New directions in perfluoroalkyl sulfonic acid–based proton-exchange membranes

Perfluoroalkyl sulfonic acid (PFSA) polymer membranes have enabled a variety of electrochemical energy storage and conversion devices such as fuel cells and flow batteries and material conversion devices such as electrolyzers to produce hydrogen and chlorine. For decades, a small group of simple copolymers were relied on in these applications. The practical limits for conductivity through lower equivalent weight are being approached for these polymers. Recently, the community has recognized the need for new PFSA ionomers with properties tailored to the specific needs of each application. An emerging area of importance is the manipulation of the diffusion properties of these polymers. For example, fuel cell membranes need to maintain good barrier properties as thicknesses are reduced, while ionomers with increased oxygen diffusion rates are sought for fuel cell electrodes. In recent years, the class of PFSA ionomers has expanded to include new side chain structures, new comonomers, and even hydrocarbon hybrid structures.     

The effective geometry Monte Carlo algorithm: Applications to molecular communication

In this work, we address the systematic biases and random errors stemming from finite step sizes encountered in diffusion simulations. We introduce the Effective Geometry Monte Carlo (EG-MC) simulation algorithm which modifies the geometry of the receiver. We motivate our approach in a 1D toy model and then apply our findings to a spherical absorbing receiver in a 3D unbounded environment. We show that with minimal computational cost the impulse response of this receiver can be precisely simulated using EG-MC. Afterwards, we demonstrate the accuracy of our simulations and give tight constraints on the single free parameter in EG-MC. Finally, we comment on the range of applicability of our results. While we present the EG-MC algorithm for the specific case of molecular diffusion, we believe that analogous methods with effective geometry manipulations can be utilized to approach a variety of problems in other branches of physics such as condensed matter physics and cosmological large scale structure simulations.     

Hexagonal boron nitride quantum dots: Properties,preparation and applications

As a new type of quantum dots (QDs), hexagonal boron nitride quantum dots (BNQDs) exhibit promising potential in the applications of disease diagnosis, fluorescence imaging, biosensing, metal ion detection, and so on, because of their remarkable chemical stability, excellent biocompatibility, low cytotoxicity, and outstanding photoluminescence properties. However, the large-scale fabrication of homogeneous BNQDs still remains challenging. In this article, the properties and common fabrication methods of BNQDs are summarized based on the recent research progress. Then, the corresponding yields, morphologies, and fabrication mechanisms of these as-obtained BNQDs are discussed in detail. Moreover, the applications of these as-obtained BNQDs in different fields are also discussed. This article is expected to inspire new methods and improvements to achieve large-scale fabrication of homogeneous BNQDs, which will enable their practical applications in future.     

利用混合FE-SEA方法的前围隔声性能优化设计*

为了提升某重型商用车前围的隔声性能,建立了用于分析前围传递损失的有限元-统计能量分析(FE-SEA)模型。针对前围结构复杂的特点,依据FE-SEA模型建模原则,提出了通过在表面创建声腔来确保能量在模型中的正确传递路径。将仿真结果与测试值对比,二者误差小于1.6 dB(A),验证了FE-SEA方法的准确性。用吸声材料与隔声材料复合设计前围声学包,采用正交试验法对前围声学包进行优化设计并对各个试验方案进行仿真计算。对仿真结果进行极差分析与方差分析,选出了在传递损失、重量和厚度三方面达到最佳平衡的声学包:毛毡(10 mm)+EPDM隔声垫(2 mm)。结果表明,优化后的前围传递损失在测试频率315 Hz～2000 Hz范围内最小提升了3.8 dB(A),最大提升了7 dB(A),前围的隔声性能得到较大的提升。     

Markov processes conditioned on their location at large exponential times

Suppose that ${\left(X_t\right)}_{t\ge 0}$ is a one-dimensional Brownian motion with negative drift $?\mu$. It is possible to make sense of conditioning this process to be in the state 0 at an independent exponential random time and if we kill the conditioned process at the exponential time the resulting process is Markov. If we let the rate parameter of the random time go to 0, then the limit of the killed Markov process evolves like $X$ conditioned to hit 0, after which time it behaves as $X$ killed at the last time $X$ visits 0. Equivalently, the limit process has the dynamics of the killed “bang–bang” Brownian motion that evolves like Brownian motion with positive drift $+\mu$ when it is negative, like Brownian motion with negative drift $?\mu$ when it is positive, and is killed according to the local time spent at 0.An extension of this result holds in great generality for a Borel right process conditioned to be in some state $a$ at an exponential random time, at which time it is killed. Our proofs involve understanding the Campbell measures associated with local times, the use of excursion theory, and the development of a suitable analogue of the “bang–bang” construction for a general Markov process.As examples, we consider the special case when the transient Borel right process is a one-dimensional diffusion. Characterizing the limiting conditioned and killed process via its infinitesimal generator leads to an investigation of the $h$-transforms of transient one-dimensional diffusion processes that goes beyond what is known and is of independent interest.     

A time-series approach to measuring node similarity in networks and its application to community detection

A central concept in network analysis is that of similarity between nodes. In this paper, we introduce a dynamic time-series approach to quantifying the similarity between nodes in networks. The problem of measuring node similarity is exquisitely embedded into the framework of time series for state evolution of nodes. We develop a deterministic parameter-free diffusion model to drive the dynamic evolution of node states, and produce a unique time series for each source node. Then we introduce a measure quantifying how far all the other nodes are located from each source one. Following this measure, a quantity called dissimilarity index is proposed to signify the extent of similarity between nodes. Thereof, our dissimilarity index gives a deep and natural integration between the local and global perspectives of topological structure of networks. Furthermore, we apply our dissimilarity index to unveil community structure in networks, which verifies the proposed dissimilarity index.