Effect of additives on microstructure of coal-based graphite

The Taixi anthracite was used as the raw materials, and mixed with different masses of additives, namely silicon oxide, titanium oxide, and iron oxide, to prepare the coal-based graphite by high temperature graphitization. The microstructure of coal-based graphite was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), laser confocal Raman spectroscopy (Raman) and Specific surface area and porosity analyzer.The results show that the graphitization degree of the coal-based graphite can reach over 89% after high temperature heat treatment at 2800 °C , which significantly improves the microcrystalline structure of anthracite and achieves orderly rearrangement of sp2 hybrid carbon atoms in the coal. Under the same additive mixing level, the graphitization degree and stacking height of coal-based graphite with titanium dioxide as additive are relatively high, the difference between the layer spacing and the ideal graphite layer spacing is the smallest, and the degree of ordering of carbon materials is the highest. The Raman spectroscopy results showed that the order degree of coal -based graphite prepared under different additives was significantly different, and the order degree of TXSC3, TXTC2 and TXIC3 coal-based graphite was the highest among the additives. Under the electron microscope, it is found that under the conditions of three additives, the scales, spherical and two shapes of coal-based graphite can be prepared separately. It can be seen from the specific surface area and pore size distribution data of coal-based graphite that they have similar low-temperature nitrogen adsorption-desorption isotherms

The Optimal Selection for Restricted Linear Models with Average Estimator

The essential task of risk investment is to select an optimal tracking portfolio among various portfolios. Statistically, this process can be achieved by choosing an optimal restricted linear model. This paper develops a statistical procedure to do this, based on selecting appropriate weights for averaging approximately restricted models. The method of weighted average least squares is adopted to estimate the approximately restricted models under dependent error setting. The optimal weights are selected by minimizing a k-class generalized information criterion (k-GIC), which is an estimate of the average squared error from the model average fit. This model selection procedure is shown to be asymptotically optimal in the sense of obtaining the lowest possible average squared error. Monte Carlo simulations illustrate that the suggested method has comparable efficiency to some alternative model selection techniques

Quantile frequency connectedness between energy tokens, crypto market, and renewable energy stock markets

Utilizing a quantile frequency connectedness approach, we explore the connectedness between energy tokens, crypto market, and renewable energy stock markets. The empirical results show that the connectedness measures of the series are characterized by asymmetry and heterogeneity across quantiles and different investment horizons. Specifically, the characteristic of clustering has been observed that energy tokens and crypto market are more interconnected, while the renewable energy stock markets are more interconnected with each other at median quantile. The linkages between energy tokens and renewable energy stock markets are quite weak under normal market conditions, suggesting the diversification opportunities in investing these financial assets. However, these series are more interconnected under extreme market conditions, with the renewable energy stock markets are on the dominating end of the propagation mechanism while the energy tokens and crypto market are net receivers of shocks. Further frequency decomposition shows that this strategy can hold in the short term, while in the long term investors could benefit from the diversification opportunities by investing both kinds of financial assets. Additionally, the dynamic analysis affirms that the connectedness measures are varied and event-dependent over time. Our results may help investors and policymakers have a better assessment and portfolio management

Are the systemic risk spillovers of good and bad volatility in oil and global equity markets alike?

This paper explores the asymmetric connectedness of systemic risk between the oil and global stock markets in both the time and frequency domains. To do so, we introduce time-varying parametric vector autoregressive (TVP-VAR) spillover index models and implied volatility indices to examine risk spillover under bad and good volatility changes. The results reveal that the oil market and global stock markets are highly risk-connected. The risk spillover from increases in oil-stock volatility is greater than that from decreases in volatility, showing an apparent asymmetric phenomenon. Additionally, asymmetric risk spillovers are caused by volatility in the oil market. In particular, the ability to predict systemic risk driven by bad and good volatility changes is enhanced during extreme market periods. Furthermore, we observe that the risk spillovers between the oil and global stock markets under bad and good volatility changes are more pronounced in the short term and that risk spillovers are still dominated by bad volatility in each period. The oil market has a significant risk spillover effect under bad volatility changes in all periods, while it only plays a strong risk spillover role under good volatility changes in the medium and long term. Finally, trading strategies based on minimum connectedness portfolios significantly reduce asset volatility. These findings can aid investors in optimizing their portfolios according to market conditions and provide recommendations to relevant policymakers

Stability Prediction of Milling Process with Variable Pitch Cutter

The use of variable pitch cutter is a known means to increase the stable limit depth of cut by disrupting the regenerative effect. In this paper, an improved semidiscretization algorithm is presented to predict the stability lobes for variable pitch cutters. Modeling efforts develop a straightforward analytical integral force model that can cover any case of piecewise continuous cutting regions regarding the helix angle. The proposed approach has been verified with the comparisons with prior works, time domain simulations, and cutting tests. In addition, the method is also applied to examine the effect of the tool geometries on the stability trends for variable pitch milling. Some new phenomena for certain combinations of parameters are shown and explained

