Magnetic properties and magnetoresistance effect of SnFe2O4 spinel nanoparticles: Experimental,ab initio and Monte Carlo simulation

In this contribution, SnFe₂O₄ nanoparticles were prepared by the solvothermal method, the structural properties were performed using X-Ray Diffraction (DRX) to prove the success of tin ferrite formation and to determine de crystals parameters. The size and morphological study were build using Scanning Electron Microscopy (SEM) and Transmission Electron microscopy (TEM), the results showed that the size of particles is uniform with a range of particles (5–7 nm). The magnetic properties were carried out using the SQUID device, the SnFe2O4 nanoparticles have a magnetic transition at 750 K. In addition, the hysteresis loops at low temperature displayed Ms and Mr equals to 23 emu/g and 6 emu/g, respectively. The magnetoresistance properties were investigated, the SnFe₂O₄ nanoparticles present a large magnetoresistance effect (80%). The experimental results are supplemented by model calculations utilizing density functional theory and Monte-Carlo simulations.     

Kinetic Monte Carlo simulation of hydrogen production from photocatalytic water splitting in the presence of methanol by 1 wt% Au/TiO2

In this research, using the kinetic Monte Carlo simulation (KMC), the hydrogen production from a water-methanol mixture using Au/TiO₂ photocatalyst is investigated. A mechanism is proposed, and the rate constants of the reaction steps are specified. The reaction rate constants of different steps and the concentration of the active sites on the photocatalyst surface were determined. An excellent match between simulated and experimental data confirms the results. The electron-hole pair production, methanol adsorption on the photocatalyst surface, and electron-hole recombination steps are considered the most critical steps. To study the effects of independent variables (initial concentration of methanol, photocatalyst dosage, pH, and time of reaction) on the produced hydrogen, a combination of KMC simulation and design of experiment was employed. The concentration of photocatalysis has the highest and pH has the lowest effect on the hydrogen production. The optimal conditions for photocatalytic hydrogen production are presented.     

Facile synthesis of black phosphorus directly grown on carbon paper as an efficient OER Electrocatalyst: Role of Interfacial charge transfer and induced local charge distribution

Black phosphorus (BP), as a new 2D material, is normally synthesized by a high-pressure and high-temperature (HPHT) method from white and red phosphorus, which severely hinders the further development of BP for any potential applications and leads to search for other potential applications of BP with big challenge. Herein, we develop a facile and efficient Thermal-Vaporization-Transformation (TVT) approach to prepare a highly active BP directly grown on carbon paper as the electrode for Oxygen evolution reaction (OER), showing a low onset potential of 1.45 V versus RHE. Simultaneously, the current density of BP-CP illustrates the excellent electro-catalysis stability only decreases by 3.4% after continuous operation for 10000 s. Meanwhile, the density functional theory (DFT) calculations further illustrates the P-doped carbon layer in the upper side of BP layer is actually responsible for its enhanced OER property, and the adjacent carbon atoms of the embedded P atoms are actually the active sites due to the induced local change distribution by intramolecular change transfer. Considering the facile, but efficient and scalable, TVT approach can directly synthesize BP-CP with excellent OER performance, which is promising for BP electrocatalysts used for OER in metal-air batteries, fuel cells, water-splitting devices, even other key renewable energy.     

Validation of qPCR method for enterococci quantification at Toronto beaches: Application for rapid recreational water monitoring

Rapid quantitative PCR-based methods for enterococci monitoring can allow public health authorities to make more timely beach posting decisions. However, qPCR methods must be assessed for proposed sites as locale-specific factors may affect DNA recovery or qPCR inhibition. We assessed the feasibility of the USEPA 1609.1 qPCR-based (Enterococcus) method at two urban Toronto beaches and three recreational areas at nearby river mouths in parallel with culture-based methods on the same water samples. A strong positive correlation was observed between the Enterococcus qPCR method and culturing-based quantification methods for E. coli and enterococci at both beaches and two river mouth areas. One river, known to be highly sewage-impacted, did not yield DNA suitable for qPCR analyses. qPCR results from biological replicates were strongly correlated and showed coefficients of variation as low as or lower than culture-based methods. With respect to Beach Action Value exceedances, the USEPA 1609.1 qPCR method provided an 80–90% level of agreement with E. coli enumeration results and >90% with enterococci enumeration. Results indicated that if recreational water locations and sampling conditions met the requirements of the USEPA 1609.1 qPCR method, the method can meet or exceed all quality control requirements and provide water quality results within 3.5 h for diverse recreational water settings around the City of Toronto.     

