Effect of cationic polymethacrylates on the rheology and flocculation of microfibrillated cellulose

Using two cationic methacrylate polymers: poly([2-(methacryloyloxy)ethyl] trimethyl ammonium iodide) (PDMQ) and poly[(stearyl methacrylate)-stat-([2-(methacryloyloxy)ethyl] trimethyl ammonium iodide)] (PSMA13Q), we modified microfibrillated cellulose (MFC) water suspensions. The aim was to affect the flocculation and rheological behavior of the MFC suspension. PDMQ is a strongly cationic polymer while PSMA13Q, also a cationic polymer, contains hydrophobic segments. We studied the MFC/polymer suspension rheological properties with a rotational rheometer in oscillatory and flow measurements. To observe structural changes in suspensions at different shear rates, we measured flow curves with transparent outer geometry and photographed the sample with a digital camera. The oscillatory measurements showed that a small amount of the cationic PDMQ in the MFC suspension strengthened the gel, whereas a small amount of amphiphilic PSMA13Q weakened it. Increased amounts of either polymer increased the gel strength. PSMA13Q also changed the rheological character of the MFC suspension turning it more fluid-like. When we photographed the flow curve measurement, we saw a clear change in the floc structure. This floc structure rupture coincided with a transient region in the flow curve.     

A reformulation framework for global optimization

In this paper, we present a global optimization method for solving nonconvex mixed integer nonlinear programming (MINLP) problems. A convex overestimation of the feasible region is obtained by replacing the nonconvex constraint functions with convex underestimators. For signomial functions single-variable power and exponential transformations are used to obtain the convex underestimators. For more general nonconvex functions two versions of the so-called αBB-underestimator, valid for twice-differentiable functions, are integrated in the actual reformulation framework. However, in contrast to what is done in branch-and-bound type algorithms, no direct branching is performed in the actual algorithm. Instead a piecewise convex reformulation is used to convexify the entire problem in an extended variable-space, and the reformulated problem is then solved by a convex MINLP solver. As the piecewise linear approximations are made finer, the solution to the convexified and overestimated problem will form a converging sequence towards a global optimal solution. The result is an easily-implementable algorithm for solving a very general class of optimization problems.     

Quantification of human brain metabolites from in vivo 1H NMR magnitude spectra using automated artificial neural network analysis

Long echo time (TE=270 ms) in vivo proton NMR spectra resembling human brain metabolite patterns were simulated for lineshape fitting (LF) and quantitative artificial neural network (ANN) analyses. A set of experimental in vivo 1H NMR spectra were first analyzed by the LF method to match the signal-to-noise ratios and linewidths of simulated spectra to those in the experimental data. The performance of constructed ANNs was compared for the peak area determinations of choline-containing compounds (Cho), total creatine (Cr), and N-acetyl aspartate (NAA) signals using both manually phase-corrected and magnitude spectra as inputs. The peak area data from ANN and LF analyses for simulated spectra yielded high correlation coefficients demonstrating that the peak areas quantified with ANN gave similar results as LF analysis. Thus, a fully automated ANN method based on magnitude spectra has demonstrated potential for quantification of in vivo metabolites from long echo time spectroscopic imaging.     

Cellulose-g-PDMAam copolymers by controlled radical polymerization in homogeneous medium and their aqueous solution properties

Water-soluble cellulose-graft-PDMAam copolymers were prepared by single-electron-transfer living radical polymerization (SET-LRP). Cellulose based macroinitiator for SET-LRP with a degree of substitution DS ≈ 2 was synthesized from softwood dissolving pulp in a homogeneous LiCl/DMAc solution. The macroinitiator was then grafted using N,N-dimethyl acrylamide (DMAam) in DMSO. Formation of cellulose-g-DMAam copolymers were confirmed by ATR–FTIR, ¹H and ¹³C NMR spectroscopy and SEC analyses. Light scattering and steady–shear viscosity measurements revealed that the studied chain length of grafts (DP_graft) had only minor effects on the solution properties of cellulose-g-PDMAam copolymers. SLS studies suggested a loose, solvent-draining architecture of the cellulose-g-PDMAam copolymer particles in H₂O.     

