Interactions of short chain phenylalkanoic acids within ionic surfactant micelles in aqueous media

% SDS KR nema Solubilization and interactions of phenylalkanoic acids induced by cationic surfactant, cetyltrimethylammonium bromide (CTAB) and an anionic surfactant, sodium dodecyl sulfate (SDS) was investigated spectrophotometrically at 25.0°C. The UV spectra of the additives (acids) were measured with and without surfactant above and below critical micelle concentration (cmc) of the surfactant. The presence of alkyl chain in phenylalkanoic acids is responsible for hydrophobic interaction resulting in shift of the spectra towards longer wavelength (red shift). The value of partition coefficient (Kx) between the bulk water and surfactant micelles and in turn standard free energy change of solubilization (ΔGpº) were also estimated by measuring the differential absorbance (ΔA) of the additives in micellar solutions

Island Size Selectivity and island-shape analysis during 2D Island Coarsening of Ag/Ag (111) Surface

In our earlier study of Ag island coarsening on Ag(111) surface using kinetic Monte Carlo (KMC) simulations we found that during early stages coarsening proceeds as a sequence of selected island sizes resulting in peaks and valleys in the island-size distribution and that this selectivity is independent of initial conditions and dictated instead by the relative energetics of edge-atom diffusion and detachment/attachment processes and by the large activation barrier for kink detachment. In this paper we present a detailed analysis of the shapes of various island sizes observed during these KMC simulations and show that selectivity is due to the formation of kinetically stable island shapes which survive longer than non-selected sizes, which decay into nearby selected sizes. The stable shapes have a closed-shell structure - one in which every atom on the periphery having at least three nearest neighbors. Our KMC simulations were carried out using a very large database of processes identified by each atom's unique local environment, the activation barriers of which were calculated using semi-empirical interaction potentials based on the embedded-atom method.Comment: 17 pages, 11 figure

SLKMC-II study of self-diffusion of small Ni clusters on Ni (111) surface

We studied self-diffusion of small 2D Ni islands (consisting of up to 10 atoms) on Ni (111) surface using a self-learning kinetic Monte Carlo (SLKMC-II) method with an improved pattern-recognition scheme that allows inclusion of both fcc and hcp sites in the simulations. In an SLKMC simulation, a database holds information about the local neighborhood of an atom and associated processes that is accumulated on-the-fly as the simulation proceeds. In this study, these diffusion processes were identified using the drag method, and their activation barriers calculated using a semi-empirical interaction potential based on the embedded-atom method. Although a variety of concerted, multi-atom and single-atom processes were automatically revealed in our simulations, we found that these small islands diffuse primarily via concerted diffusion processes. We report diffusion coefficients for each island size at various tepmratures, the effective energy barrier for islands of each size and the processes most responsible for diffusion of islands of various sizes, including concerted and multi-atom processes that are not accessible under SLKMC-I or in short time-scale MD simulations

Performance Evaluation of Mutual Funds in Pakistan

In Pakistan Mutual Funds were introduced in 1962, when the public offering of National Investment (Unit) Trust (NIT) was introduced which is an open-end mutual fund. In 1966 another fund that is Investment Corporation of Pakistan (ICP) was establishment. ICP subsequently offered a series of closed-end mutual funds. Up to early 1990s, twenty six (26) closed-end ICP mutual funds had been floated by Investment Corporation of Pakistan. After considering the option of restructuring the corporation, government decided to wind up ICP in June, 2000. In 2002, the Government started Privatisation of the Investment Corporation of Pakistan. 25 Out of 26 closed-end funds of ICP were split into two lots. There had been a competitive bidding for the privatisation of funds. Management Right of Lot-A comprising 12 funds was acquired by ABAMCO Limited. Out of these 12, the first 9 funds were merged into a single closed-end fund and that was named as ABAMCO Capital Fund, except 4th ICP mutual fund as the certificate holders of the 4th ICP fund had not approved the scheme of arrangement of Amalgamation into ABAMCO capital fund in their extra ordinary general meeting held on December 20, 2003. The fund has therefore been reorganised as a separate closedend trust and named as ABAMCO Growth Fund. Rest of the three funds were merged into another single and named as ABAMCO Stock Market Fund. So far as the Lot-B is concerned, it comprised of 13 ICP funds, for all of these thirteen funds, the Management Right was acquired by PICIC Asset Management Company Limited. All of these thirteen funds were merged into a single closed-end fund which was named as “PICIC Investment Fund”. Later on the 26th fund of ICP (ICP-SEMF) was also acquired by PICIC Asset Management Company Limited

