Wetting and bonding characteristics of selected liquid-metals with a high power diode laser treated alumina bioceramic

Changes in the wettability characteristics of an alumina bioceramic occasioned by high power diode laser (HPDL) surface treatment were apparent from the observed reduction in the contact angle. Such changes were due to the HPDL bringing about reductions the surface roughness, increases in the surface O2 content and increases in the polar component of the surface energy. Additionally, HPDL treatment of the alumina bioceramic surface was found to effect an improvement in the bonding characteristics by increasing the work of adhesion. An electronic approach was used to elucidate the bonding characteristics of the alumina bioceramic before and after HPDL treatment. It is postulated that HPDL induced changes to the alumina bioceramic produced a surface with a reduced bandgap energy which consequently increased the work of adhesion by increasing the electron transfer at the metal/oxide interface and thus the metal-oxide interactions. Furthermore, it is suggested that the increase in the work of adhesion of the alumina bioceramic after HPDL treatment was due to a correlation existing between the wettability and ionicity of the alumina bioceramic; for it is believed that the HPDL treated surface is less ionic in nature than the untreated surface and therefore exhibits better wettability characteristics

Nanofiller-tuned microporous polymer molecular sieves for energy and environmental processes

10.1039/c5ta09060aJournal of Materials Chemistry A41270-27

Regulating the aqueous phase monomer balance for flux improvement in polyamide thin film composite membranes

Polyamide thin film composite (PA TFC) membranes are synthesized from interfacial polymerization using two amines in the aqueous phase. The conventional monomer, mphenelynediamine (MPD), is partially replaced by a linear monomer, 1,3–diamino-2- hydroxypropane (DAHP). The water permeability of the membranes improves by around 22 % (to 2.67 ± 0.09 L.m-2.h-1.bar-1) while keeping the same high salt rejection (96-98%) at an optimum DAHP/MPD ratio of 12.8 %. While developing the control PA TFC membrane we introduce a washing step and show that the support surface should be free from surface protective coatings to achieve high water flux (2.18 ± 0.08 L.m-2.h-1.bar-1). Incorporating DAHP units into the polyamide network improves the water flux through the membranes fabricated on both original and washed supports. The surface morphologies of polyamide films change significantly with introduction of DAHP, from large ridge-and-valley structure to enlarged nodular structures. High resolution SEM images show an ultrathin polyamide thin film with a thickness that is reduced with addition of DAHP. These influences of DAHP, namely a reduction in the selective layer thickness, an alteration in surface morphology, changes in internal molecular packing and hydrophilicity, are suggested as factors behind the improved water permeability.We would like to thank EPSRC grant, Schlumberger Faculty for the Future (FFTF) foundation and the Qatar National Research Fund for the research financial support.This is the accepted manuscript. The final version is available at http://www.sciencedirect.com/science/article/pii/S0376738815002161

Deep Discrete Hashing with Self-supervised Pairwise Labels

Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantly outperformed by deep hashing methods due to their better feature representation and end-to-end learning framework. However, the most striking successes in deep hashing have mostly involved discriminative models, which require labels. In this paper, we propose a novel unsupervised deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image retrieval and classification. In the proposed framework, we address two main problems: 1) how to directly learn discrete binary codes? 2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way? We resolve these problems by introducing an intermediate variable and a loss function steering the learning process, which is based on the neighborhood structure in the original space. Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17) demonstrate that our DDH significantly outperforms existing hashing methods by large margin in terms of~mAP for image retrieval and object recognition. Code is available at \url{https://github.com/htconquer/ddh}

Computational evaluation of the diffusion mechanisms for C8 aromatics in porous organic cages

The development of adsorption and membrane-based separation technologies toward more energy and cost-efficient processes is a significant engineering problem facing the world today. An example of a process in need of improvement is the separation of C8 aromatics to recover para-xylene, which is the precursor to the widely used monomer terephthalic acid. Molecular simulations were used to investigate whether the separation of C8 aromatics can be carried out by the porous organic cages CC3 and CC13, both of which have been previously used in the fabrication of amorphous thin-film membranes. Metadynamics simulations showed significant differences in the energetic barriers to the diffusion of different C8 aromatics through the porous cages, especially for CC3. These differences imply that meta-xylene and ortho-xylene will take significantly longer to enter or leave the cages. Therefore, it may be possible to use membranes composed of these materials to separate ortho- and meta-xylene from para-xylene by size exclusion. Differences in the C8 aromatics’ diffusion barriers were caused by their different diffusion mechanisms, while the lower selectivity of CC13 was largely down to its more significant pore breathing. These observations will aid the future design of adsorbents and membrane systems with improved separation performance

On the transport and thermodynamic properties of quasi-two-dimensional purple bronzes A0.9_{0.9}Mo6_6O17_{17} (A=Na, K)

We report a comparative study of the specific heat, electrical resistivity and thermal conductivity of the quasi-two-dimensional purple bronzes Na$_{0.9}$Mo$_6$O$_{17}$ and K$_{0.9}$Mo$_6$O$_{17}$, with special emphasis on the behavior near their respective charge-density-wave transition temperatures $T_P$. The contrasting behavior of both the transport and the thermodynamic properties near $T_P$ is argued to arise predominantly from the different levels of intrinsic disorder in the two systems. A significant proportion of the enhancement of the thermal conductivity above $T_P$ in Na$_{0.9}$Mo$_6$O$_{17}$, and to a lesser extent in K$_{0.9}$Mo$_6$O$_{17}$, is attributed to the emergence of phason excitations.Comment: 8 pages, 6 figures, To appear in Physical Review

REMOVED: High Performance Gas Separation Membrane from a Polymer of Intrinsic Microporosity by Photochemical Surface Modification

This article has been removed: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy).This article has been removed at the request of the Executive Publisher.This article has been removed because it was published without the permission of the author(s)

Rapid measurement of three-dimensional diffusion tensor

In this article, the authors demonstrate a rapid NMR method to measure a full three-dimensional diffusion tensor. This method is based on a multiple modulation multiple echo sequence and utilizes static and pulsed magnetic field gradients to measure diffusion along multiple directions simultaneously. The pulse sequence was optimized using a well-known linear inversion metric (condition number) and successfully tested on both isotropic (water) and anisotropic (asparagus) diffusion systems.open4