Transient liquid phase sintering of high-purity mullite for high-temperature structural ceramics

High-purity mullite ceramics, promising engineering ceramics for high-temperature applications, were fabricated using transient liquid phase sintering to improve their high-temperature mechanical properties. Small amounts of ultrafine alumina or silica powders were uniformly mixed with the mullite precursor depending on the silica-alumina ratio of the resulting ceramics to allow for the formation of a transient liquid phase during sintering, thus, enhancing densification at the early stage of sintering and mullite formation by the reaction between additional alumina and the residual glassy phase (mullitization) at the final stage of sintering. The addition of alumina powder to the silica-rich mullite precursor resulted in a reaction between the glassy silica and alumina phases during sintering, thereby forming a mullite phase without inhibiting densification. The addition of fine silica powder to the mullite single-phase precursor led to densification with an abnormal grain growth of mullite, whereas some of the added silica remained as a glassy phase after sintering. The resulting mullite ceramics prepared using different powder compositions showed different sintering behaviors, depending on the amount of alumina added. Upon selecting an optimum process and the amount of alumina to be added, the pure mullite ceramics obtained via transient liquid phase sintering exhibited high density (approximately 99%) and excellent high-temperature flexural strength (approximately 320 MPa) at 1500 °C in air. These results clearly demonstrate that pure mullite ceramics fabricated via transient liquid phase sintering with compositions close to those of stoichiometric mullite could be a promising process for the fabrication of high-temperature structural ceramics used in an ambient atmosphere. The transient liquid phase sintering process proposed in this study could be a powerful processing tool that allows for the preparation of superior high-temperature structural ceramics used in the ambient processing atmosphere.     

Study of Pb-based and Pb-free perovskite solar cells using Cu-doped Ni1-xO thin films as hole transport material

SCAPS solar cell simulation program was applied to model an inverted structure of perovskite solar cells using Cu-doped Ni_1-xO thin films as hole transport layer. The Cu-doped Ni_1-xO film were made by co-sputtering deposition under different deposition conditions. By increasing the amount of the Cu-dopant, the film crystallinity enhanced whereas the bandgap energy decreased. The transmittance of the thin films decreased significantly by increasing the sputtering power of copper. High quality, uniform, compact, and pin-hole free films with low surface roughness were achieved. The structural, chemical, surface morphology, optical, electrical, and electronic properties of the Cu doped Ni_1-xO films were used as input parameters in the simulation of Pb-based (MAPbI_3-xCl_x) and Pb-free (MAGeI₃) perovskite solar cells. Simulation results showed that the performance of both Pb-based and Pb-free perovskite solar cell devices significantly enhanced with Cu-doped Ni_1-xO film. The highest power conversion efficiency (PCE) for the Pb-free perovskite solar cell is 8.9% which is lower than the highest PCE of 17.5% for the Pb-based perovskite solar cell.     

Changes in the content and antioxidative activity of β-carotene and its metabolite vitamin A during gastrointestinal digestion and absorption and optimisation of HPLC-based detection

The β-Carotene (BC), an important precursor of vitamin A (VA), possesses antioxidant activity but is fat-soluble and has low bioavailability. In previous in-vitro assays evaluating antioxidant and 2,2′-azobis(2-amidinopropane) dihydrochloride (AAPH) free radical scavenging, both BC and VA showed a strong ability to scavenge radicals and protected cells from oxidative stress. Here, we used artificially simulated gastrointestinal digestion and Caco-2 cell absorption models to evaluate the bioavailability of the BC during gastrointestinal digestion and absorption using high-performance liquid chromatography (HPLC) analysis. We observed high absorptive and transfer rates of BC and detected retinol metabolites (Vitamin A). Therefore, BC can be detected in the acidic gastrointestinal environment using HPLC. Optimised method provided better separation of BC and VA in the column, improving the accuracy of the test results.     

