Immobilizing Ni (II)-Exchanged Heteropolyacids on Silica as Catalysts for Acid-Catalyzed Esterification Reactions

Biodiesel was synthesized from oleic acid using Ni (II)-exchanged heteropolyacids immobilized on silica (Ni0.5H3SiW / SiO2 ) as a solid acid catalyst. Based on detailed analyses of FT-IR, XRD, TG and SEM, the structural, surface and thermal stability of Ni0.5H3SiW / SiO2 were investigated. Obtained results demonstrated that the Keggin structure was well in the immobilization process and possess a high thermal stability. Various esterification reaction conditions and reusability of catalyst were studied. High oleic acid conversion of 81.4 % was observed at a 1:22 mole ratio (oleic acid: methanol), 3 wt. % catalyst at 70 °C for 4 h. The Ni0.5H3SiW / SiO2 catalyst was reused for several times and presented relatively stable. More interestingly, the kinetic studies revealed the esterification process was compatible with the first order model, and a lower activation energy was obtained in this catalytic system

NRT1.1 Regulates Nitrate Allocation and Cadmium Tolerance in Arabidopsis

Abiotic stress induces nitrate (NO3-) allocation to roots, which increases stress tolerance in plants. NRT1.1 is broadly involved in abiotic stress tolerance in plants, but the relationship between NRT1.1 and NO3- allocation under stress conditions is unclear. In this study, we found that Arabidopsis wild-type Col-0 was more cadmium (Cd2+)-tolerant than the nrt1.1 mutant at 20 μM CdCl2. Cd2+ exposure repressed NRT1.5 but upregulated NRT1.8 in roots of Col-0 plants, resulting in increased NO3- allocation to roots and higher [NO3-] root-to-shoot (R:S) ratios. Interestingly, NITRATE REGULATORY GENE2 (NRG2) was upregulated by Cd2+ stress in Col-0 but not in nrt1.1. Under Cd2+ stress, nrg2 and nrg2-3chl1-13 mutants exhibited similar phenotypes and NO3- allocation patterns as observed in the nrt1.1 mutant, but overexpression of NRG2 in Col-0 and nrt1.1 increased the [NO3-] R:S ratio and restored Cd2+ stress tolerance. Our results indicated that NRT1.1 and NRG2 regulated Cd2+ stress-induced NO3- allocation to roots and that NRG2 functioned downstream of NRT1.1. Cd2+ uptake did not differ between Col-0 and nrt1.1, but Cd2+ allocation to roots was higher in Col-0 than in nrt1.1. Stressed Col-0 plants increased Cd2+ and NO3- allocation to root vacuoles, which reduced their cytosolic allocation and transport to the shoots. Our results suggest that NRT1.1 regulates NO3- allocation to roots by coordinating Cd2+ accumulation in root vacuoles, which facilitates Cd2+ detoxification

Enhanced corrosion protection by Al surface immobilization of in-situ grown layered double hydroxide films co-intercalated with inhibitors and low surface energy species

Abstract(#br)In this work, a novel in-situ grown layered double hydroxide (LDH) film co-intercalated with inhibitors (vanadates) and low surface energy substance (laurates) was immobilized on Al substrates. A long-term monitoring of electrochemical impedance spectra (EIS) of the various samples in 3.5 wt.% NaCl solution demonstrated the synergetic protection of the intercalated two functional species. Meanwhile, the X-ray diffraction (XRD) result of the samples after immersion in NaCl solution for a long time presented the anion-exchange process between vanadates/laurates and chlorides. The synergetic effect of the two species loaded film significantly contributed to the enhanced long-term corrosion protection of aluminum

Using Network Pharmacology and Molecular Docking to Explore the Mechanism of Shan Ci Gu (Cremastra appendiculata) Against Non-Small Cell Lung Cancer

Background: In recent years, the incidence and mortality rates of non-small cell lung cancer (NSCLC) have increased significantly. Shan Ci Gu is commonly used as an anticancer drug in traditional Chinese medicine; however, its specific mechanism against NSCLC has not yet been elucidated. Here, the mechanism was clarified through network pharmacology and molecular docking.Methods: The Traditional Chinese Medicine Systems Pharmacology database was searched for the active ingredients of Shan Ci Gu, and the relevant targets in the Swiss Target Prediction database were obtained according to the structure of the active ingredients. GeneCards were searched for NSCLC-related disease targets. We obtained the cross-target using VENNY to obtain the core targets. The core targets were imported into the Search Tool for the Retrieval of Interacting Genes/Proteins database, and Cytoscape software was used to operate a mesh chart. R software was used to analyze the Gene Ontology biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The core targets and active compounds were molecularly docked through Auto-Dock Vina software to predict the detailed molecular mechanism of Shan Ci Gu for NSCLC treatment. We did a simple survival analysis with hub gene to assess the prognosis of NSCLC patients.Results: Three compounds were screened to obtain 143 target genes and 1,226 targets related to NSCLC, of which 56 genes were related to NSCLC treatment. Shan Ci Gu treatment for NSCLC involved many BPs and acted on main targets including epidermal growth factor receptor (EGFR), ESR1, and SRC through signaling pathways including the endocrine resistance, EGFR tyrosine kinase inhibitor resistance, and ErbB signaling pathways. Shan Ci Gu might be beneficial for treating NSCLC by inhibiting cell proliferation and migration. Molecular docking revealed that the active compounds β-sitosterol, stigmasterol, and 2-methoxy-9,10-dihydrophenanthrene-4,5-diol had good affinity with the core target genes (EGFR, SRC, and ESR1). Core targets included EGFR, SRC, ESR1, ERBB2, MTOR, MCL1, matrix metalloproteinase 2 (MMP2), MMP9, KDR, and JAK2. Key KEGG pathways included endocrine resistance, EGFR tyrosine kinase inhibitor resistance, ErbB signaling, PI3K-Akt signaling, and Rap1 signaling pathways. These core targets and pathways have an inhibitory effect on the proliferation of NSCLC cells.Conclusion: Shan Ci Gu can treat NSCLC through a multi-target, multi-pathway molecular mechanism and effectively improve NSCLC prognosis. This study could serve as a reference for further mechanistic research on wider application of Shan Ci Gu for NSCLC treatment

