Melting process and melt structure of fly ash/magnesium slag blends for the production of inorganic fibers

Fiber production from inorganic industrial solid wastes is an effective waste management strategy. Because of cost considerations, most enterprises generally use local solid wastes as raw materials to produce fibers. In this study, we explored the feasibility of producing fibers using fly ash and magnesium slag. The results show that the melting temperature of the blends composed of fly ash, magnesium slag, and a small amount of calcined dolomite first decreased and then increased with an increase in acidity coefficient (M_k) from 1.0 to 2.4_. The samples could form a eutectic system in the M_k range of 1.4–1.8, and therefore have a relatively low melting temperature in this M_k range. Fly ash could react with magnesium slag and calcined dolomite to form akermanite, gehlenite-magnesium, and anorthite at temperatures close to the melting temperature; therefore, these crystalline phases were the main reaction products formed in the samples with M_k values lower than 1.80. Anorthite reacted further with some Na-containing and Si-containing spieces to produce labradorite. Thus, the content of anorthite and labradorite rapidly increased and they became the major crystal phases in the blend samples with M_k values greater than 1.80. MAS-NMR spectroscopic analysis revealed that the network structure of the melts depended on the ratio of bridging oxygen to non-bridging oxygen; a high ratio of bridging oxygen to non-bridging oxygen could lead to the formation of a dense network structure in the melt. The blends of fly ash and magnesium slag can be used to produce wool fibers and continuous fibers. In addition, the suitable temperature ranges for the production of both types of fibers were determined. The drawing temperature for continuous fiber production depended on the degree of polymerization and structure of the melt.     

Portrait of Cancer Stem Cells on Colorectal Cancer: Molecular Biomarkers,Signaling Pathways and miRNAome

Colorectal cancer (CRC) is a leading cause of cancer death worldwide, and about 20% is metastatic at diagnosis and untreatable. Increasing evidence suggests that the heterogeneous nature of CRC is related to colorectal cancer stem cells (CCSCs), a small cells population with stemness behaviors and responsible for tumor progression, recurrence, and therapy resistance. Growing knowledge of stem cells (SCs) biology has rapidly improved uncovering the molecular mechanisms and possible crosstalk/feedback loops between signaling pathways that directly influence intestinal homeostasis and tumorigenesis. The generation of CCSCs is probably connected to genetic changes in members of signaling pathways, which control self-renewal and pluripotency in SCs and then establish function and phenotype of CCSCs. Particularly, various deregulated CCSC-related miRNAs have been reported to modulate stemness features, controlling CCSCs functions such as regulation of cell cycle genes expression, epithelial-mesenchymal transition, metastasization, and drug-resistance mechanisms. Primarily, CCSC-related miRNAs work by regulating mainly signal pathways known to be involved in CCSCs biology. This review intends to summarize the epigenetic findings linked to miRNAome in the maintenance and regulation of CCSCs, including their relationships with different signaling pathways, which should help to identify specific diagnostic, prognostic, and predictive biomarkers for CRC, but also develop innovative CCSCs-targeted therapies.     

Organic Semiconducting Luminophores for Near-Infrared Afterglow,Chemiluminescence, and Bioluminescence Imaging

Optical imaging has played a pivotal role in deciphering in vivo bioinformatics but is limited by shallow penetration depth and poor imaging performance owing to interfering tissue autofluorescence induced by concurrent photoexcitation. The emergence of near-infrared (NIR) self-luminescence imaging independent of real-time irradiation has timely addressed these problems. There are two main kinds of self-luminescent agents, namely inorganic and organic luminophores. Inorganic luminophores usually suffer from long-term biotoxicity concerns resulting from potential heavy-metal ions leakage and nonbiodegradability, which hinders their further translational application. In contrast, organic luminophores, especially organic semiconducting luminophores (OSLs) with good biodegradable potential, tunable design, and outstanding optical properties, are preferred in biological applications. This review summarizes the recent progress of OSLs used in NIR afterglow, chemiluminescence, and bioluminescence imaging. Molecular manipulation and nanoengineering approaches of OSLs are discussed, with emphasis on strategies that can extend the emission wavelength from visible to NIR range and amplify luminescence signals. This review concludes with a discussion of current challenges and possible solutions of OSLs in the self-luminescence field.     

New insight into subcritical rupture of SiC-based filaments at intermediate temperatures (400–900 °C) under air

The delayed failure of SiC fibrous reinforcement has continuously been investigated to warrant the long term performances of Ceramic Matrix Composite (CMC). Chiefly assessed on multifilament tow samples to alleviate some handling difficulties, subcritical crack growth (SCG) parameters are however ruled by structural artifacts which hinder the identification of intrinsic filament behavior. In this paper, we propose to estimate the true filament parameters for 5 fiber types from bundle behavior using a recently communicated Monte Carlo algorithm integrating flaw and stress distributions through a deterministic fracture mechanics law under Paris’ formulation. So computed tow lifetime are broadly dispersed, encompassing raw data, and show a structure-dependent scale effect, revealed by n_filament>n_tow where n is the stress exponent. The relationship between SCG coefficient and chemical composition of the substrate is discussed and highlights the major effect of doping elements (Ti or Zr), oxygen or hydrogen content.     

