溶胶-凝胶原位合成钒钨钛催化剂及NH3-SCR性能

利用溶胶-凝胶技术原位合成V₂O₅-WO₃/TiO₂介孔结构脱硝催化剂。通过XRD、BET、SEM和XPS等手段对催化剂结构与组成进行表征,发现结构导向剂CTAB促进催化剂的比表面积和孔容的增加,促进催化剂活性组分的分散与裸露的V活性组分数量增加;且随CTAB含量的增加,V⁴⁺/V⁵⁺的比值降低,即降低了催化剂表面钒物种的氧化能力,有利于提高催化剂的低温脱硝活性与高温选择性。催化活性评价结果表明,CTAB对催化剂的脱硝活性有显著促进作用,当CTAB的添加量为0.01（质量）时,催化剂的脱硝活性大于90%的温度窗口最宽为212~393℃,200℃时脱硝效率达85.6%。     

Trajectory sensitivity analysis: a new criterion

For linear system simulation and analysis, it is sometimes necessary to compare the trajectories of a nominal system and a perturbed system. The perturbed system may result from system parameter variations, non-linearities, model reduction, suboptimal controller design and soon. A criterion to measure the trajectory deviation is established through the definition of the error index. The analytical expression of the error index and its application are presented in the paper.     

COMPETITION OF FOOD BACTERIA WITH LISTERIA MONOCYTOGENES DURING ENRICHMENT CULTURE

Thirty-two foodborne bacterial isolates were tested as potential competitors of Listeria monocytogenes strain LM82 during enrichment because of their resistance to the selective agents in Listeria enrichment and isolation media. Competitive ability of each isolate was classified as weak, moderate, or strong by determining the ratio at which it masked identification of LM82 at an inoculation concentration of 10 colony forming units (CFU)/10 mL of Listeria enrichment broth. Of the competitive isolates identified, six were Enterococcus spp., two were Staphylococcus spp., and one was a Corynebacterium sp. Although several strains of Enterococcus faecium were examined, not all were competitive. Of six other bacterial strains associated with food fermentations and tested for competitiveness with LM82, one, a Gram-positive tetrad, was competitive. This study showed that although food microfloral strains that are able to survive in enrichment and isolation environments are fairly common, they do not necessarily compete with Listeria. Not all strains in a competitive species are necessarily competitive .     

Effect of insulating oil covering electrodes on the characteristics of a dielectric barrier discharge

In this study, the effects of the fluid cooling and electric field line deformation were investigated in a dielectric barrier discharge (DBD) plasma source. The DBD plasma jet is improved by covering the ground electrode and a power electrode with insulating oil. We obtained positive results as insulating oil prevents arc formation, while it improved the supplied power and plasma jet length, and increased radical production. Radical production of this nonthermal plasma jet is studied with polyvinyl alcohol–potassium iodide liquid.     

Mapping birefringence in three dimensions using polarized light field microscopy: the case of the juvenile clamshell

We report methods to generate three‐dimensional maps of birefringence, its position and orientation in juvenile shells of the Atlantic hard clamshell (Mercenaria mercenaria). For measuring the retardance and optic axis orientation of curved shell surfaces in three dimensions, we developed enhanced acquisition and processing algorithms and combined results from conventional and light field imaging approaches to reconstruct the three‐dimensional shell shape and its anisotropic optical properties. Our work represents the first successful attempt to generate such maps at a spatial resolution of about 2 μm and angular steps of about 9° in terms of the inclination angles of the optic axis. The maps of clamshell birefringence provide structural insights into the early mineralization during juvenile clamshell development.     

AN FFT-BASED FORMULATION FOR THE COMPUTATION OF ELECTROMAGNETIC SCATTERING FROM 3-D,PERFECTLY CONDUCTING BODIES

An FFT-based formulation for the computation of electromagnetic scattering from 3-D, perfectly conducting objects is presented. The formulation solves the EFIE iteratively via a conjugate gradient scheme and has the major advantage of a low storage requirement for scatterers in the resonance region. This factor, coupled with the low run-times resulting from use of the FFT to compute the associated matrices, makes the formulation a particularly efficient one. Numerical results encompassing current distributions and far-field radar cross-sections are presented for PEC cubes of resonant size. It is shown that the computed results yield good agreement compared with other numerical techniques and experimental data.     

