Quantitative analysis of MWCNT agglomeration in polymeric‐based membranes using atomic force microscope

In this work, we performed an analysis on the surface properties of polyethersulfone (PES)‐based ultrafiltration membranes that were prepared by incorporating acid functionalized multiwalled carbon nanotube (f‐MWCNT) and polyvinylpyrrolidone into membrane matrix via simple blending method. The bonding‐chemistry of the nanocomposites implanted within the PES membrane was investigated by Fourier transform infrared spectrometer while atomic force microscope was employed to observe the distribution of f‐MWCNT in the membrane matrix and further measured its agglomeration quantitatively. The resultant 3D atomic force microscope images provided the most satisfactory way to examine the distribution of nanomaterials in the membranes, and with the help of ‘point profile data’ the maximum height and width of the agglomerated cluster could be quantitatively calculated. Moreover, the surface profile studies of the membrane surface provided the information about the length, volume, area, perimeter, radius and diameter of grains. It was observed that PES/nanocomposite blended membranes were held together via strong hydrogen bonding, but poor dispersion of MWCNT in the membrane matrix reduced the membrane performance in terms of protein (pepsin and bovine albumin serum) rejection and increased the surface roughness. Copyright © 2016 John Wiley & Sons, Ltd.     

Biotechnological Innovations from Ocean: Transpiring Role of Marine Drugs in Management of Chronic Disorders

Marine drugs are abundant in number, comprise of a diverse range of structures with corresponding mechanisms of action, and hold promise for the discovery of new and better treatment approaches for the management of several chronic diseases. There are huge reserves of natural marine biological compounds, as 70 percent of the Earth is covered with oceans, indicating a diversity of chemical entities on the planet. The marine ecosystems are a rich source of bioactive products and have been explored for lead drug molecules that have proven to be novel therapeutic targets. Over the last 70 years, many structurally diverse drug products and their secondary metabolites have been isolated from marine sources. The drugs obtained from marine sources have displayed an exceptional potential in the management of a wide array of diseases, ranging from acute to chronic conditions. A beneficial role of marine drugs in human health has been recently proposed. The current review highlights various marine drugs and their compounds and role in the management of chronic diseases such as cancer, diabetes, neurodegenerative diseases, and cardiovascular disorders, which has led to the development of new drug treatment approaches.     

The Cradle-to-Cradle Life Cycle Assessment of Polyethylene terephthalate: Environmental Perspective

Over the last several years, the number of concepts and technologies enabling the production of environmentally friendly products (including materials, consumables, and services) has expanded. One of these ways is cradle-to-cradle (C2C) certified^TM. Life cycle assessment (LCA) technique is used to highlight the advantages of C2C and recycling as a method for reducing plastic pollution and fossil depletion by indicating the research limitations and gaps from an environmental perspective. Also, it estimates the resources requirements and focuses on sound products and processes. The C2C life cycle measurements for petroleum-based poly (ethylene terephthalate) (PET) bottles, with an emphasis on different end-of-life options for recycling, were taken for mainland China, in brief. It is considered that the product is manufactured through the extraction of crude oil into ethylene glycol and terephthalic acid. The CML analysis method was used in the LCIA for the selected midpoint impact categories. LCA of the product has shown a drastic aftermath in terms of environmental impacts and energy use. But the estimation of these consequences is always dependent on the system and boundary conditions that were evaluated throughout the study. The impacts that burden the environment are with the extraction of raw material, resin, and final product production. Minor influences occurred due to the waste recycling process. This suggests that waste degradation is the key process to reduce the environmental impacts of the production systems. Lowering a product’s environmental impact can be accomplished in a number of ways, including reducing the amount of materials used or choosing materials with a minimal environmental impact during manufacture processes.     