Influence of Electrostatic Force Nonlinearity on the Sensitivity Performance of a Tapered Beam Micro-Gyroscope Based on Frequency Modulation

Electrostatic force nonlinearity is widely present in MEMS systems, which could impact the system sensitivity performance. The Frequency modulation (FM) method is proposed as an ideal solution to solve the problem of environmental fluctuation stability. The effect of electrostatic force nonlinearity on the sensitivity performance of a class of FM micro-gyroscope is investigated. The micro-gyroscope consists of a tapered cantilever beam with a tip mass attached to the end. Considering the case of unequal width and thickness, the motion equations of the system are derived by applying Hamilton’s principle. The differential quadrature method (DQM) was used to analyze the micro-gyroscope’s static deflection, pull-in voltage, and natural frequency characteristics. We observed that from the onset of rotation, the natural frequencies of the drive and sense modes gradually split into a pair of natural frequencies that were far from each other. The FM method directly measures the angular velocity by tracking the frequency of the drive and sense modes. Then, based on the linear system, the reduced-order model was used to analyze the influence of the shape factor and DC voltage on the sensitivity performance. Most importantly, the nonlinear frequency of system was obtained using the invariant manifold method (IMM). The influence of electrostatic force nonlinearity on the performance of the FM micro-gyroscope was investigated. The results show that the different shape factors of width and thickness, as well as the different DC voltages along the drive and sense directions, break the symmetry of the micro-gyroscope and reduce the sensitivity of the system. The sensitivity has a non-linear trend with the rotation speed. The DC voltage is proportional to the electrostatic force nonlinearity coefficient. As the DC voltage gradually increases, the nonlinearity is enhanced, resulting in a significant decrease in the sensitivity of the micro-gyroscope. It is found that the negative shape factor (width and thickness gradually increase along the beam) can effectively restrain the influence of electrostatic force nonlinearity, and a larger dynamic detection range can be obtained

Improvement of leaching efficiency of cathode material of spent LiNixCoyMnzO2LiNi_xCo_yMn_zO_2 lithium-ion battery by the in-situ thermal reduction

Green cars and electronic products consume lots of lithium-ion batteries (LIBs), and massive spent LIBs are yielded due to performance degradation. This paper provides an economical and environmentally friendly approach to recover valuable metals from cathode materials of the spent LIBs. It combines the in-situ thermal reduction (self-reduction by polyvinylidene fluoride (PVDF) and residual electrolyte in cathode material) and sulfuric acid leaching. Elements of high valent are reduced by the binder (PVDF) and the residual electrolyte on the surface of $NCM(LiNi_xCo_yMn_{1-x-y}O_2)$ material at high temperatures. Moreover, the changes in substance type, element valency, and contents of cathode materials reduced with various terminal temperatures and retention time are analyzed by Xray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). Results show that the optimal terminal temperature for in-situ thermal reduction is 600 °C, and the optimum retention time is 120 min. Under the best in-situ thermal reduction conditions, the results from XRD confirm that part of $Ni^{2+}$ is converted to simple substance $Ni$, $Co^{3+}$ is reduced to $Co$, and $Mn^{4+}$ is reduced to $Mn^{2+}$ and elemental $Mn$, which are confirmed by XRD. Analyzed results by XPS indicate that the content of $Ni^{2+}$ decreases to 67.05%, and $Co^{3+}$ is completely reduced to $Co$. $Mn^{4+}$ is reduced to 91.41% of $Mn^{2+}$ and 8.59% of simple substance $Mn$. In-situ thermal reduction benefits the leaching processes of cathode materials. The leaching efficiencies of $Ni$, $Co$, and $Mn$ increase from 53.39%, 51.95%, and 0.71% to 99.04%, 96.98%, and 97.52%, respectively

Escherichia coli templated iron oxide biomineralization under oscillation

Motility is significant in organisms. Studying the influence of motility on biological processes provides a new angle in understanding the essence of life. Biomineralization is a representative process for organisms in forming functional materials. In the present study, we investigated the biomineralization of iron oxides templated by Escherichia coli (E. coli) cells under oscillation. The formation of iron oxide minerals with acicular and banded morphology was observed. The surface charge of E. coli cells contributed to the biomineralization process. The surface components of E. coli cells including lipids, carbohydrates and proteins also have roles in regulating the formation and morphology of iron oxide minerals. As-prepared mineralized iron oxide nanomaterials showed activity in photocatalytic degradation of methylene blue as well as in electrocatalytic hydrogen evolution reaction. This study is helpful not only in understanding motility in biological processes, but also in developing techniques for fabricating functional nanomaterials