Synthesis of visible light-active nanostructured TiOx (x < 2) photocatalysts in a flame aerosol reactor

Titanium dioxide is a wide band gap (3.2 eV) semiconductor which is photo-active when irradiated with UV light. For wider scale use of TiO₂ as a photocatalyst, its activity needs to be extended to the visible light region (constituting 45% of total incident solar energy). A diffusion flame aerosol reactor (FLAR) with an oxygen lean environment in the particle formation zone has been used to synthesize oxygen deficient titanium suboxide (TiO_x with x < 2) nanoparticles. Using a standard-based electron energy loss spectroscopy (EELS) technique, the non-stoichiometry (x in TiO_x) in the flame synthesized particles has been quantified with high accuracy (uncertainty less than 3%). Under an oxygen lean environment in the particle formation zone, the non-stoichiometry in the TiO_x particles is a function of the flame temperature. The value of x in the flame synthesized TiO_x nanoparticles is in the range of 1.88 < x < 1.94. Diffuse reflectance spectra confirmed that the oxygen deficient TiO_x particles absorbed visible light. Visible light activity of the TiO_x particles is demonstrated by photocatalytic degradation of methyl orange solution under visible light illumination.     

Processing-Property interactions in vinylidene fluoride/trifluoroethylene random copolymers

The effect of flow history, processing temperature, and exit draw ratio have been studied for copolymers of vinylidene fluoride/trifluoroethylene of molar compositions 66/34 and 75/25%. The copolymers were extruded through an impinging channels film die which produces a mixed extension and shearing flow as well as a slit die. Stress fields during flow were measured using flow birefringence. Differential Scanning Calorimetry (DSC) analyses were used to characterize the thermal behavior; and flat film and Wide Angle X-ray Scattering (WAXS) were used to evaluate the crystal structure and morphology of the extrudates. Extensional melt stresses on the order of 6.35 × 10⁵ Pa were necessary to induce sufficient orientation for crystallization of water-cooled 66/34 mol % copolymers into the all-trans configuration. Preorientation of the melt by extensional stresses enhanced the transformation of the 3/1 helical to an all-trans structure in the 66/34 copolymer as well as producing a more compact unit cell in the 75/25 mol % copolymer leading to as much as a 7°C higher Curie transition temperature.     

Processing–property interactions in poly(vinylidene fluoride). II. Morphology and property characterization of extruded films

The effects of flow history, processing temperature, and exit draw ratio have been studied for a poly(vinylidene fluoride) resin. Quantification of the stress fields and flow kinematics were described in Part I while, in this publication, attention has been addressed to the evaluation of film properties. Hot-stage and differential scanning colorimetry (DSC) analyses were used to characterize the thermal behavior; polarized light optical microscopy and electron microscopy were used to characterize the morphology; Fourier transform infrared (FTIR) and wide-angle x-ray scattering (WAXS) were used to evaluate crystal structure; and mechanical testing was used to evaluate tensile properties. Extensional melt stresses on the order of 1.4 × 10⁶ dyne/cm² were necessary to induce row-nucleated crystallization in undrawn samples, and in all cases, preorientation of the melt by extensional flow enhanced the efficiency of the α → β transformation with drawing. The various transformations on drawing were as follows: unoriented α to oriented superheatable α phase for draw ratio (DR) < 5; transformation from α to β phase for 5 < DR ≤ 25; transformation to more highly oriented α and β phases, DR > 25.     

Kinetic and thermodynamic studies of AISI 4130 steel alloy corrosion in ethylene glycol-water mixture in presence of inhibitors

The electrochemical behavior of steel alloy in ethylene glycol-water mixture was investigated by electrochemical methods. The results obtained showed that corrosion rate was decreased with increasing ethylene glycol concentration. The effect of inorganic inhibitors including NO₃ ^?, NO₂ ^?, Cr₂O₇ ^2? and CrO₄ ^2? were studied using electrochemical techniques where the highest inhibition efficiency was obtained for CrO₄ ^2?. In the presence of chromate the inhibitor efficiency increased with its concentration. The inhibiting effect of the chromate was explained on the basis of the competitive adsorption between the inorganic anions and the aggressive Cl^? ions, and the adsorption isotherm basically obeys the Langmuir adsorption isotherm. Thermodynamic parameters for steel corrosion and inhibitor adsorption were determined and reveal that the adsorption process is spontaneous. Also, a phenomenon of both physical and chemical adsorption is proposed.     