基于VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

Silver-substituted (Ag1-xCux)2ZnSnS4 solar cells from aprotic molecular inks

To battle the high open-circuit voltage deficit (V_OC,def) in kesterite (Cu₂ZnSnS₄ or CZTS) solar cells, a current field of research relates to point defect engineering by cation substitution. For example, by partly replacing Cu with an element of a larger ionic radius, such as Ag, the degree of Cu/Zn disorder decreases, and likewise does the associated band tailing. In this paper, solution-processed (Ag_1-xCu_x)₂ZnSnS₄ (ACZTS) samples are prepared through the aprotic molecular ink approach using DMSO as the solvent. The successful incorporation of silver into the CZTS lattice is demonstrated with relatively high silver concentrations, namely Ag/(Ag+Cu) ratios of 13% and 26%. The best device was made with 13% Ag/(Ag+Cu) and had an efficiency of 4.9%. The samples are compared to the pure CZTS sample in terms of microstructure, phase distribution, photoluminescence, and device performance. In the XRD patterns, a decrease in the lattice parameter c/a ratio is observed for ACZTS, as well as significant peak splitting with Ag addition for several of the characteristic kesterite XRD reflections. In addition to the improvement in efficiency, other advantageous effects of Ag-incorporation include enhanced grain growth and an increased band gap. A too high concentration of Ag leads to the formation of secondary phases such as SnS and Ag₂S as detected by XRD.     

A novel strategy based on salp swarm algorithm for extracting the maximum power of proton exchange membrane fuel cell

Cell temperature and water content of the membrane have a significant effect on the performance of fuel cells. The current-power curve of the fuel cell has a maximum power point (MPP) that is needed to be tracked. This study presents a novel strategy based on a salp swarm algorithm (SSA) for extracting the maximum power of proton-exchange membrane fuel cell (PEMFC). At first, a new formula is derived to estimate the optimal voltage of PEMFC corresponding to MPP. Then the error between the estimated voltage at MPP and the actual terminal voltage of the fuel cell is fed to a proportional-integral-derivative controller (PID). The output of the PID controller tunes the duty cycle of a boost converter to maximize the harvested power from the PEMFC. SSA determines the optimal gains of PID. Sensitivity analysis is performed with the operating fuel cell at different cell temperature and water content of the membrane. The obtained results through the proposed strategy are compared with other programmed approaches of incremental resistance method, Fuzzy-Logic, grey antlion optimizer, wolf optimizer, and mine-blast algorithm. The obtained results demonstrated high reliability and efficiency of the proposed strategy in extracting the maximum power of the PEMFC.     

In Vivo Albumin-Binding of a C-Functionalized Cyclam Platform for 64Cu-PET/CT Imaging in Breast Cancer Model

An improved glucose-chelator-albumin bioconjugate (GluCAB) derivative, GluCAB-2^Mal, has been synthesized and studied for in vivo ⁶⁴Cu-PET/CT imaging in breast cancer mice models together with its first-generation analogue GluCAB-1^Mal. The radioligand works on the principle of tumor targeting through the enhanced permeability and retention (EPR) effect with a supportive role played by glucose metabolism. [⁶⁴Cu]Cu-GluCAB-2^Mal (99 % RCP) exhibited high serum stability with immediate binding to serum proteins. In vivo experiments for comparison between tumor targeting of [⁶⁴Cu]Cu-GluCAB-2^Mal and previous-generation [⁶⁴Cu]Cu-GluCAB-1^Mal encompassed microPET/CT imaging and biodistribution analysis in an allograft E0771 breast cancer mouse model. Tumor uptake of [⁶⁴Cu]Cu-GluCAB-2^Mal was clearly evident with twice as much accumulation as compared to its predecessor and a tumor/muscle ratio of up to 5 after 24 h. Further comparison indicated a decrease in liver accumulation for [⁶⁴Cu]Cu-Glu-CAB-2^Mal.     

基于改进SOA-VMD的磁声发射信号去噪算法

磁声发射（MAE）是铁磁性材料磁化过程中产生的声发射信号，在构件应力检测和微观损伤检测中有着广泛的应用。针对MAE信号非稳态、复杂性、衰减性等特点，提出海鸥算法结合变分模态分解（SOA-VMD）的去噪方法，为克服海鸥算法求解过程中易陷入局部最优解问题，利用柯西变异算子产生随机迭代过程，使改进算法即柯西变异海欧算法（CVSOA）跳出早熟收敛。采用以幅值谱熵为适应度函数，优化VMD算法中分解模态个数K和二次惩戒因子α两个参数，将含噪声的MAE信号进行VMD分解重构。经仿真信号和实际检测信号分析表明，改进后的CVSOA-VMD算法全局寻优能力和去噪性能优于传统的SOA-VMD算法，降噪后的MAE信号特征值对于不同应力下均方根、偏斜度特征值的重复性更好，可靠性更高。