Unique CNTs-chained Li4Ti5O12 nanoparticles as excellent high rate anode materials for Li-ion capacitors

Composite anode materials with a unique architecture of carbon nanotubes (CNTs)-chained spinel lithium titanate (Li₄Ti₅O₁₂, LTO) nanoparticles are prepared for lithium ion capacitors (LICs). The CNTs networks derived from commercial conductive slurry not only bring out a steric hindrance effect to restrict the growth of Li₄Ti₅O₁₂ particles but greatly enhance the electronic conductivity of the CNTs/LTO composites, both have contributed to the excellent rate capability and cycle stability. The capacity retention at 30 C (1 C = 175 mA g^?1) is as high as 89.7% of that at 0.2 C with a CNTs content of 11 wt%. Meanwhile, there is not any capacity degradation after 500 cycles at 5 C. The LIC assembled with activated carbon (AC) cathode and such a CNTs/LTO composite anode displays excellent energy storage properties, including a high energy density of 35 Wh kg^?1 at 7434 W kg^?1, and a high capacity retention of 87.8% after 2200 cycles at 1 A g^?1. These electrochemical performances outperform the reported data achieved on other LTO anode-based LICs. Considering the facile and scalable preparation process proposed herein, the CNTs/LTO composites can be very potential anode materials for hybrid capacitors towards high power-energy outputs.     

亲水作用色谱-串联质谱法测定动物源运动食品中3种氨基糖苷类抗生素的残留量

该研究建立了一种亲水交互作用色谱-串联质谱（HILIC-MS/MS）法测定动物源运动食品中潮霉素B、新霉素、安普霉素3种氨基糖苷类抗生素残留量的方法。结果表明，样品经Sielc Obelisc R柱分离，采用0.1%甲酸水溶液-乙腈梯度洗脱，可以实现3种目标物组分的分离。在此条件下，3种氨基糖苷类抗生素在5～500 ng/mL的质量浓度范围内线性关系良好，相关系数R2为0.999 5～0.999 9，检出限均为15 μg/kg，定量限均为50 μg/kg，保留时间的日间和日内相对标准偏差（RSD）分别为3.5%～7.9%和3.5%～4.1%，峰面积的日间和日内RSD分别为3.6%～7.4%和3.2%～3.9%，加标回收率为85.7%～93.6%，回收率试验结果的RSD为3.1%～5.2%。该方法可以满足动物源运动食品中3种氨基糖苷类抗生素的检测需求。     

Rapid screening of DON contamination in whole wheat meals by Vis/NIR spectroscopy and computer vision coupling technology

This work intends to develop an online experimental system for screening of deoxynivalenol (DON) contamination in whole wheat meals by visible/near-infrared (Vis/NIR) spectroscopy and computer vision coupling technology. Spectral and image information of samples with various DON levels was collected at speed of 0.15 m s⁻¹ on a conveyor belt. The two-type data were then integrated and subjected to chemometric analysis. Discriminant analysis showed that samples could be classified by setting 1000 μg kg⁻¹ as the cut-off value. The best correct classified rate obtained in prediction was 93.55% based on fusion of spectral and image features, with reduced prediction uncertainty as compared to single feature. However, quantification of DON by quantitative analysis was not successful due to poor model performance. These results indicate that, although not accurate enough to provide conclusive result, this coupling technology could be adopted for rapid screening of DON contamination in cereals and feeds during processing.     

Characterization of anisotropic gas permeability and thermomechanical properties of highly textured porous alumina

Porous alumina with a highly textured microstructure was fabricated by pulse electric current sintering (PECS) using alumina platelets. Highly oriented porous alumina with a porosity of 3%–50% was obtained by a pressure-controlled method of PECS. The properties of the highly textured porous alumina were measured in two directions. The nitrogen gas permeance and thermal conductivity at room temperature were higher in the direction along the platelet length due to the higher continuity of pores and the connectivity of alumina platelets, respectively. The anisotropy of the thermal conductivity at room temperature was investigated and explained by the effect of grain size of platelets as well as morphology and orientation of pores. The bending strength was higher with the loading direction along the platelet thickness. The thermal shock strength was clearly different in the two directions. The difference in the thermal shock strength was investigated by the measurement of properties and thermal stress analysis.     

Rational Designs for Lithium-Sulfur Batteries with Low Electrolyte/Sulfur Ratio

Lithium-sulfur batteries (LSBs) are considered a promising next-generation energy storage device owing to their high theoretical energy density. However, their overall performance is limited by several critical issues such as lithium polysulfide (PS) shuttles, low sulfur utilization, and unstable Li metal anodes. Despite recent huge progress, the electrolyte/sulfur ratio (E/S) used is usually very high (≥20 µL mg⁻¹), which greatly reduces the practical energy density of devices. To push forward LSBs from the lab to the industry, considerable attention is devoted to reducing E/S while ensuring the electrochemical performance. To date, however, few reviews have comprehensively elucidated the possible strategies to achieve that purpose. In this review, recent advances in low E/S cathodes and anodes based on the issues resulting from low E/S and the corresponding solutions are summarized. These will be beneficial for a systematic understanding of the rational design ideas and research trends of low E/S LSBs. In particular, three strategies are proposed for cathodes: preventing PS formation/aggregation to avoid inadequate dissolution, designing multifunctional macroporous networks to address incomplete infiltration, and utilizing an imprison strategy to relieve the adsorption dependence on specific surface area. Finally, the challenges and future prospects for low E/S LSBs are discussed.     

