Xuzhou Institute of Technology

About: Xuzhou Institute of Technology is a education organization based out in Xuzhou, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 1696 authors who have published 1521 publications receiving 13541 citations.

Independent tunable multi-band absorbers based on molybdenum disulfide metasurfaces

Abstract: In this paper, we theoretically and numerically demonstrate a dual-band independently adjustable absorber comprising an array of stacked molybdenum disulfide (MoS2) coaxial nanodisks and a gold reflector that are separated by two dielectric insulating layers. The array plane functionality is explained by the dipole resonances with the MoS2 nanodisks. As a result, strong absorption is achieved at a wide range of incident angles under TE and TM polarizations. The structural parameters of the entire array and the carrier concentration in the MoS2 layers were varied to get the optimized absorption. The absorptance positioning can be adjusted by scaling the diameters of the MoS2 disks. We also proposed the array modification where nanodisks are replaced by a layer with nanoholes. The position of both absorptance peaks can be adjusted individually by changing the carrier concentration in the array. This structure can be useful for the design of chemical sensors, detectors or multi-band absorbers.

Evaluation of the differences in phenolic compounds and antioxidant activities of five green asparagus (Asparagus officinalis L.) cultivars

Abstract: Phenolic compounds and total antioxidant activities were analysed in five green asparagus cultivars (Grande, Altas, UC800, UC301 and UC157). In addition, the colours as well as the lignin, amino acid, and microelement contents of the asparagus cultivars were assessed. Cultivar ‘Grande’ had the best greenness (a*=-12.47), the highest vitamin C (59.1 mg/kg) and microelement contents (282.3 mg/kg) and the lowest lignin content (0.92%). Cultivar ‘UC301’ had the highest amino acid content (3.95 g/kg), and cultivar ‘UC157’ had the highest total phenolic and total flavonoid contents. Correlations were determined to evaluate the relationship between phenolic compounds and total antioxidant activity. Phenolic compounds, particularly rutin, are major contributors to the antioxidant activity of green asparagus, with a correlation coefficient of r=0.977-0.982. A principal component analysis was then performed to investigate the interrelationships between the parameters and the investigated cultivars. The results showed that cultivars ‘UC157’ and ‘Grande’ had high total phenolic and rutin contents and consequently high antioxidant activity. The cultivar had a marked influence on bioactive constituents, particularly the phenolic compound content and composition, and on the antioxidant activity of green asparagus, which may provide a basis for improved health benefits and breeding programs.

Enhancement of Thickness Measurement in Eddy Current Testing Using a Log–Log Method

Abstract: Eddy current testing for thickness measurement has great advantages, such as non-contact, low cost, and high efficiency. It is reported that there is a linear relationship between the tangent of the phase angle of impedance change and the thickness, termed as the approximate linear method (ALM). However, the accuracy of ALM is not very good, especially when the thickness of a specimen is very thin compared with standard penetration depth. The relationship between tangent of phase angle and thickness is simulated by Dodd-Deed model. The first and second derivatives of tangent of phase angle to thickness is consistent with the power function. Thus, the log–log method (LLM) is obtained by taking logarithm of power fitting equation. And, it is found that the change of excitation frequencies and lift-offs hardly affect the slope and linearity of LLM. The correctness and feasibility of LLM are verified by numerical simulation and experiments.

Modelling best fit-curve between China’s production and consumption-based temporal carbon emissions and selective socio-economic driving factors

Abstract: Production and consumption-based approaches are primarily used to determine emissions responsibility at industrial and national levels. China is the world's topmost emitter under both these approaches. Most of the literature especially for China mainly focuses on drivers of direct GHG emissions. This study based on the curvilinear analysis, models best-fit curves between these two emission types and selective driving factors. GDP, GDP/Capita and GNI best-fit curves didn't support EKC hypothesis for production-based emissions, while for consumption-based emissions their curves are in support of EKC. Population, population density, Urbanization, C02 intensity and urban population agglomeration all had non-linear best-fit curves. While energy use indicated a linear relation with production-based emissions and non-linear with consumption-based emissions. FDI and renewable energy consumption showed a non-linear negative relation with both emissions. Understanding of the non-linear relationship between vital driving factors and China's emissions under both approaches can help policymakers formulate more informed mitigation policies.

Optimization Design of Groundwater Pollution Monitoring Scheme and Inverse Identification of Pollution Source Parameters Using Bayes’ Theorem

Abstract: In the process of identifying groundwater pollution sources, in order to solve the problem that the monitoring data of monitoring wells was insufficient or the correlation between monitoring data and model parameters was weak, a monitoring well optimization method based on Bayesian formula and information entropy was proposed. Two-dimensional phreatic groundwater solute transport model was built and solved by using GMS software. To reduce the computational load of calling the numerical model repeatedly in the optimization design of the monitoring schemes and the identification process of the pollution sources, the Kriging method was used to establish the surrogate model of the numerical model. Under the condition of single well monitoring and determined monitoring frequency, with the target of optimization of monitoring position number D and monitoring time interval ∆t, both the single-objective monitoring scheme with the minimum information entropy of the model parameter posterior distribution and the multi-objective monitoring scheme with the minimum information entropy and the shortest monitoring time were optimized respectively. According to the above-optimized monitoring schemes, the delayed rejection adaptive Metropolis algorithm was used to identify the pollution source parameters. The case study results showed that under the condition of pre-set single well monitoring with monitoring frequency of 10 times, the single-objective optimized monitoring scheme was D = 37 and Δt = 20 days. Under this monitoring scheme, the mean errors of inversion pollution source parameters α = (XS, YS, T1, T2, QS) were 0.09%, 0.4%, 4.72%, 2.43%, and 9.29%, respectively. The multi-objective optimized monitoring scheme was D = 37 and Δt = 2 days. Under this monitoring scheme, the mean errors of the inversion parameters α = (XS, YS, T1, T2, QS) were 12.76%, 3.77%, 5.13%, 1.36%, and 7.68%, respectively. Compared with the monitoring scheme based on the single-objective optimization, although the inversion mean error of the five parameters based on the multi-objective optimized monitoring scheme increased by 2.75%, the monitoring time significantly reduced from 180 to 18 days.