Fabrication of paper based carbon/graphene/ZnO aerogel composite decorated by polyaniline nanostructure: Investigation of electrochemical properties

The cellulose derived carbon/graphene/ZnO aerogel composite was prepared as an electrode in order to investigate the electrochemical properties. Carbon aerogel was synthesized using paper as an available cellulose source, and the composite was obtained through a new and simple preparation method including the immersion of monolithic carbon aerogel in graphene oxide/Zn²⁺ suspension and subsequent chemical reduction and freeze drying. The morphology, functional groups and crystalline structure of the samples were studied with Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR) and X-ray Diffraction Spectroscopy (XRD), respectively. Electrochemical performance of the prepared binder free electrodes was examined using Cyclic Voltammetry (CV), Galvanostatic Charge-Discharge (GCD) and Electrochemical Impedance Spectroscopy (EIS). The data revealed that flexible carbon/graphene/ZnO composite resulted in a low density (0.035 g cm⁻³) electrode with the capacitance of 900 mF cm⁻² at a high current density of 10 mA cm⁻², lower IR drop and high cyclic stability (capacitance retention of 96%) after 1000 cycles, at 10 mA cm⁻². These features were due to the presence of 3D porous conductive network, highly reduced graphene oxide, and the formation of ZnO nanoparticles on graphene sheets. Moreover, polyaniline (PANI) was introduced to carbon/graphene/ZnO composite electrode using electro-oxidation method at different reaction time and aniline concentration in order to achieve remarkably improved capacitance of 2500 mF cm⁻² (at 10 mA cm⁻²) and low charge transfer resistance. Also, after the supercapacitor device assembly, the capacitance was retained. Based on the results, the synthesized composite is a promising material for new generation of lightweight freestanding electrodes with the high electrochemical performance.     

Spontaneous Cell Detachment and Reattachment in Cancer Cell Lines: An In Vitro Model of Metastasis and Malignancy

There is an unmet need for simplified in vitro models of malignancy and metastasis that facilitate fast, affordable and scalable gene and compound analysis. “Adherent” cancer cell lines frequently release “free-floating” cells into suspension that are viable and can reattach. This, in a simplistic way, mimics the metastatic process. We compared the gene expression profiles of naturally co-existing populations of floating and adherent cells in SW620 (colon), C33a (cervix) and HeLa (cervix) cancer cells. We found that 1227, 1367 and 1333 genes were at least 2-fold differentially expressed in the respective cell lines, of which 122 were shared among the three cell lines. As proof of principle, we focused on the anti-metastatic gene NM23-H1, which was downregulated both at the RNA and protein level in the floating cell populations of all three cell lines. Knockdown of NM23-H1 significantly increased the number of floating (and viable) cells, whereas overexpression of NM23-H1 significantly reduced the proportion of floating cells. Other potential regulators of these cellular states were identified through pathway analysis, including hypoxia, mTOR (mechanistic target of rapamycin), cell adhesion and cell polarity signal transduction pathways. Hypoxia, a condition linked to malignancy and metastasis, reduced NM23-H1 expression and significantly increased the number of free-floating cells. Inhibition of mTOR or Rho-associated protein kinase (ROCK) significantly increased cell death specifically in the floating and not the adherent cell population. In conclusion, our study suggests that dynamic subpopulations of free-floating and adherent cells is a useful model to screen and identify genes, drugs and pathways that regulate the process of cancer metastasis, such as cell detachment and anoikis.     

Marine Phospholipids from Fishery By-Products Attenuate Atherosclerosis

In this study, the anti-atherosclerotic properties of three marine phospholipids (MPLs) extracts from fishery by-products including codfish roe, squid gonad, and shrimp head are verified. Their effects on key factors involved in atherosclerosis are examined and compared to explore whether the differences in their constitutions lead to the differences in the function. All three MPLs dampen oxidation of low- density lipoproteins (LDL) in vitro. Treating RAW264.7 macrophages and HUVECs endothelial cells with each MPLs ranging 10–100 µg mL⁻¹ does not decrease cell viability, yet ox-LDL caused cytotoxicity of both cells are alleviated by 50 or 100 µg mL⁻¹ MPLs treatment. In addition, the three MPLs reduce ox-LDL induced macrophage foam-like transition, mainly through inhibition of lipid uptake. Of the three MPLs, the one from squid gonad exhibits the best effect. On the other hand, all three MPLs modulate inflammatory responses, equally, by inhibiting the adhesion of monocytes to endothelial cells, and decreasing secretion of pro-inflammatory cytokines IL-6 and MCP-1. Using a high-cholesterol diet induced zebrafish model, it is found that all three MPLs, especially the one from squid gonad, alleviates cholesterol accumulation in early plaques, and decreases total cholesterol as well as lipid peroxide in vivo. Practical Applications: As a way of making the best of the increasingly scarce marine resources, valuable lipid components can be recovered from by-products and wastes from the fishery industry. Here, we tested the anti-atherosclerotic effects and the mechanisms of three MPLs extracted from codfish roe, squid gonad, and shrimp head. Our study provides further evidence that marine phospholipids extracted from fishery by-products could protect against atherosclerosis, and helps to elucidate the structure-function relationship of MPLs.     