Realizing fragment spawning and fragment growth in the parallelized Discrete Element Method (DEM) during modelling of comminution

The particle based Discrete Element Method (DEM) can be applied to examine comminution processes. In this study, a DEM framework has been extended to model particle breakage without mass loss. After a breakage event occurs, spherical particles, as often considered in the DEM, are replaced by size reduced spherical fragments. During the following time steps, the fragments grow to their desired sizes, so that the mass loss can be counterbalanced. Previously defined overlaps with adjacent unbroken and broken particles (fragments) as well as walls are allowed. The breakage model has been realized in a parallelized DEM framework because comminution processes are often attributed to large numbers of particles and by parallelization the computational time can be reduced efficiently. An oedometer (one-dimensional compression in axial direction of a confined particle bed) has been modelled to investigate the parallelization efficiency and the influence of the permitted overlaps during the growth process on the growth duration. A simplified roller mill has been considered to examine the applicability of the breakage procedure considering parallelization. The results show that parallelization reduces computational time considerably. The breakage procedure is suitable to model comminution processes involving even densely packed particle systems and is superior to existing approaches.     

Bayesian Inference for ARFIMA Models

This article develops practical methods for Bayesian inference in the autoregressive fractionally integrated moving average (ARFIMA) model using the exact likelihood function, any proper prior distribution, and time series that may have thousands of observations. These methods utilize sequentially adaptive Bayesian learning, a sequential Monte Carlo algorithm that can exploit massively parallel desktop computing with graphics processing units (GPUs). The article identifies and solves several problems in the computation of the likelihood function that apparently have not been addressed in the literature. Four applications illustrate the utility of the approach. The most ambitious is an ARFIMA(2,d,2) model for the Campito tree ring time series (length 5405), for which the methods developed in the article provide an essentially uncorrelated sample of size 16,384 from the exact posterior distribution in under four hours. Less ambitious applications take as little as 4 minutes without exploiting GPUs.     

基于多尺度高斯核的分布式正则化回归学习算法

针对工业、信息等领域出现的基于较大规模、非平稳变化复杂数据的回归问题,已有算法在计算成本及拟合效果方面无法同时满足要求.因此,文中提出基于多尺度高斯核的分布式正则化回归学习算法.算法中的假设空间为多个具有不同尺度的高斯核生成的再生核Hilbert空间的和空间.考虑到整个数据集划分的不同互斥子集波动程度不同,建立不同组合系数核函数逼近模型.利用最小二乘正则化方法同时独立求解各逼近模型.最后,通过对所得的各个局部估计子加权合成得到整体逼近模型.在2个模拟数据集和4个真实数据集上的实验表明,文中算法既能保证较优的拟合性能,又能降低运行时间.     

Process-induced variability modeling of subthreshold leakage power considering device stacking

The dominance of leakage currents in circuit design has been impelled by steady downscaling of MOSFET into nanometer regime, and has become a significant component of total IC power dissipation. The issue is further aggravated with the inability to gauge the tolerance of process parameters around their nominal value. Consequently, the drive to improve the static power prediction has enticed accurate and reliable modeling of leakage current, specifically for ultralow power applications. In contrast to gate- and band-to-band-tunneling leakages, subthreshold leakage exhibits high susceptibility to process variations and hence has been considered for variability modeling. Fluctuations in the device electrical and geometry parameters result in a wider distribution of subthreshold leakage current. Hence, taking into account stacking effect, an analytical variability model to estimate subthreshold leakage power in subthreshold circuits, in the presence of threshold voltage variations is proposed. Further, the impact of threshold voltage variability on subthreshold leakage power is modeled in conjunction with simultaneous variations in gate length and width. The leakage power variability is characterized by model-generated distributions obtained using Monte Carlo analysis and validated against SPICE simulations. The proposed model is about 700 computationally faster than SPICE simulations with mean error being less than 0.19%.