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This article presents vertically coupled, rectangular complementary split-ring resonator-shaped quad-band double-negative (DNG) metamaterial unit cells, that is, having both negative permittivity and permeability, which redirect negative refractive and also are not found in nature. The metamaterial is fabricated on magnesium zinc ferrite-based flexible microwave substrates, and the flexible substrates are chosen with two different concentrations of magnesium (Mg) denoted by Mg30 and Mg50 for 30% and 50% of Mg, which possess dielectric constants of 4.32 and 3.15 and loss tangents of 0.003 and 0.005, respectively. The proposed metamaterials are demonstrated by utilizing the CST microwave simulator, and their effective parameters are extracted according to the Nicolson-Ross-Wire method. With Mg30, the prepared, flexible metamaterial shows measured resonances at 3.70 GHz, 7 GHz, 8.60 GHz, and 9.78 GHz, whereas with Mg50 it shows the measured resonances at 4.10 GHz, 7.70 GHz, 9.33 GHz, and 10.62 GHz. Very good effective medium ratios (EMR) along with DNG properties are obtained, namely 6.5 and 5.85 for Mg30 and Mg50, respectively, with a physical dimension of 12.5 × 9.5 mm2 for both of the unit cells. Also, the electric field, magnetic field, and surface current distribution at different resonances and the polarization insensitivity at different polarization angles were observed. Thus, the designed new flexible substrate microwave materials based on DNG metamaterials are potential candidates for S-, C- and X-band applications, as well as for flexible microwave technologies. 相似文献
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《Journal of voice》2022,36(4):582.e23-582.e32
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以氧化石墨烯(GO)为原料, 利用温和方法制备了3种不同还原程度的部分还原氧化石墨烯pRGO1, pRGO2和pRGO3(pRGO1—3); 利用傅里叶变换红外光谱(FTIR)、 拉曼光谱(Raman)、 X 射线光电子能谱(XPS)、 紫外-可见光谱(UV-Vis)、 透射电子显微镜(TEM)和 EDS能谱对其结构和形貌进行了表征. 细胞实验结果表明, 无激光照射下pRGO1—3本身的细胞毒性较低; 近红外(NIR)激光照射下pRGO1—3通过光热和光毒性双重作用杀伤肿瘤细胞. 实验结果显示了pRGO 在肿瘤光热疗法和光动力疗法领域的应用潜力. 相似文献
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Laser cooling of a molecule with heavy nuclei is often complicated because of the density distribution of the electronic states. Here, we evaluate the feasibility of the laser cooling of the SrI molecule by calculating the potential energy curves and transition dipole moments of the ground and low-lying excited states using the multi-reference configuration interaction plus Davidson corrections (MRCI + Q) and the all-electron basis sets of ANO-RCC. The relativistic effect and the spin-orbit coupling splits are included, because both Sr and I are heavy atoms. Based on the obtained potential energy curves, we solve the Schrödinger equation of nuclear motion to determine the rovibrational energy levels and the Franck-Condon factors. The spectroscopic parameters are obtained by fitting the rovibrational energy levels with the Dunham expression. The radiation lifetimes, the Doppler and recoil temperatures between the X2Σ+ and the 2Π1/2/2Π3/2/B2Σ+ states are calculated. 5-color laser cooling schemes for the molecule are proposed, which can lead to the total effective Franck-Condon factors being 0.99983, 0.99979, and 0.99941 for the three transitions, respectively. All the obtained results suggest that the SrI molecule is a feasible candidate for laser cooling. 相似文献
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Yuan Xin Zhang Shuren Zhong Xuanmeng Yuan Hao Song Dongfan Wang Xiaoyu Yu Hanyang Guo Zijian 《中国科学:化学(英文版)》2022,65(10):1978-1984
Science China Chemistry - Oncogenic KRAS reprograms pancreatic ductal adenocarcinoma (PDAC) cells to a state that is awfully resistant to apoptosis. An alternative coping strategy is to trigger a... 相似文献
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《Arabian Journal of Chemistry》2022,15(7):103915
There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R2. Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively. 相似文献