Effects of the joining process on the microstructure and properties of liquid-phase-sintered SiC-SiC joints formed with Ti foil

The joining of liquid-phase sintered SiC (LPS-SiC) ceramics was conducted using spark plasma sintering (SPS), through solid state diffusion bonding, with Ti-metal foil as a joining interlayer. Samples were joined at 1400 °C, under applied pressures of either 10 or 30 MPa, and with different atmospheres (argon, Ar, vs. vacuum). It was demonstrated that the shear strength of the joints increased with an increase in the applied joining pressure. The joining atmosphere also affected on both the microstructure and shear strength of the SiC joints. The composition and microstructure of the interlayer were examined to understand the mechanism. As a result, a SiC-SiC joining with a good mechanical performance could be achieved under an Ar environment, which in turn could provide a cost-effective approach and greatly widen the applications of SiC ceramic components with complex shape.     

Patterns in environmental priorities revealed through government open data portals

The ways in which environmental priorities are framed are varied and influenced by political forces. One technological advance--the proliferation of government open data portals (ODPs)--has the potential to improve governance through facilitating access to data. Yet it is also known that the data hosted on ODPs may simply reflect the goals and interests of multiple levels of political power. In this article, I use traditional statistical correlation and regression techniques along with newer natural language processing and machine learning algorithms to analyze the corpus of datasets hosted on government ODPs (total: 49,066) to extract patterns that relate scales of governance and political liberalism/conservatism to the priorities and meaning attached to environmental issues. I find that state-level and municipal-level ODPs host different categories of environmental datasets, with municipal-level ODPs generally hosting more datasets pertaining to services and amenities and state-level ODPs hosting more datasets pertaining to resource protection and extraction. Stronger trends were observed for the influences of political conservatism/liberalism among state-level ODPs than for municipal-level ODPs.     

Spirobifluorene Dimers: Understanding How The Molecular Assemblies Drive The Electronic Properties

Spirobifluorene (SBF) is one of the most important scaffolds used in the design of organic semi-conductors (OSCs) for electronics. In recent years, among all the structures developed for these applications, SBF dimers have been highlighted due to their great potential in thermally activated delayed fluorescence and in phosphorescent organic light-emitting diodes. Attaching two SBF units generate 10 dimers, each possessing its own structural specificity, which in turn drives its electronic properties. These ten SBF dimers are gathered herein. Understanding how the molecular assembly determines the electronic properties has been one of the pillars of organic electronics. This is the goal of this article. As positional isomerism is a key tool to design OSCs, defining the design guidelines for the SBF scaffold appears of interest for the future of this building block. Herein, the importance of the two main parameters involved in the electrochemical and photophysical properties, namely the nature of the phenyl linkages and the steric congestion between the two SBF units is discussed. The combination of these two parameters drives the electronic properties but their respective weight is different as a function of the regioisomer involved or of the property considered (frontier orbitals energy level, absorption, fluorescence, phosphorescence).     

Description of the thermodynamic properties and fluid-phase behavior of aqueous solutions of linear,branched, and cyclic amines

The SAFT-γ Mie group-contribution equation of state is used to represent the fluid-phase behavior of aqueous solutions of a variety of linear, branched, and cyclic amines. New group interactions are developed in order to model the mixtures of interest, including the like and unlike interactions between alkyl primary, secondary, and tertiary amine groups (NH₂, NH, N), cyclic secondary and tertiary amine groups (cNH, cN), and cyclic methine-amine groups (cCHNH, cCHN) with water (H₂O). The group-interaction parameters are estimated from appropriate experimental thermodynamic data for pure amines and selected mixtures. By taking advantage of the group-contribution nature of the method, one can describe the fluid-phase behavior of mixtures of molecules comprising those groups over broad ranges of temperature, pressure, and composition. A number of aqueous solutions of amines are studied including linear, branched aliphatic, and cyclic amines. Liquid–liquid equilibria (LLE) bounded by lower critical solution temperatures (LCSTs) have been reported experimentally and are reproduced here with the SAFT-γ Mie approach. The main feature of the approach is the ability not only to represent accurately the experimental data employed in the parameter estimation, but also to predict the vapor–liquid, liquid–liquid, and vapor–liquid–liquid equilibria, and LCSTs with the same set of parameters. Pure compound and binary phase diagrams of diverse types of amines and their aqueous solutions are assessed in order to demonstrate the main features of the thermodynamic and fluid-phase behavior.     

自重湿陷性黄土地基预钻孔浸水处理的可靠度设计方法

为解决传统预浸水法存在的浸水时间长、浸水处理范围难以确定等不足,基于土体中水分运移规律,依据可靠度理论、极限状态设计方法及复合 Poisson 过程原理,提出一种消除黄土湿陷性的处理浸水方法——预钻孔浸水法。给出了利用预钻孔浸水法对自重湿陷性黄土地基进行浸水时,水平向及竖直向浸水影响范围的确定模型;在此基础上结合达西定律给出了浸水孔设计参数如孔深、孔间距及浸水孔个数的确定方法。结合铜川某工程,设计进行了现场预钻孔浸水试验,对该方法的合理性进行了验证,并通过现场钻探、现场勘探、室内湿陷性试验等方法对该方法的处理效果进行了评价。该浸水方法具有浸水时间短、浸水影响范围可根据浸水孔布设进行控制等优点,且浸水处理效果良好,完全符合施工要求。     

