Catalytic diesel particulate filters reduce the in vitro estrogenic activity of diesel exhaust

An in vitro reporter gene assay based on human breast cancer T47D cells (ER-CALUX) was applied to examine the ability of diesel exhaust to induce or inhibit estrogen receptor (ER)-mediated gene expression. Exhaust from a heavy-duty diesel engine was either treated by iron- or copper/iron-catalyzed diesel particulate filters (DPFs) or studied as unfiltered exhaust. Collected samples included particle-bound and semivolatile constituents of diesel exhaust. Our findings show that all of the samples contained compounds that were able to induce ER-mediated gene expression as well as compounds that suppressed the activity of the endogenous hormone 17beta-estradiol (E2). Estrogenic activity prevailed over antiestrogenic activity. We found an overall ER-mediated activity of 1.63 +/- 0.31 ng E2 CALUX equivalents (E2-CEQs) per m(3) of unfiltered exhaust. In filtered exhaust, we measured 0.74 +/- 0.07 (iron-catalyzed DPF) and 0.55 +/- 0.09 ng E2-CEQ m(-3) (copper/iron-catalyzed DPF), corresponding to reductions in estrogenic activity of 55 and 66%, respectively. Our study demonstrates that both catalytic DPFs lowered the ER-mediated endocrine-disrupting potential of diesel exhaust.     

New method to determine the total carbonyl functional group content in extractable particulate organic matter by tandem mass spectrometry

A functional group analysis method was developed to determine the quantitative content of carbonyl functional groups in atmospheric particulate organic matter (POM) using constant neutral loss scanning-tandem mass spectrometry (CNLS-MS/MS). The neutral loss method consists in monitoring the loss of a neutral fragment produced by the fragmentation of a precursor ion in a collision cell. The only ions detected are the daughter ions resulting from the loss of the neutral fragment under study. Then, scanning the loss of a neutral fragment characteristic of a functional group enables the selective detection of the compounds bearing the chemical function under study within a complex mixture. The selective detection of carbonyl functional groups was achieved after derivatization with pentafluorophenylhydrazine (PFPH) by monitoring the neutral loss of C(6)F(5)N (181 amu), which was characteristic of a large panel of derivatized carbonyl compounds. The method was tested on 25 reference mixtures of different composition, all containing 24 carbonyl compounds at randomly determined concentrations. The repeatability and calibration tests were satisfying as they resulted in a relative standard deviation below 5% and a linear range between 0.01 and 0.65 mM with a calculated detection limit of 0.0035 mM. Also, the relative deviation induced by changing the composition of the mixture while keeping the total concentration of carbonyl functional groups constant was less than 20%. These reliability experiments demonstrate the high robustness of the developed procedure for accurate carbonyl functional group measurement, which was applied to atmospheric POM samples.     

Enthalpic relaxation of silica–polyvinyl acetate nanocomposites

Although the effects of filler nanoparticles size and surface treatment on the glass transition temperature of the matrix phase have received well-deserved attention, nanofiller effects on other physical parameters associated with the glass transition have received less interest. To better understand how the incorporation of nanofillers affects the enthalpic relaxations associated with the glass transition, differential scanning calorimeter measurements were carried out on silica–polyvinyl acetate nanocomposites with respect to filler content, annealing temperature, and annealing period. As expected, longer annealing periods below the glass transition temperature resulted in an increase of the subsequent enthalpic relaxations. However, the presence of filler substantially reduces the enthalpic relaxation relative to that of the neat polymer at longer annealing periods only. The underlying enthalpic relaxations and the effects suppressed by the fillers are specific to the annealing temperature. These results suggest a significant alteration in the physical state of the matrix because of the presence of the filler particles. However, this does not imply the existence of a glassy layer or layers with a glass transition gradient near the filler surface. © 2008 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 46: 2733–2740, 2008     

Insights into the Reinforcement of Butyl Rubber by Carbon Black and Silica with the Aid of Their Dynamic Properties

Carbon black (N234) and silica (Vulksail N) with a silane coupling agent Si-69 were chosen as reinforcing fillers in butyl rubber (IIR). The rheological behavior of the IIR compounds and the dynamic mechanical properties of IIR vulcanizates were investigated with a rubber processing analyzer and dynamic mechanical analysis (DMA) to examine the filler dispersion in the rubber matrix and the interaction between filler and matrix. The data indicated that the N234 filled IIR compounds had more filler networks than those filled with silica. Filler networks first appeared at 30 phr N234 and 45 phr silica with silane coupling agent Si-69. The interaction between N234 and IIR was far stronger than that between silica and IIR. However, the silica Vulksail N filled IIR had better wet-grip and lower rolling resistance compared to the carbon black-filled IIR should IIR be chosen as a substitute of styrene-butadiene rubber (SBR) in tire tread. The reinforcing factor, R, R (related to the difference in tan d peak height at T_g for the filled and nonfilled rubbers), also demonstrated that the N234-IIR interaction was stronger than for the silica. IIR with 30 phr N234 exhibited the largest tensile strength, 20.1 MPa, for those vulcanizates examined. The tensile and tear strengths of N234 filled IIR were higher than those of IIR with similar amounts of silica. Thus, it was concluded that N234 is a more active reinforcing filler in IIR than silica (Vulksail N) even with a silane coupling agent (Si-69).     

