A composition-free parallel volume rendering method

Journal of Visualization - In the in situ visualization of large-scale simulation, if using the traditional sort-last parallel rendering method, the performance cannot be fully improved due to the...     

Insight into room-temperature catalytic oxidation of NO by CrO2(110): A DFT study

The first-principles DFT calculations together with microkinetic analysis reveal the complex catalytic mechanism of low-content NO oxidation on CrO₂(110) at room temperature. It quantitatively makes clear that CrO₂(110) can exhibit considerable activity with the Mars-van-Krevelen mechanism preferred, and the nitrate species serves as the key poisoning species.     

Nonlinear vibration analysis in precision motion stage with PID and time-delayed feedback controls

Nonlinear Dynamics - We investigate the control of friction-induced vibration in a precision motion stage under the effect of the LuGre friction dynamics. We consider a lumped parameter model of...     

Decorating Single-Atomic Mn Sites with FeMn Clusters to Boost Oxygen Reduction Reaction

The regulation of electron distribution of single-atomic metal sites by atomic clusters is an effective strategy to boost their intrinsic activity of oxygen reduction reaction (ORR). Herein we report the construction of single-atomic Mn sites decorated with atomic clusters by an innovative combination of post-adsorption and secondary pyrolysis. The X-ray absorption spectroscopy confirms the formation of Mn sites via Mn-N₄ coordination bonding to FeMn atomic clusters (FeMn_ac/Mn-N₄C), which has been demonstrated theoretically to be conducive to the adsorption of molecular O₂ and the break of O−O bond during the ORR process. Benefiting from the structural features above, the FeMn_ac/Mn-N₄C catalyst exhibits excellent ORR activity with half-wave potential of 0.79 V in 0.5 M H₂SO₄ and 0.90 V in 0.1 M KOH as well as preeminent Zn-air battery performance. Such synthetic strategy may open up a route to construct highly active catalysts with tunable atomic structures for diverse applications.     

Grain Boundary Electronic Insulation for High-Performance All-Solid-State Lithium Batteries

Sulfide electrolytes with high ionic conductivities are one of the most highly sought for all-solid-state lithium batteries (ASSLBs). However, the non-negligible electronic conductivities of sulfide electrolytes (≈10⁻⁸ S cm⁻¹) lead to electron smooth transport through the sulfide electrolyte pellets, resulting in Li dendrite directly depositing at the grain boundaries (GBs) and serious self-discharge. Here, a grain-boundary electronic insulation (GBEI) strategy is proposed to block electron transport across the GBs, enabling Li−Li symmetric cells with 30 times longer cycling life and Li−LiCoO₂ full cells with three times lower self-discharging rate than pristine sulfide electrolytes. The Li−LiCoO₂ ASSLBs deliver high capacity retention of 80 % at 650 cycles and stable cycling performance for over 2600 cycles at 0.5 mA cm⁻². The innovation of the GBEI strategy provides a new direction to pursue high-performance ASSLBs via tailoring the electronic conductivity.     

等级孔微孔-介孔Fe2O3/ZSM-5中空分子筛催化材料的制备及催化苄基化性能

ZSM-5分子筛具有极其均匀的孔道结构、 良好的形状选择性和催化活性及耐水热稳定性, 是一种高效、 绿色的固体催化剂, 被广泛应用于石油催化裂化、 精细化工和环境保护等领域. 但其单一的微孔结构大大降低了客体分子的流通扩散性, 导致由大分子参与的芳烃烷基化反应受到极大限制. 本文采用NaOH/四丙基氢氧化铵(TPAOH)混合碱处理微孔ZSM-5, 制备了具备高结晶度、 高比表面积的等级孔微孔-介孔ZSM-5中空分子筛材料, 该材料在保持微孔孔道良好水热稳定性和大量活性中心的同时, 还通过介孔的引入进一步促进反应物及产物的扩散, 使间三甲苯苄基化反应的转化率提高了3.8倍. 通过在等级孔微孔-介孔ZSM-5中空材料上负载Fe, 开发出了具有双功能的等级孔微孔-介孔Fe₂O₃/ZSM-5中空催化剂, 该催化剂在苯的苄基化反应中表现出优异的催化性能, 当Fe负载量(质量分数)为6.67%, 反应温度为75 ℃, 反应时间为15 min时, 转化率高达98.3%, 选择性为81.6%, 最终收率达到80.2%.     

Optimal Randomness for Stochastic Configuration Network (SCN) with Heavy-Tailed Distributions

Stochastic Configuration Network (SCN) has a powerful capability for regression and classification analysis. Traditionally, it is quite challenging to correctly determine an appropriate architecture for a neural network so that the trained model can achieve excellent performance for both learning and generalization. Compared with the known randomized learning algorithms for single hidden layer feed-forward neural networks, such as Randomized Radial Basis Function (RBF) Networks and Random Vector Functional-link (RVFL), the SCN randomly assigns the input weights and biases of the hidden nodes in a supervisory mechanism. Since the parameters in the hidden layers are randomly generated in uniform distribution, hypothetically, there is optimal randomness. Heavy-tailed distribution has shown optimal randomness in an unknown environment for finding some targets. Therefore, in this research, the authors used heavy-tailed distributions to randomly initialize weights and biases to see if the new SCN models can achieve better performance than the original SCN. Heavy-tailed distributions, such as Lévy distribution, Cauchy distribution, and Weibull distribution, have been used. Since some mixed distributions show heavy-tailed properties, the mixed Gaussian and Laplace distributions were also studied in this research work. Experimental results showed improved performance for SCN with heavy-tailed distributions. For the regression model, SCN-Lévy, SCN-Mixture, SCN-Cauchy, and SCN-Weibull used less hidden nodes to achieve similar performance with SCN. For the classification model, SCN-Mixture, SCN-Lévy, and SCN-Cauchy have higher test accuracy of 91.5%, 91.7% and 92.4%, respectively. Both are higher than the test accuracy of the original SCN.     

The minmax regret gradual covering location problem on a network with incomplete information of demand weights

The gradual covering location problem seeks to establish facilities on a network so as to maximize the total demand covered, allowing partial coverage. We focus on the gradual covering location problem when the demand weights associated with nodes of the network are random variables whose probability distributions are unknown. Using only information on the range of these random variables, this study is aimed at finding the “minmax regret” location that minimizes the worst-case coverage loss. We show that under some conditions, the problem is equivalent to known location problems (e.g. the minmax regret median problem). Polynomial time algorithms are developed for the problem on a general network with linear coverage decay functions.