Preparation and antifouling performance of thin inorganic ultrafiltration membrane via assisted sol-gel method with different composition of dual additives

A novel TiO₂ thin film was prepared on the ceramic hollow fiber by the sol-gel method using poly(vinylpyrrolidone) (PVP) and polyvinyl alcohol (PVA) as additives. SEM images verified the formation of TiO₂ layer with various thickness using different composition of titania sols. The effect of the PVP and PVA contents on the TiO₂ sol properties, the separation and the antifouling performance of the ultrafiltration membranes were investigated thoroughly. When the contents of PVP and PVA were 1.0 wt% and 0.8 wt%, respectively, the resultant membrane showed a thickness of 0.55 μm with a pure water flux of 255 L m^?2 h^?1. In addition, the adherent foulant bovine serum albumin was applied to evaluate the antifouling performance. During the three fouling-recovery cycles, the flux recovery ratio and the flux decay ratio maintained about 99% and 30%. The BSA flux and rejection were still 169 L m^?2 h^?1 and 96.9% after the cycles, indicating a superior antifouling property.     

Reasonable construction of proton conducting channel via biomimetic caterpillar-like alumina fiber to improve the properties of its composite proton exchange membrane

In this paper, we prepare a novel biomimetic caterpillar-like alumina fiber with the characteristic of continuous alumina backbone and fine needle whiskers spine. Then the high-performance caterpillar-like alumina fiber composite proton exchange membrane (CAPEM) is obtained by introducing the amino modified biomimetic caterpillar-like alumina fiber into sulfonated polysulfone (SPSF) matrix, which successfully reasonable construction of the proton conducting channels in both vertical and horizontal orientation. The properties of CAPEM, including proton conductivity, methanol permeability, etc. Are systematically studied. The results show that the proton conductivity of CAPEM increases with rising the temperature, which reaches the maximum of 0.263 S/cm at 80 °C and 100% RH, respectively. The excellent proton conductivity of CAPEM is attributed to the long-range continuous proton conducting channel formed by the horizontal continuous alumina skeleton in the in-plane direction and the vertical overlapped fine needle whiskers spine in the through-plane direction. In addition, the interfacial compatibility between amino modified caterpillar-like alumina fiber and SPSF matrix is enhanced through the reasonable construction of proton conducting channels, which effectively inhibits the methanol permeation of the composite membrane with 4.18 × 10^?7 cm² s^?1 and improves the comprehensive performance of the CAPEM.     

基于蜻蜓翅脉结构的连续碳纤维增强树脂基复合材料仿生设计与增材制造

以具有轻质高强优异性能的蜻蜓翅脉结构为设计灵感，在分析翅脉网格结构抗冲击原理的基础上，设计了传统和仿生两类对比结构。采用熔融挤出3D打印机成功制备了具有不同结构的连续碳纤维增强聚乳酸复合材料试样，并对不同结构复合材料试样的拉伸性能和抗冲击性能进行了测试和对比分析。研究分析结果表明：由于拉伸力方向上的连续碳纤维含量相对较少，限制了仿生结构复合材料抗拉强度的提高，但仿生结构的平均抗拉强度为传统结构的1.18倍；当仿生结构复合材料试样受到冲击力时，其内部六边形结构的连接角度会发生变化，从而极大消耗冲击能量，同时具有六边形网格结构的连续碳纤维可以有效阻碍裂纹的扩展，因此仿生结构的平均冲击韧性可以达到传统结构的2.46倍；仿生蜻蜓翅脉结构可以显著提高增材制造复合材料的综合力学性能，且对于抗冲击性能的提高具体突出效果。连续碳纤维增强树脂基复合材料的有效可行的仿生蜻蜓翅脉结构设计和增材制造，可极大扩展其在高冲击载荷领域中的相应应用。     

基于纸浆纤维的贵重物品内包装塑造系统研究

为解决贵重物品与其内包装“契合度”不高的问题，提出基于纸浆纤维的贵重物品内包装塑造系统。系统通过三维扫描获取待包装物品的点云信息，经主控计算机进行数据处理，以下位机为执行中枢，辅之其他外部设备，利用纸浆纤维为贵重物品塑造“量身定制”的内包装，提升内包装契合度，增强保护效果，绿色环保，安全稳定。     

Improving accuracy of automatic optical inspection with machine learning

Electronic devices require the printed circuit board(PCB)to support the whole structure,but the assembly of PCBs suffers from welding problem of the electronic components such as surface mounted devices(SMDs)resistors.The automated optical inspection(AOI)machine,widely used in industrial production,can take the image of PCBs and examine the welding issue.However,the AOI machine could commit false negative errors and dedicated technicians have to be employed to pick out those misjudged PCBs.This paper proposes a machine learning based method to improve the accuracy of AOI.In particular,we propose an adjacent pixel RGB value based method to pre-process the image from the AOI machine and build a customized deep learning model to classify the image.We present a practical scheme including two machine learning procedures to mitigate AOI errors.We conduct experiments with the real dataset from a production line for three months,the experimental results show that our method can reduce the rate of misjudgment from 0.3%–0.5%to 0.02%–0.03%,which is meaningful for thousands of PCBs each containing thousands of electronic components in practice.