics of a bead-spring Rouse chain (RC) dissolved in porous media. The media contains fluid particles and immobile matrix ones wherein the latter leads to the lack of translational invariance. The friction kernel ζ (t) used in the GLE can be obtained directly by adopting a simple density-functional approach in which the density correlators calculated by MCT equations of porous media serve as inputs. Due to cage effects generated by surrounding particles, ζ (t) shows a very long tail memory in the high volume fraction of fluid and matrix. It is found that the long-time center-of-mass diffusion constant DCM of the RC decreases with the increment of volume fraction, influencing more strongly by the matrix particles than by the fluid ones. The auto-correlation function (ACF) of the end-to-end distance fluctuation can also be calculated theoretically based on GLE. Of particular interest is that the power-law region of ACF has a nearly fixed length in logarithmic scale when it shifts to longer time range, with increasing the volume fraction of media particles. Moreover, the effect of lack of translational invariance has been investigated by comparing the results between fluid-matrix and pure fluid cases under identical total volume fraction. [ABSTRACT FROM AUTHOR]