Analytical treatment of linearized self-consistent theory of a gyromonotron with a non-fixed structure

The linearized self-consistent theory of gyrotron is analytically treated for a self-excited oscillator with non-fixed structure. The self-consistent RF axial field profile consists of four waves, namely the forward and backward travelling waves of the waveguide, and two electron cyclotron waves. According to the magnetic field value (or the detuning parameter), the electron cyclotron wave couples strongly with the forward wave, or weakly with backward wave of the waveguide mode. Also, the starting current and the axial field profile are significantly different for the forward versus the backward interaction.     

Development of deep neural network model to predict the compressive strength of FRCM confined columns

The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix (FRCM). through both physical models and Deep Neural Network model (artificial neural network (ANN) with double and triple hidden layers). The database of 330 samples collected for the training model contains many important parameters, i.e., section type (circle or square), corner radius r_c, unconfined concrete strength f_co, thickness n_t, the elastic modulus of fiber E_f , the elastic modulus of mortar E_m. The results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction accuracy. The ANN model with double hidden layers (APDL-1) was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical models. Furthermore, the results also reveal that the unconfined compressive strength of concrete, type of fiber mesh for FRCM, type of section, and the corner radius ratio, are the most significant input variables in the efficiency of FRCM confinement prediction. The performance of the proposed ANN models (including double and triple hidden layers) had high precision with R higher than 0.93 and RMSE smaller than 0.13, as compared with other models from the literature available.     

Application of machine learning technique for predicting and evaluating chloride ingress in concrete

The degradation of concrete structure in the marine environment is often related to chloride-induced corrosion of reinforcement steel. Therefore, the chloride concentration in concrete is a vital parameter for estimating the corrosion level of reinforcement steel. This research aims at predicting the chloride content in concrete using three hybrid models of gradient boosting (GB), artificial neural network (ANN), and random forest (RF) in combination with particle swarm optimization (PSO). The input variables for modeling include exposure condition, water/binder ratio (W/B), cement content, silica fume, time exposure, and depth of measurement. The results indicate that three models performed well with high accuracy of prediction (R²≥ 0.90). Among three hybrid models, the model using GB_PSO achieved the highest prediction accuracy (R² = 0.9551, RMSE = 0.0327, and MAE = 0.0181). Based on the results of sensitivity analysis using SHapley Additive exPlanation (SHAP) and partial dependence plots 1D (PDP-1D), it was found that the exposure condition and depth of measurement were the two most vital variables affecting the prediction of chloride content. When the number of different exposure conditions is larger than two, the exposure significantly impacted the chloride content of concrete because the chloride ion ingress is affected by both chemical and physical processes. This study provides an insight into the evaluation and prediction of the chloride content of concrete in the marine environment.     

混凝土基本组分吸收二氧化碳的能力(英文)

研究了利用混凝土产品吸收二氧化碳的可行性。混凝土的3个基本组分:水泥、细沙和骨料,都可被用来作为二氧化碳吸附物以实现储留最大化,另外,可利用高钙钢渣等工业废料生产混凝土骨料。研究发现波特兰水泥2h碳化后吸收14%二氧化碳,碳化后的材料的强度与7d传统养护的强度相当。钢包钢渣细粉能吸收4%～12%二氧化碳,能用来替代河沙。吹氧高炉钢渣对二氧化碳吸收的能力超过12%。由此制成的粗骨料其强度可与石灰岩相比。如果混凝土砌块由这3组分组成,一块20cm×20cm×20cm的标准砌块能吸收1.39kg二氧化碳。二氧化碳在混凝土中的储留是一项经济可行、直接减排的有效技术。