Development of autonomous monitoring and performance evaluation system of grid-tied photovoltaic station

This work aims to improve the existing monitoring systems MS for two grid-connected PV stations GCPVS of URERMS ADRAR, to eliminate its limitations. This improvement consists of developing an MS which is used for two PV stations with different configurations. This MS contains new LabVIEW-based monitoring software for visualizing real-time measured data and evaluating GCPVS performance. In addition, it illustrates the 2D and 3D real-time relationships of PV system parameters, which allow us to understand the dynamic behavior of PV system components. This developed monitoring software synchronizes also the various data acquisition units DAU of GCPVS, allowing simultaneous data access.To perform a reliable performance analysis and a comparative study of different GCPVS based on accurate measurements, the sensor's calibration is performed with its DAU. The MS autonomy is ensured by integrating developed PV-UPS. A graphical user interface is provided for the evaluation of PV-UPS performance.     

TiO2/MXene-Assisted LDI-MS for Urine Metabolic Profiling in Urinary Disease

Cancer remains an intractable medical problem. Rapid diagnosis and identification of cancer are critical to differentiate it from nonmalignant diseases. High-throughput biofluid metabolic analysis has potential for cancer diagnosis. Nevertheless, the present metabolite analysis method does not meet the demand for high-throughput screening of diseases. Herein, a high-throughput, cost-effective, and noninvasive urine metabolic profiling method based on TiO₂/MXene-assisted laser desorption/ionization mass spectrometry (LDI-MS) is presented for the efficient screening of bladder cancer (BC) and nonmalignant urinary disease. Combined with machine learning, TiO₂/MXene-assisted LDI-MS enables high diagnostic accuracy (96.8%) for the classification of patient groups (including 47 BC and 46 ureteral calculus (UC) patients) from healthy controls (113 cases). In addition, BC patients can also be identified from noncancerous UC individuals with an accuracy of 88.3% in the independent test cohort. Furthermore, metabolite variations between BC and UC individuals are investigated based on relative quantification, and related pathways are also discussed. These results suggest that this method, based on urine metabolic patterns, provides a potential tool for rapidly distinguishing urinary diseases and it may pave the way for precision medicine.     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

Quantitative UPLC‐MS/MS analysis of chlorogenic acid derivatives in antioxidant fractionates from dandelion (Taraxacum officinale) root

While qualitative studies have identified chlorogenic acids in antioxidant extracts, particularly ethyl acetate‐derived extracts, of Taraxacum officinale, quantitative analysis of these phenolic compounds remains largely unreported for this species. In this study, bioactivity‐guided fractionation of an antioxidant crude ethyl acetate extract (DPPH = 295.481 ± 0.955 mg TE g^?1 extract) from T. officinale root resulted in a number of reverse‐phase fractions that demonstrated high antioxidant activity (DPPH = 1058.733–1312.136 mg TE g^?1 extract), stronger than that of the synthetic antioxidant Trolox^®. UPLC‐MS/MS screening of these fractions for the presence of selected mono‐ and di‐caffeoylquinic acids revealed large quantities of 1,5‐dicaffeoylquinic acid present in several fractions (853.052–907.324 μg mg^?1), respectively. Due to the antioxidant potency and high levels of 1,5‐dicaffeoylquinic acid observed in these fractions, it was concluded that specifically this chlorogenic acid derivative is a major contributor to the antioxidant efficacy of dandelion root.     

GOLPH3 Regulates EGFR in T98G Glioblastoma Cells by Modulating Its Glycosylation and Ubiquitylation

Protein trafficking is altered when normal cells acquire a tumor phenotype. A key subcellular compartment in regulating protein trafficking is the Golgi apparatus, but its role in carcinogenesis is still not well defined. Golgi phosphoprotein 3 (GOLPH3), a peripheral membrane protein mostly localized at the trans-Golgi network, is overexpressed in several tumor types including glioblastoma multiforme (GBM), the most lethal primary brain tumor. Moreover, GOLPH3 is currently considered an oncoprotein, however its precise function in GBM is not fully understood. Here, we analyzed in T98G cells of GBM, which express high levels of epidermal growth factor receptor (EGFR), the effect of stable RNAi-mediated knockdown of GOLPH3. We found that silencing GOLPH3 caused a significant reduction in the proliferation of T98G cells and an unexpected increase in total EGFR levels, even at the cell surface, which was however less prone to ligand-induced autophosphorylation. Furthermore, silencing GOLPH3 decreased EGFR sialylation and fucosylation, which correlated with delayed ligand-induced EGFR downregulation and its accumulation at endo-lysosomal compartments. Finally, we found that EGF failed at promoting EGFR ubiquitylation when the levels of GOLPH3 were reduced. Altogether, our results show that GOLPH3 in T98G cells regulates the endocytic trafficking and activation of EGFR likely by affecting its extent of glycosylation and ubiquitylation.