CT and MRI Medical Image Fusion Using Noise-Removal and Contrast Enhancement Scheme with Convolutional Neural Network

Medical image fusion (MIF) has received painstaking attention due to its diverse medical applications in response to accurately diagnosing clinical images. Numerous MIF methods have been proposed to date, but the fused image suffers from poor contrast, non-uniform illumination, noise presence, and improper fusion strategies, resulting in an inadequate sparse representation of significant features. This paper proposes the morphological preprocessing method to address the non-uniform illumination and noise by the bottom-hat–top-hat strategy. Then, grey-principal component analysis (grey-PCA) is used to transform RGB images into gray images that can preserve detailed features. After that, the local shift-invariant shearlet transform (LSIST) method decomposes the images into the low-pass (LP) and high-pass (HP) sub-bands, efficiently restoring all significant characteristics in various scales and directions. The HP sub-bands are fed to two branches of the Siamese convolutional neural network (CNN) by process of feature detection, initial segmentation, and consistency verification to effectively capture smooth edges, and textures. While the LP sub-bands are fused by employing local energy fusion using the averaging and selection mode to restore the energy information. The proposed method is validated by subjective and objective quality assessments. The subjective evaluation is conducted by a user case study in which twelve field specialists verified the superiority of the proposed method based on precise details, image contrast, noise in the fused image, and no loss of information. The supremacy of the proposed method is further justified by obtaining 0.6836 to 0.8794, 0.5234 to 0.6710, and 3.8501 to 8.7937 gain for QFAB, CRR, and AG and noise reduction from 0.3397 to 0.1209 over other methods for objective parameters.     

Graphene-Oxide-Based Fluoro- and Chromo-Genic Materials and Their Applications

Composite materials and their applications constitute a hot field of research nowadays due to the fact that they comprise a combination of the unique properties of each component of which they consist. Very often, they exhibit better performance and properties compared to their combined building blocks. Graphene oxide (GO), as the most widely used derivative of graphene, has attracted widespread attention because of its excellent properties. Abundant oxygen-containing functional groups on GO can provide various reactive sites for chemical modification or functionalization of GO, which in turn can be used to develop novel GO-based composites. This review outlines the most recent advances in the field of novel dyes and pigments encompassing GO as a key ingredient or as an important cofactor. The interactions of graphene with other materials/compounds are highlighted. The special structure and unique properties of GO have a great effect on the performance of fabricated hybrid dyes and pigments by enhancing the color performance of dyes, the anticorrosion properties of pigments, the viscosity and rheology of inks, etc., which further expands the applications of dyes and pigments in dyeing, optical elements, solar-thermal energy storage, sensing, coatings, and microelectronics devices. Finally, challenges in the current development as well as the future prospects of GO-based dyes and pigments are also discussed. This review provides a reference for the further exploration of novel dyes and pigments.     

Harmonic and intermodulation performance of optical-fiber Mach–Zehnder microphone

In this paper, closed-form mathematical expressions are obtained for the amplitudes of the harmonic and intermodulation products of the fiber optic Mach–Zehnder microphone excited by a multitone acoustical perturbation. The results show the strong dependence of the harmonic and intermodulation performance on the amplitude of the acoustical perturbation tones and the phase shift produced by the DC bias voltage applied to the reference fiber of the Mach–Zehnder interferometer. The results also show that depending on the phase shift produced by the DC bias voltage, either the third-order or the second-order intermodulation will be dominant. Compared to previously published results for other types of microphones, it appears that the phase shift produced by the DC bias voltage plays an important role in deciding the order of the dominant intermodulation product.     

Manipulation of inherent characteristics of graphene through N and Mg atom co-doping; a DFT study

First-principles calculations were performed to investigate the structural, electronic, magnetic and optical properties of nitrogen (N) and magnesium (Mg) atom co-doped graphene systems. We observed that, N and Mg atom co-doping in graphene, introduces half-metallic properties in the electronic structure of graphene, introduces ferromagnetism behavior along with new trends in optical properties of graphene. Doping site and concentration of N and Mg atoms in graphene was changed and resulting effects of these changes on aforementioned properties were investigated. Through density of states plots we observed that, Mg atom sp orbitals mainly induced magnetic moments in graphene. It was revealed that, N/Mg atoms substitution in graphene introduces a red shift in absorption spectrum towards visible range and a finite absorption coefficient quantity value in 0 to 3 eV and 7 to 11 eV energy intervals is also produced, that is unavailable for absorption spectrum of intrinsic graphene. Moreover, N/Mg atoms co-doping produces increment in the reflectivity parameter of graphene in low lying energy region, while producing diminishing behavior in the higher energy range. These results offer a possibility to tune electronic, magnetic and optical characteristics of graphene sufficiently for utilization in graphene based spintronic and optoelectronic devices.     

