Synthesis, characterization and catalytic application of Ru/Sn-and Cu/Zn-based nanocomposites

In the present work, the preparation of metal or metal oxide nanoparticles for catalytic applications was studied. Various routes and reaction conditions were explored, including the preparation through coordination polymers, sol-gel synthesis, and heat-treatment of metal salt solutions etc. TG-IR or TG-MS was employed to investigate the thermolysis of precursors. SEM, TEM, IR, XRD and EXAFS were used to elucidate the morphology and structure of the samples. Nitrogen physisorption and N2O chemisorption were applied to investigate the specific surface area, the pore structure, and copper surface area. Ru3Sn7 or (Sn, Ru)O2 were obtained in nanocrystalline forms by the thermolysis of [(CH3)3Sn]4Ru(CN)6. A batch-wise method and a continuous synthesis method were applied for the precipitation of Zn[Cu(CN)3]. The thermolysis of the cyanide led to Cu-Zn bimetallic oxides, which were reduced to Cu/ZnO and used as catalysts for methanol synthesis. ZnO nanoparticles were prepared by the thermolysis of the binuclear complex [Zn(en)3][Zn(CN)4] and the mononuclear compound Zn(CN)2. A sol-gel route was employed to prepare Cu/Zn/Al xerogels and aerogels with propylene oxide as the gelation initiator. ZnO can be atomically dispersed in Al2O3 in xerogels in a wide range of Zn concentrations. A higher Cu dispersion was observed in the aerogels than in the xerogels, leading to a higher specific surface area, a higher Cu surface area and subsequently a higher catalytic activity in methanol synthesis. In addition, Cu-Zn oxides were prepared by heat-treatment of the corresponding metal hydrate solutions. The preparation conditions, such as the additives (NaAOT etc.) and the solvents (aqueous or organic), have significant effects on the morphology of the products

DeltaFS: Pursuing Zero Update Overhead via Metadata-Enabled Delta Compression for Log-structured File System on Mobile Devices

Data compression has been widely adopted to release mobile devices from intensive write pressure. Delta compression is particularly promising for its high compression efficacy over conventional compression methods. However, this method suffers from non-trivial system overheads incurred by delta maintenance and read penalty, which prevents its applicability on mobile devices. To this end, this paper proposes DeltaFS, a metadata-enabled Delta compression on log-structured File System for mobile devices, to achieve utmost compressing efficiency and zero hardware costs. DeltaFS smartly exploits the out-of-place updating ability of Log-structured File System (LFS) to alleviate the problems of write amplification, which is the key bottleneck for delta compression implementation. Specifically, DeltaFS utilizes the inline area in file inodes for delta maintenance with zero hardware cost, and integrates an inline area manage strategy to improve the utilization of constrained inline area. Moreover, a complimentary delta maintenance strategy is incorporated, which selectively maintains delta chunks in the main data area to break through the limitation of constrained inline area. Experimental results show that DeltaFS substantially reduces write traffics by up to 64.8\%, and improves the I/O performance by up to 37.3\%

Online Fatigue-Monitoring Models with Consideration of Temperature Dependent Properties and Varying Heat Transfer Coefficients

Thermal stress failure caused by alternating operational loads is the one of important damage mechanisms in the nuclear power plants. To evaluate the thermal stress responses, the Green’s function approach has been generally used. In this paper, a method to consider varying heat transfer coefficients when using the Green’s function method is proposed by using artificial parameter method and superposition principle. Time dependent heat transfer coefficient has been treated by using a modified fluid temperature and a constant heat transfer coefficient. Three-dimensional temperature and stress analyses reflecting entire geometry and heat transfer properties are required to obtain accurate results. An efficient and accurate method is confirmed by comparing its result with corresponding 3D finite element analysis results for a reactor pressure vessel (RPV). From the results, it is found that the temperature dependent material properties and varying heat transfer coefficients can significantly affect the peak stresses and the proposed method can reduce computational efforts with satisfactory accuracy

A green and template-free synthesis process of superior carbon material with ellipsoidal structure as enhanced material for supercapacitors.

Metal Organic Frameworks or related carbon materials are the ideal materials for supercapacitors due to their high surface area and unique porous structure. Here, we propose a new green and recyclable synthesis method of porous carbon. Aluminum hydroxide (Al(OH)₃) and trimesic acid (BTC) are employed as raw materials to obtain aluminium trimesic (denoted as Al-BTC) via their covalent reaction. Then, the porous carbon is obtained through carbonization and dissolving process to remove the aluminum oxide (Al₂O₃). Al(OH)₃ is recovered by the Bayer method for the next batch. The SEM images show that the porous carbon has rugby-like morphology with the same of 400 nm wide and 1000 nm long which indicates the porous carbon with c/a ratio of 2.5 providing the largest specific volume surface area. The sample offers 306.4 F gˉ¹at 1 A gˉ¹, and it can retain 72.2% even at the current density of 50 A gˉ¹. In addition, the porous carbon provides excellent durability of 50,000 cycles at 50 A gˉ¹ with only 5.05% decline of capacitance. Moreover, the porous carbon has an ultrafast charge acceptance, and only 4.4 s is required for one single process, which is promising for application in electric vehicles

PRED_PPI: a server for predicting protein-protein interactions based on sequence data with probability assignment

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. Given the importance of PPIs, several methods have been developed to detect them. Since the experimental methods are time-consuming and expensive, developing computational methods for effectively identifying PPIs is of great practical significance.</p> <p>Findings</p> <p>Most previous methods were developed for predicting PPIs in only one species, and do not account for probability estimations. In this work, a relatively comprehensive prediction system was developed, based on a support vector machine (SVM), for predicting PPIs in five organisms, specifically humans, yeast, <it>Drosophila</it>, <it>Escherichia coli</it>, and <it>Caenorhabditis elegans</it>. This PPI predictor includes the probability of its prediction in the output, so it can be used to assess the confidence of each SVM prediction by the probability assignment. Using a probability of 0.5 as the threshold for assigning class labels, the method had an average accuracy for detecting protein interactions of 90.67% for humans, 88.99% for yeast, 90.09% for <it>Drosophila</it>, 92.73% for <it>E. coli</it>, and 97.51% for <it>C. elegans</it>. Moreover, among the correctly predicted pairs, more than 80% were predicted with a high probability of ≥0.8, indicating that this tool could predict novel PPIs with high confidence.</p> <p>Conclusions</p> <p>Based on this work, a web-based system, Pred_PPI, was constructed for predicting PPIs from the five organisms. Users can predict novel PPIs and obtain a probability value about the prediction using this tool. Pred_PPI is freely available at <url>http://cic.scu.edu.cn/bioinformatics/predict_ppi/default.html</url>.</p