Impact of particle morphology on surface oxidation of nanoparticles: A kinetic Monte Carlo based study

A high‐fidelity coagulation driven kinetic Monte Carlo (KMC) model is developed to study the physics of the nonlinear interplay between competing exothermic collision‐coalescence mediated surface oxidation and complex morphologies in aggregated nanostructures generated during gas‐phase synthesis of nanoparticles. Results suggest a twofold oxidation mechanism in which thermally activated processes form a critical oxide shell, beyond which morphological complexity of nanoparticles gives rise to enhanced oxidation. Simulation results for the example case‐study of Al nanoparticle synthesis in air under different prototypical processing conditions, i.e., temperature, pressure and volume loading, show the efficacy of the model in determining optimal process variables for tuning the structural and chemical makeup of energetic nanomaterials. Finally, it is demonstrated that inclusion of nonisothermal coalescence that leads to the formation of fractal‐like nanoparticles (particularly, < 15 nm) gives rise to higher degrees of oxidation when compared to instantly coalescing spherical particles. © 2012 American Institute of Chemical Engineers AIChE J, 2012     

The effect of interfacial instabilities on the strength of the interface in two-layer plastic structures

We have experimentally investigated the coupling between interfacial instabilities and mechanical interlocking in polymeric films consisting of the incompatible polymer pair of polypropylene/high-density polyethylene and the compatible polymer pair of linear low-density polyethylene/high-density polyethylene. Our experimental results show that mechanical interlocking between the two phases can be achieved by controlling the extent of interfacial instabilities by properly selecting the initial disturbance frequency and amplitude as well as the layer depth ratio. Additionally, it has been shown that strength enhancement of the interface due to mechanical interlocking is directly proportional to the extent of wave bending in the processing apparatus. In fact, it has been demonstrated that in our test geometry maximum strength enhancement can be achieved at dimensionless wavenumbers near unity that correspond to disturbances with the largest growth rates. Overall, it has been shown that mechanical interlocking induced by a controlled amount of interfacial instabilities (i.e., based on knowledge of the stability of the interface and growth/decay rate of interfacial waves) can significantly increase the interfacial strength of two layer polymeric structures consisting of incompatible and compatible polymer pairs. Moreover, this effect is more pronounced in incompatible polymer pairs that possess negligible interfacial strength in absence of mechanical interlocking.     

Elongational flow in a two-dimensional channel geometry

An analysis has been carried out of the two-dimensional elongational flow in an impinging channel geometry having either straight or converging wall downstream ducts. Numerical solutions for Stokes flow were obtained using a nonorthogonal transformation of variables which converts the system to a square grid geometry. Calculations show that a strong extensional flow exists from the point of channel impingement to a distance downstream approximately D/4 where D is the channel depth at the impingement point. Extensional gradients and total fluid strains also increase when the downstream duct is convergent as opposed to being straight. An experimental analysis of the velocity field in the former geometry demonstrates that, under slow flow conditions, the kinematics of a Newtonian and a highly non-Newtonian fluid become indistinguishable in the downstream region. The latter observation is shown to be consistent with second-order fluid theory and the Giesekus-Tanner Theorem.     

Minimum positive influence dominating set and its application in influence maximization: a learning automata approach

In recent years, with the rapid development of online social networks, an enormous amount of information has been generated and diffused by human interactions through online social networks. The availability of information diffused by users of online social networks has facilitated the investigation of information diffusion and influence maximization. In this paper, we focus on the influence maximization problem in social networks, which refers to the identification of a small subset of target nodes for maximizing the spread of influence under a given diffusion model. We first propose a learning automaton-based algorithm for solving the minimum positive influence dominating set (MPIDS) problem, and then use the MPIDS for influence maximization in online social networks. We also prove that by proper choice of the parameters of the algorithm, the probability of finding the MPIDS can be made as close to unity as possible. Experimental simulations on real and synthetic networks confirm the superiority of the algorithm for finding the MPIDS Experimental results also show that finding initial target seeds for influence maximization using the MPIDS outperforms well-known existing algorithms.     

CFIN: A community-based algorithm for finding influential nodes in complex social networks

Influence maximization (IM) problem, a fundamental algorithmic problem, is the problem of selecting a set of k users (refer as seed set) from a social network to maximize the expected number of influenced users (also known as influence spread). Due to the numerous applications of IM in marketing, IM has been studied extensively in recent years. Nevertheless, many algorithms do not take into consideration the impact of communities to influence maximization and some algorithms are non-scalable and time-consuming in practice. In this paper, we proposed a fast and scalable algorithm called community finding influential node (CFIN) that selects k users based on community structure, which maximizes the influence spread in the networks. The CFIN consists of two main parts for influence maximization: (1) seed selection and (2) local community spreading. The first part of CFIN is the extraction of seed nodes from communities which obtained the running of the community detection algorithm. In this part, to decrease computational complexity effectively and scatter seed nodes into communities, the meaningful communities are selected. The second part consists of the influence spread inside communities that are independent of each other. In this part, the final seed nodes entered to distribute the local spreading by the use of a simple path inside communities. To study the performance of the CFIN, several experiments have been conducted on some real and synthetic networks. The experimental simulations on the CFIN, in comparison with other algorithms, confirm the superiority of the CFIN in terms of influence spread, coverage ratio, running time, and Dolan-Moré performance profile.