Increasing fire retardancy of natural wood fibre board with colloidal silica-based geopolymer

Natural fibre contains cellulose, lignin, and hemicellulose, which makes it a highly flammable material. It is easily burned to decomposition and transformed into black charcoal with an average ignition temperature of around 300℃ [1]. However, natural fibre is widely used in architecture by adding it into brittle concrete or polymer matrix due to the durability, sustainability, and increasing strength of fibre material [2]. Therefore, how to increase the fire retardancy of natural fibre-based material has been a challenge for engineers. Contributions such as attaching common fire retardant materials like Mg(OH)2 and fly ash had been researched. Geo-polymer acts like inorganic polymer with non-carbon element, mainly silica-based network structure, which provides excellent mechanical properties, and high compressive strength up to 100 MPa [3]. Meanwhile, the geopolymer shows high fire retardancy compared with polymers. Thus, geopolymer like concretes have dominated the construction material with its expected characteristics. All above, an optimized system consisting of natural fibre and silica geopolymer was developed by adding 40% colloidal silica solution with Potassium hydroxide into natural fibre board as matrix. This system aims on increasing the fire retardancy of the board by attaching the matrix onto the fibre providing a protection layer. By adjusting the silica versus potassium ratio, the effect of fire retardancy changed. Comparing different systems with several fire property tests; the most appropriate concentration was found.Bachelor of Engineering (Materials Engineering

Applied automatic machine learning process for material computation

Machine learning enables computers to learn without being explicitly programmed. This paper outlines state-of-the-art implementations of machine learning approaches to the study of physical material properties based on Elastomer we developed, which combines with robotic automation and image recognition to generate a computable material model for non-uniform linear Elastomer material. The development of the neural network includes a few preliminary experiments to confirm the feasibility and the influential parameters used to define the final RNN neural network, the study of the inputs and the quality of the testing samples influencing the accuracy of the output model, and the evaluation of the generated material model as well as the method itself. To conclude, this paper expands such methods to the possible architectural implications on other non-uniform materials, such as the performance of wood sheets with different grains and tensile material made from composite materials

Some results about g-frames in Hilbert spaces

The concept of g-frame is a natural extension of the frame. This article mainly discusses the relationship between some special bounded linear operators and g-frames, and characterizes the properties of g-frames. In addition, according to the operator spectrum theory, the eigenvalues are introduced into the g-frame theory, and a new expression of the best frame boundary of the g-frame is given

The smart robot crafting approach to computing materials

This study presents a universal method that combines robotic/mechanical automation with image processing and artificial intelligence (AI) to generate material models without any pre-existing knowledge of the material itself. Inspired by the “hand-eye-mind” process, used typically in designing and crafting, this study proposed a digital version of the process that is capable of automatically conducting a large number of material experiments, observing them using image recognition, and subsequently training AI. The proposed method generates neural network models for common digital design environments that help to bridge a wide range of design intentions, fabrication controls, and dynamic material behaviors. In this study, two different experiments were conducted using the same method. The first one generated a material model for the bending behavior of non-linear synthetic rubber, and the other involved the dynamic control of the form-finding process of thermoplastics based on dynamic annealing, which contributed to a new 3D printing method. With current progress, we are able to prove that such a workflow is a widely adaptable method that encompasses a large variety of material properties and fabrication methods. It enables design and construction using complex material behaviors without the support of existing material/structure models

Synthesis Of Nd/Si Codoped Yag Powders Via A Solvothermal Method

We report the synthesis of single-phased Nd/Si-doped yttrium aluminum garnet powder via a solvothermal method by using ethanol as the solvent. The obtained powder exhibits a spherical shape, no agglomeration, and an average particle size of ∼300 nm, suitable for conventional powder processing. The effect of heat treatment on the lattice parameter and optical properties of the powder is also studied. The results are discussed in terms of Nd/Si solubility. © 2006 The American Ceramic Society

A New Classification Method for Ship Trajectories Based on AIS Data

Automatic identification systems (AIS) can record a large amount of navigation information about ships, including abnormal or illegal ship movement information, which plays an important role in ship supervision. To distinguish the trajectories of ships and analyze the behavior of ships, this paper adopts the method of supervised learning to classify the trajectories of ships. First, the AIS data for the ships were marked and divided into five types of ship tracks. The Tsfresh module was then used to extract various ship trajectory features, and a new ensemble classifier based on traditional classification using a machine learning algorithm was proposed for modeling and learning. Moreover, ten-fold cross validation was used to compare the ship trajectory classification results. The classification performance of the ensemble classifier was better than that of the other single classifiers. The average F1 score was 0.817. The results show that the newly proposed method and the new ensemble classifier have good classification effects on ship trajectories