An active balancer with rapid bidirectional charge shuttling and adaptive equalization current control for lithium-ion battery strings

This article proposes an active balancer, which features bidirectional charge shuttling and adaptive equalization current control, to fast counterbalance the state of charge (SOC) of cells in a lithium-ion battery (LIB) string. The power circuit consists of certain bidirectional buck-boost converters to transfer energy among the different cells back and forth. Owing to the characterization of the open-circuit voltage (OCV) vs SOC in LIB being relatively smooth near the SOC middle range, the SOC-inspected balance strategy can achieve more precise and efficient equilibrium than the voltage-based control. Accordingly, a compensated OCV-based SOC estimation is put forward to take into account the discrepancy of SOC estimation. Besides, the varied-duty-cycle (VDC) and curve-fitting modulation (CFM) methods are devised herein to tackle the problems of slow equalization rate and low balance efficacy, which arise from the diminution in balancing current as the SOC difference between the cells decreases in the later duration of equalization especially. The proposed strategies have taken the battery nonlinear characteristic and circuit parameter nonideality into account and can adaptively modulate the duty cycle with the SOC difference to keep balancing current constant throughout the balancing cycle. Simulated and experimental results are given to demonstrate the feasibility and effectiveness of the same prototype constructed. Compared with the fixed duty cycle and the VDC methods, the proposed CFM has the best balancing efficiency of 81.4%, and the balance time is shortened by 27.1% and 18.6%, respectively.     

Application of robust machine learning methods to modeling hydrogen solubility in hydrocarbon fuels

Having accurate information about the hydrogen solubility in hydrocarbon fuels and feedstocks is very important in petroleum refineries and coal processing plants. In the present work, extreme gradient boosting (XGBoost), multi-layer perceptron (MLP) trained with Levenberg–Marquardt (LM) algorithm, adaptive boosting support vector regression (AdaBoost?SVR), and a memory-efficient gradient boosting tree system on adaptive compact distributions (LiteMORT) as four novel machine learning methods were used for estimating the hydrogen solubility in hydrocarbon fuels. To achieve this goal, a database containing 445 experimental data of hydrogen solubilities in 17 various hydrocarbon fuels/feedstocks was collected in wide-spread ranges of operating pressures and temperatures. These hydrocarbon fuels include petroleum fractions, refinery products, coal liquids, bitumen, and shale oil. Input parameters of the models are temperature and pressure along with density at 20 °C, molecular weight, and weight percentage of carbon (C) and hydrogen (H) of hydrocarbon fuels. XGBoost showed the highest accuracy compared to the other models with an overall mean absolute percent relative error of 1.41% and coefficient of determination (R²) of 0.9998. Also, seven equations of state (EOSs) were used to predict hydrogen solubilities in hydrocarbon fuels. The 2- and 3-parameter Soave-Redlich-Kwong EOS rendered the best estimates for hydrogen solubilities among the EOSs. Moreover, sensitivity analysis indicated that pressure owns the highest influence on hydrogen solubilities in hydrocarbon fuels and then temperature and hydrogen weight percent of the hydrocarbon fuels are ranked, respectively. Finally, Leverage approach results exhibited that the XGBoost model could be well trusted to estimate the hydrogen solubility in hydrocarbon fuels.     

Solution plasma-assisted preparation of highly dispersed NiMnAl-LDO catalyst to enhance low-temperature activity of CO2 methanation

In this work, the solution plasma-assisted method was used to prepare NiMnAl-LDO (layered double oxides) catalysts with different treatment times, which were used for the CO₂ methanation reaction. Solution plasma treatment can enhance the dispersibility of the catalyst, create oxygen defects and improve the chemical adsorption capacity of the catalyst. The results show that the low-temperature activity of the catalyst has been improved after the solution plasma treatment. We demonstrate that the NiMnAl-LDO-P(20) catalyst with high dispersion has the highest catalytic activity in CO₂ methanation (81.3% CO₂ conversion and 96.7% CH₄ selectivity at 200 °C). Even though working for 70 h, the catalyst is still highly stable. This work provides a great promise for improving the low-temperature activity of Ni-based catalysts.     

Effect of fiber-reinforcement on the mechanical behavior of sand approaching the critical state

Several types of ground improvement methods that employ fiber-reinforcement have been developed in recent years. A series of consolidated drained triaxial compression tests has been conducted here to examine the effect of short fibers on the mechanical properties of Toyoura sand. Sand with 0%, 0.2%, 0.4%, and 1% fiber contents, prepared to yield random distribution, was sheared under several confining pressures and controlled via their initial relative densities. The test results showed that the maximum and residual deviatoric stresses increased, whereas the volumetric expansion decreased with an increase in fiber content. Although the stress ratio η (=q/p′) and specific volume changed depending on the fiber content and confining pressure with shear progression, they each reached the same values for a definite fiber content at the end of shearing, independent of initial relative density. In other words, the unique critical state line can be found for a definite fiber content. Moreover, the greater the fiber content, the larger the slope of the critical state line at the end of shearing. Additionally, as the length of fibers shortened with the same percentage of fiber inclusions in sand, the deviatoric stress and the stress ratio decreased, approaching the shear-strain-volumetric response of unreinforced sand.