Capillary electrophoretic separation of uncharged polymers using polyelectrolyte engines. Theoretical model

Fixed-size moving window evolving factor analysis and base peak chromatograms have been used for peak purity detection in data generated with LC-MS. The two methods were evaluated with both real and simulated data and were found to be fast and complementary to each other. When a possibly impure peak is detected, it is suggested that further information can be obtained from local principal component analysis modelling and comparative mass chromatogram plots.     

Some Isoperimetric Inequalities in Electrochemistry and Hele Shaw Flows

Isoperimetric inequalities are applied to a moving-boundaryproblem for doubly-connected domains. This problem occurs forexample in electrochemistry, in which case the domains in questionare the electrolyte of an electrolytic cell. The two electrodessurrounding the electrolyte are assumed to grow or dissolve,at different rates in general, by electrochemical reaction.We obtain optimal estimates showing, for example, that the leastchange in volume of each electrode always occurs in sphericalsymmetry.     

Electronic structure of lanthanum transition-metal oxyarsenides LaMAsO (M = Fe,Co, Ni) and LaFe1−xM′xAsO (M′ = Co,Ni) by X-ray photoelectron and absorption spectroscopy

The electronic structures of the quaternary oxyarsenides LaMAsO (M = Fe, Co, Ni) were examined with X-ray photoelectron spectroscopy (XPS) and X-ray absorption near-edge spectroscopy (XANES). Interpretation of the metal 2p_3/2 and arsenic 3d_5/2 binding energies, as well as a satellite feature in the Co 2p XPS spectrum, suggests charges that are much less extreme than expected (i.e., not M²⁺ and As^3?) because of the strong covalent character within the M–As bonds. As M is varied, the differing degrees of charge transfer from M to As atoms within these bonds are manifested by shifts in the As 3d_5/2 binding energies and changes in the As K-edge intensities. This charge transfer is isolated within the [MAs] layer and does not influence the O 1s and La 3d XPS spectra. Fitting the experimental valence band spectra of these oxyarsenides LaMAsO yielded electron populations of states that support the formal charge assignment [La³⁺O^2?][M²⁺As^3?]. The mixed-metal series LaFe_1?xM′_xAsO (M′ = Co, Ni) was examined by XANES; analysis of the metal K- and L-edges, as well as of the Co 2p XPS satellite feature, revealed that no metal–metal charge transfer takes place.     

An Introduction to Closed/Open Neighborhood Sums: Minimax, Maximin, and Spread

For a graph G of order |V(G)| = n and a real-valued mapping f:V(G)?\mathbbR{f:V(G)\rightarrow\mathbb{R}}, if S ì V(G){S\subset V(G)} then f(S)=?_{w ? S} f(w){f(S)=\sum_{w\in S} f(w)} is called the weight of S under f. The closed (respectively, open) neighborhood sum of f is the maximum weight of a closed (respectively, open) neighborhood under f, that is, NS[f]=max{f(N[v])|v ? V(G)}{NS[f]={\rm max}\{f(N[v])|v \in V(G)\}} and NS(f)=max{f(N(v))|v ? V(G)}{NS(f)={\rm max}\{f(N(v))|v \in V(G)\}}. The closed (respectively, open) lower neighborhood sum of f is the minimum weight of a closed (respectively, open) neighborhood under f, that is, NS^-[f]=min{f(N[v])|v ? V(G)}{NS^{-}[f]={\rm min}\{f(N[v])|v\in V(G)\}} and NS^-(f)=min{f(N(v))|v ? V(G)}{NS^{-}(f)={\rm min}\{f(N(v))|v\in V(G)\}}. For W ì \mathbbR{W\subset \mathbb{R}}, the closed and open neighborhood sum parameters are NS_W[G]=min{NS[f]|f:V(G)? W{NS_W[G]={\rm min}\{NS[f]|f:V(G)\rightarrow W} is a bijection} and NS_W(G)=min{NS(f)|f:V(G)? W{NS_W(G)={\rm min}\{NS(f)|f:V(G)\rightarrow W} is a bijection}. The lower neighbor sum parameters are NS^-_W[G]=maxNS^-[f]|f:V(G)? W{NS^{-}_W[G]={\rm max}NS^{-}[f]|f:V(G)\rightarrow W} is a bijection} and NS^-_W(G)=maxNS^-(f)|f:V(G)? W{NS^{-}_W(G)={\rm max}NS^{-}(f)|f:V(G)\rightarrow W} is a bijection}. For bijections f:V(G)? {1,2,?,n}{f:V(G)\rightarrow \{1,2,\ldots,n\}} we consider the parameters NS[G], NS(G), NS ⁻[G] and NS ⁻(G), as well as two parameters minimizing the maximum difference in neighborhood sums.     

Synthesis of silicon doped SrMO3 (M = Mn, Co): stabilization of the cubic perovskite and enhancement in conductivity

In this paper we report the successful incorporation of silicon into SrMO(3) (M = Co, Mn) leading to a structural change from a hexagonal to a cubic perovskite. For M = Co, the cubic phase was observed for low doping levels (3%), and these doped phases showed very high conductivities (up to ≈350 Scm(-1) at room temperature). However, annealing studies at intermediate temperatures (700-800 °C), indicated that the cubic phase was metastable with a gradual transformation to a hexagonal cell on annealing. Further work showed that co-doping with Fe resulted in improved stability of the cubic phase; a composition SrCo(0.85)Fe(0.1)Si(0.05)O(3-y) displayed good stability at intermediate temperatures and a high conductivity (≈150 Scm(-1) at room temperature). For M = Mn, the work showed that higher substitution levels were required to form the cubic perovskite (≈15% Si doping), although in these cases the phases were shown to be stable to annealing at intermediate temperatures. Conductivity measurements again showed an enhancement in the conductivity on Si doping, although the conductivities were lower (≈0.3-14 Scm(-1) in the range 20-800 °C) than the cobalt containing systems. The conductivities of both systems suggest potential for use as cathode materials in solid oxide fuel cells.     

The diffusion coefficient of a polymer in an array of obstacles is a non-monotonic function of the degree of disorder in the medium

Using Monte Carlo simulations, we have found that there is a surprising non-monotonic dependence of a polymer's diffusion coefficient upon the degree of disorder of the surrounding environment. Starting with a two-dimensional periodic lattice of obstacles, we randomly displace obstacles to create a quenched gel system with a tunable degree of disorder. Very small displacements increase the diffusion coefficient of polymers since they increase the width of the tube through which the polymer chains reptate. As we displace the obstacles further, however, entropic trapping is observed and the diffusion coefficient of the polymer decreases dramatically. This is a striking example of the delicate balance between entropic and frictional effects for a polymer diffusing in a dense system.     

Convex labelings of trees

A convex labeling of a tree T of order n is a one-to-one function f from the vertex set of T into the nonnegative integers, so that f(y) ? (f(x) + f(z))/2 for every path x, y, z of length 2 in T. If, in addition, f(v) ? n ? 1 for every vertex v of T, then f is a perfect convex labeling and T is called a perfectly convex tree. Jamison introduced this concept and conjectured that every tree is perfectly convex. We show that there exists an infinite class of trees, none of which is perfectly convex, and in fact prove that for every n there exists a tree of order n which requires a convex labeling with maximum value at least 6n/5 – 22. We also prove that every tree of order n admits a convex labeling with maximum label no more than n²/8 + 2. In addition, we present some constructive methods for obtaining perfect convex labelings of large classes of trees.