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. The predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.     

Simply constructed highly dispersed cobalt nanoparticles in diverse N-doped graphitic carbon with remarkable performances for water electrolysis

Metal/carbon composite materials are highly promising electrocatalysts for water electrolysis. In this work, three composites of metal cobalt nanoparticles highly dispersed in N-doped carbon materials were facilely constructed by pyrolysis of different phenylenediamine based Schiff base-Co complexes (PDBs). Interestingly, the composites derived from PDBs based on different phenylenediamine exhibited different morphologies. The superior case is that rodlike composite catalyst was derived from o-phenylenediamine based PDBs. The obtained catalyst exhibited remarkable performances for both cathodic hydrogen evolution reaction (HER) and anodic oxygen evolution reaction (OER), as well as overall water electrolysis. Only 172 and 289 mV of overpotentials and 1.57 V of cell voltage were exhibited at 10 mA cm^?2 for HER, OER and water splitting in 1.0 M KOH, respectively. The catalyst also displayed robust stability and high Faraday efficiency, and thus are potential high-performance catalyst for commercial water electrolysis.     

Identification and U-control of a state-space system with time-delay

This article presents a state-space model with time-delay to map the relationship between known input-output data for discrete systems. For the given input-output data, a model identification algorithm combining parameter estimation and state estimation is proposed in line with the causality constraints. Consequently, this article proposes a least squares parameter estimation algorithm, and analyzes its convergence for the studied systems to prove that the parameter estimation errors converge to zero under the persistent excitation conditions. In control system design, the U-model based control is introduced to provide a unilateral platform to improve the design efficiency and generality. A simulation portfolio from modeling to control is provided with computational experiments to validate the derived results.     

Tabular data: Deep learning is not all you need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.     

单增李斯特菌生态行为模型构建及机制研究进展

目的 探究食品中的单增李斯特菌随食品链相关条件的变化而发生的"生长-失活-再生长"生态行为规律.方法 以热加工为失活条件,综述单增李斯特菌生态行为的影响因素、模型构建和相关机制.结果 单增李斯特菌在"生长-失活"过程中,细胞的历史生长条件会对其失活特性产生影响,在"失活-再生长"过程中,细胞的历史生理状态会影响其修复再生长的延滞期.结论 食品链中细菌的生长和失活是连续的过程,有必要研究在连续的环境条件变化过程中细胞的生态行为,以期为食品风险评估和食品安全防控提供支持.     

煤基高能量密度燃料的合成及表征

高能量密度燃料是为新型高性能飞行器提供动力保障的关键,其合成及应用研究具有重要的前瞻性和重大战略意义。煤炭是我国的主体能源和重要原料,通过煤直接转化获取的煤基油,充分保留了煤中特有的环状分子化学结构,具有良好的热安定性和较高的能量密度,被认为是高超音速飞行器的优选燃料。以煤直接液化工艺生产的煤液化石脑油馏分为起始原料,通过富集轻质芳烃、化学合成、催化加氢稳定和产物分离提纯等方法制备煤基高能量密度燃料,并对其产物进行分子结构表征和性能评价。结果表明,煤直接液化生产的石脑油馏分是一种优异的催化重整原料,经催化重整富集轻质芳烃后,其轻质芳烃质量分数高达71.05%。Diels-Alder化学合成主产物是由多个封闭环平面组成且具有空间立体构型的二环或三环烃类物质,质量分数为46.18%,因分子内存在较大的张力能,结构紧凑,其拥有更大的密度和体积热值。煤基高能量密度燃料的密度和体积热值分别为0.8990 g/cm3与38.06 MJ/L,均大大超过现行的国内石油基喷气燃料(RP-3和RP-6)、煤基大比重喷气燃料、美国和俄罗斯军用标准。与单一纯物质合成高能量密度燃料(JP-10和T-10)比较,其密度与体积热值偏小。究其原因主要是轻质芳烃的富集度仅为71.05%,需进一步提高其轻质芳烃质量分数。另外,制备的煤基高能量密度燃料种类复杂,其主产物质量分数仅46.18%,下一步可重点调控合成产物的分子构型和纯化分离。