The influence of particle distribution and interphase on the thermal expansion coefficient of particulate composites by the use of a new model

In this work, the thermal expansion coefficient (CTE) of a composite containing spherical particles surrounded by an inhomogeneous interphase embedded in an isotropic matrix is evaluated by means of a new model. The thermomechanical properties of the interphase are formulated as continuous radial functions. It is assumed that this third phase developed between the polymeric matrix and the filler particles contains both areas of absorption interaction in polymer surface layers onto filler particles as well as areas of mechanical imperfections. It can be said that the concept of boundary interphase is a useful tool to describe quantitatively the adhesion efficiency between matrix and particles and that there is an effect of this phase on the thermomechanical properties of the composite. The thickness and volume fraction of this phase were determined from heat capacity measurements for various filler contents. On the other hand, it is assumed that the particle arrangement (distribution) which can be considered as an influence of neighboring inclusions and their interaction should affect the thermomechanical constants of the composite. The theoretical predictions were compared with experimental results as well as with theoretical values from expressions obtained from other workers and they were found to be in satisfactory agreement.     

Opinion strength influences the spatial dynamics of opinion formation

Opinions are rarely binary; they can be held with different degrees of conviction, and this expanded attitude spectrum can affect the influence one opinion has on others. Our goal is to understand how different aspects of influence lead to recognizable spatio-temporal patterns of opinions and their strengths. To do this, we introduce a stochastic spatial agent-based model of opinion dynamics that includes a spectrum of opinion strengths and various possible rules for how the opinion strength of one individual affects the influence that this individual has on others. Through simulations, we find that even a small amount of amplification of opinion strength through interaction with like-minded neighbors can tip the scales in favor of polarization and deadlock.     

Models for the Investigation of the Structure of Vulcanizates

For the investigation of vulcanizates structure, new models have been developed relating the modulus of vulcanizates and the volume fraction of filler. The parameters of the models are the filler distribution and the properties of the shell surrounding the filler particles. Three functions of filler distribution have been assumed. Dynamic mechanical properties of a series of vulcanizates were tested and the parameters of the models were computed. The obtained data allows estimating the properties of the shell formed around filler particles in vulcanizates and to distinguish the functions of filler distribution.     

On the sensitive dynamical system and the transition from the apparently deterministic process to the completely random process

The detailed analysis of the dynamical process of coin tossing is made. Through calculations, it is illustrated how and why the result is extremely sensitive to the initial conditions. It is also shown that, as the initial height of the mass center of the coin increases, the final configuration, i.e. head or tail, becomes more and more sensitive to the initial parameters (the initial velocity angular velocity, and the initial orientation), the coefficient of the air drag, and the energy absorption factor of the surface on which the coin bounces. If we keep the head upward initially but allow a small range for the change of some other initial parameters, the frequency that the final configuration is head, would be 1 if the initial height h of the mass center is sufficiently small, and would be clo to 1/2 if h is sufficiently large. An interesting question is how this frequency changes continuously from 1 to 1/2 as h increases. Detailed calculations show that such a transition is very similar to the transition from laminar to turbulent flows. A basic difference between the transition stage and the completely random stage is indicated: In the completely random stage, the deterministic process of the individual case is extremely sensitive to the initial conditions and the dynamical parameters, out the statistical properties of the ensemble are insensitive to the small changes of the initial conditions and the dynamical parameters. On the contrary, in the transition stage, both the deterministic process of the individual case and the statistical properties of the ensemble are sensitive to the initial conditions and the dynamical parameters. The mechanism for this feature of the transition stage is the existence of the long-train structure in the parameter space. The illuminations of this analysis on some other random phenomena are discussed.     

A Learning Algorithm for the Control of Continuous Action Set-Point Regulator Systems

The convergence properties for reinforcement learning approaches, such as temporal differences and Q-learning, have been established under moderate assumptions for discrete state and action spaces. In practice, however, many systems have either continuous action spaces or a large number of discrete elements. This paper presents an approximate dynamic programming approach to reinforcement learning for continuous action set-point regulator problems, which learns near-optimal control policies based on scalar performance measures. The continuous-action space (CAS) algorithm uses derivative-free line search methods to obtain the optimal action in the continuous space. The theoretical convergence properties of the algorithm are presented. Several heuristic stopping criteria are investigated and practical application is illustrated by two example problems—the inverted pendulum balancing problem and the power system stabilization problem.     

ChatGPT在计算结构力学领域的应用初探与浅析

OpenAI开发的人工智能聊天机器人ChatGPT自发布以来,受到了社会各界的广泛关注。凭借卓越的分析计算能力和良好的开放性,该模型已作为一种全新服务信息检索和问题解决的助手。平面桁架结构作为结构力学中的常见结构,在工程实践中通常采用刚度法编写计算程序;然而,初学者或缺乏力学背景的研究人员难以高质量地快速完成编程任务。本文首先简要介绍了ChatGPT的原理及应用,然后以使用ChatGPT快速生成平面桁架计算程序为例,探索并展示了ChatGPT辅助完成计算结构力学程序编制的卓越性能与具体使用方法,最后讨论了以ChatGPT为代表的生成式人工智能在计算结构力学程序开发中的应用和挑战。