Water vapor adsorption onto activated carbons prepared from cattle manure compost (CMC)

Activated carbons were prepared from cattle manure compost (CMC) using zinc chloride activation. The structural and surface chemical characteristics of CMC-based activated carbons were determined by N₂ adsorption-desorption and Boehm titration, respectively. The water vapor adsorption properties of the prepared activated carbons with various pore structure and surface nature were examined, and the mechanism of water adsorbed onto activated carbon was also discussed. The results show that the adsorption of water vapor on carbons begins at specific active sites at low relative humidity (RH), followed by micropore filling at medium RH through the formation of pentamer cluster of water molecules in the narrow micropores. The water vapor adsorption capacity of activated carbon is predominantly dependent on its pore volume and surface area. Although capillary condensation is not the mechanism for water adsorption onto activated carbon, water can adsorb on narrow mesopore to some extent.     

Solvent Effects in the Homogeneous Catalytic Reduction of Propionaldehyde with Aluminium Isopropoxide Catalyst: New Insights from PFG NMR and NMR Relaxation Studies

Solvent effects in homogeneous catalysis are known to affect catalytic activity. Whilst these effects are often described using qualitative features, such as Kamlet-Taft parameters, experimental tools able to quantify and reveal in more depth such effects have remained unexplored. In this work, PFG NMR diffusion and T₁ relaxation measurements have been carried out to probe solvent effects in the homogeneous catalytic reduction of propionaldehyde to 1-propanol in the presence of aluminium isopropoxide catalyst. Using data on diffusion coefficients it was possible to estimate trends in aggregation of different solvents. The results show that solvents with a high hydrogen-bond accepting ability, such as ethers, tend to form larger aggregates, which slow down the molecular dynamics of aldehyde molecules, as also suggested by T₁ measurements, and preventing their access to the catalytic sites, which results in the observed decrease of catalytic activity. Conversely, weakly interacting solvents, such as alkanes, do not lead to the formation of such aggregates, hence allowing easy access of the aldehyde molecules to the catalytic sites, resulting in higher catalytic activity. The work reported here is a clear example on how combining traditional catalyst screening in homogeneous catalysis with NMR diffusion and relaxation time measurements can lead to new physico-chemical insights into such systems by providing data able to quantify aggregation phenomena and molecular dynamics.     

Rapid Learning-Based and Geologically Consistent History Matching

Various types of data become available at different stages of a reservoir’s life. The production data are integrated into the flow simulation models through a process referred to as history matching. The history-matching process is iterative, and it usually involves a large number of simulation runs. Hence, this process requires significant computational time. In most history-matching methods, the initial geological assumptions in the reservoir model are destroyed or significantly altered in the process. Furthermore, they do not account for the information obtained during the previous trials, and lack learning from the previous failures. In this paper, we introduce a new methodology that maintains the geological realism. The candidate realizations are selected through a learning-based history-matching (LHM) algorithm by which all the previously successful patterns are preserved and used to assist the construction of the next realizations. The various pieces of matching regions are assembled together to make a pool of the successful candidates. Such regions are then utilized for making an auxiliary dataset in a multiscale framework by which the next model is generated. To prevent from trapping in local minima, ideas from the genetic algorithm is adapted. The LHM algorithm can be applied to both categorical and continuous distributions. The LHM provides a conditional map by which the new production data are immediately incorporated into the existing reservoir models. We apply the LHM algorithm to various 2D and 3D examples with very complex binary and continuous properties. The algorithm is shown to produce history-matched models with significantly smaller CPU times.