Key Features of SARS-CoV-2 and Available Therapies for COVID-19

The disease caused by severe acute respiratory syndrome (SARS-CoV2) is highly pathogenic and communicable infection, progressed in Wuhan city of China and then goes viral around the globe. The Genomic investigations exposed that Phylogenetically SARS-CoV2 resembles the other SARS-like bat viruses, therefore bats were also considered as the possible potential reservoir for SARS-CoV2. There are 2 prevalent types of SARS-CoV2, L type (~70%) and S type (~30%).The L strains are considered more infectious and virulent than the ancestral S strain. The positive sense single-stranded RNA genetic material contains 29891 nucleotides which codes for 9860 amino acids. The ORF1a/b is involved in carrying the translation of two (2) polyproteins, pp1a and pp1ab as well as the encoding of 16 NSPs (Non-structural proteins), and the leftover ORFS can bring about the encoding of non-essential and structural proteins. The origination source and transmission to humankinds is still not clear, but the intermediate hosts are supposed to have a significant role in the transfer and emergence of SARS-CoV2 from bats to humans. There is still no approved drug or vaccine available for Covid-19. In the current review, we condense and fairly evaluate the emergence and pathogenicity of SARS-CoV2, SARS-CoV and MERS-CoV. Moreover, we also discuss the treatment and vaccine developments strategies for Covid-19

Functionalized Multi-Walled Carbon Nanotube-Reinforced Epoxy-Composites: Electrical And Mechanical Characterization

Carbon nanotubes (CNTs) got great attention because of their interesting physical and mechanical properties. Due to these interesting properties observed at the nanoscale have motivated scientific community to utilize CNTs as reinforcement in composite materials. In the present study, different CNTs and epoxy nano-composites with different wt% (1, 2, 3, and 4%) of f-MWCNTs were prepared and their surface morphology and orientation has been investigated in detail. Further, the surface investigation, electrical and mechanical tests were carried out on CNTs-filled and unfilled epoxy at maximum sonication time 30 minute to identify the loading effect on the properties of the materials. Experimental results depicts well dispersion of f-MWCNTs, significant improvement that the resistivity of pure epoxy decreased from 108 .m to average value 103 .m with 1, 2, 3, and 4wt% f-MWCNTs. The 4.5wt% CNTs/epoxy was attributed to poor dispersion of f-MWCNTs in the nanocomposte. The hardness of nanocomposite loading 1, 2, 3, 4wt% of CNTs, increased 20.7%, 23.02%, 25.62%, 29.09% respectively as compared to pure epoxy. We believe that our strategy for obtaining CNT–reinforced epoxy nanocomposites is a very promising technology and will open a new doors in fields of aviation, aerospace, marine and sporting goods

Extended Pattern Recognition Scheme for Self-learning Kinetic Monte Carlo (SLKMC-II) Simulations

We report the development of a pattern-recognition scheme that takes into account both fcc and hcp adsorption sites in performing self-learning kinetic Monte Carlo (SLKMC-II) simulations on the fcc(111) surface. In this scheme, the local environment of every under-coordinated atom in an island is uniquely identified by grouping fcc sites, hcp sites and top-layer substrate atoms around it into hexagonal rings. As the simulation progresses, all possible processes including those like shearing, reptation and concerted gliding, which may involve fcc-fcc, hcp-hcp and fcc-hcp moves are automatically found, and their energetics calculated on the fly. In this article we present the results of applying this new pattern-recognition scheme to the self-diffusion of 9-atom islands (M9) on M(111), where M = Cu, Ag or Ni

Kinetically driven shape changes in early stages of two-dimensional island coarsening: Ag/Ag(111)

We present here a detailed analysis of the shapes of two-dimensional Ag islands of various sizes observed during the early stages of coarsening on the Ag(111) surface, using kinetic Monte Carlo (KMC) simulations, and show that selectivity is due to the formation of kinetically stable island shapes that survive longer than nonselected sizes, which decay into nearby selected sizes. The stable shapes have a closed-shell structure-one in which every atom on the periphery has at least three nearest neighbors. These findings further explain our earlier study in which we found that in the early stages coarsening proceeds as a sequence of selected island sizes resulting in peaks and valleys in the island size distribution [G. Nandipati, A. Kara, S. I. Shah, and T. S. Rahman, J. Phys.: Condens. Matter 23, 262001 (2011)]. This selectivity is dictated by the relative energetics of edge-atom diffusion and detachment and attachment processes and by the large activation barrier for kink detachment. Our simulations were carried out using a very large database of processes identified by each atom\u27s unique local environment using the self-learning KMC scheme. The activation barriers were calculated using semiempirical interaction potentials based on the embedded-atom method