Organic‐Inorganic Hybrid Materials: Syntheses,X‐ray Diffraction Study,and Characterisations of Manganese,Cobalt, and Copper Complexes of Modified Bis(phosphonates)

Four new cobalt, manganese, and copper bis(phosphonates), [Co₂{Cl₂C(PO₃)₂}(H₂O)₇ · 4H₂O] ( 1 ), [Co{Cl₂C(PO₂O(C(O)C₆H₅))₂(H₂O)₅} · 2H₂O{Cl₂C(PO₂O(C(O)C₆H₅))₂}{Co(H₂O)₆}] ( 2 ), [Mn{[Cl₂C(PO₂O(C(O)C₆H₅))₂](H₂O)₃}] ( 3 ), and [Cu{(CH₂C₅H₅N)C(OH)(PO₃H)₂}₂ · 4H₂O] ( 4 ), were prepared by gel, liquid, and evaporation crystallisation methods. Compounds 1 – 4 were characterised by X‐ray single‐crystal diffraction, elemental analysis, infrared spectroscopy, and thermogravimetric analysis. The effects of metal and various substituted groups in bis(phosphonate) ligands on the structure formation of bis(phosphonates) were studied. In the structure of 1 , the clodronic acid ligand ( L1 ) is in bischelating bonding mode, and the dinuclear units of 1 are surrounded by two‐dimensional water cluster patterns. The hydrogen bond network of compound 1 is extended to a three‐dimensional framework when the phosphonate oxygen atoms serve as hydrogen‐bond acceptors. In complex 2 , the CoO₆ octahedron shares a corner of one PCO₃ tetrahedron of the dibenzoyl derivative of clodronic acid ligand ( L2 ), and forms a two‐dimensional hydrogen bonding network, which consists of [Co(H₂O)₆}]²⁺ cations, lattice water molecules and L2 ligand molecules. Compound 3 , in turn, consists of dimeric building blocks built up of PCO₃ tetrahedra of the ligand L2 , which connect the corner‐sharing MnO₆ octahedra and form an overall 2D structure through hydrogen bonds of coordinated and crystal water molecules and phosphonate oxygen atoms. Complex 4 is among the first metal complexes of risedronic acid ( L3 ). In compound 4 , two L3 ligand molecules chelate tridentately the Cu^II atom at the center of symmetry, and the monomeric units of 4 are connected to a 3D structure through hydrogen bonding of coordinated and lattice water molecules to both protonated and deprotonated phosphonate oxygen atoms and protonated nitrogen atoms in the pyridine ring.     

Learning intransitive reciprocal relations with kernel methods

In different fields like decision making, psychology, game theory and biology, it has been observed that paired-comparison data like preference relations defined by humans and animals can be intransitive. Intransitive relations cannot be modeled with existing machine learning methods like ranking models, because these models exhibit strong transitivity properties. More specifically, in a stochastic context, where often the reciprocity property characterizes probabilistic relations such as choice probabilities, it has been formally shown that ranking models always satisfy the well-known strong stochastic transitivity property. Given this limitation of ranking models, we present a new kernel function that together with the regularized least-squares algorithm is capable of inferring intransitive reciprocal relations in problems where transitivity violations cannot be considered as noise. In this approach it is the kernel function that defines the transition from learning transitive to learning intransitive relations, and the Kronecker-product is introduced for representing the latter type of relations. In addition, we empirically demonstrate on two benchmark problems, one in game theory and one in theoretical biology, that our algorithm outperforms methods not capable of learning intransitive reciprocal relations.     

Spectral methods and cluster structure in correlation-based networks

We investigate how in complex systems the eigenpairs of the matrices derived from the correlations of multichannel observations reflect the cluster structure of the underlying networks. For this we use daily return data from the NYSE and focus specifically on the spectral properties of weight W_ij=|C|_ij−δ_ij and diffusion matrices D_ij=W_ij/s_j−δ_ij, where C_ij is the correlation matrix and s_i=∑_jW_ij the strength of node j. The eigenvalues (and corresponding eigenvectors) of the weight matrix are ranked in descending order. As in the earlier observations, the first eigenvector stands for a measure of the market correlations. Its components are, to first approximation, equal to the strengths of the nodes and there is a second order, roughly linear, correction. The high ranking eigenvectors, excluding the highest ranking one, are usually assigned to market sectors and industrial branches. Our study shows that both for weight and diffusion matrices the eigenpair analysis is not capable of easily deducing the cluster structure of the network without a priori knowledge. In addition we have studied the clustering of stocks using the asset graph approach with and without spectrum based noise filtering. It turns out that asset graphs are quite insensitive to noise and there is no sharp percolation transition as a function of the ratio of bonds included, thus no natural threshold value for that ratio seems to exist. We suggest that these observations can be of use for other correlation based networks as well.     

Thermal stability of the structural features in the superhydrophobic boehmite films on austenitic stainless steels

Boehmite thin film with 50–100 nm surface flake structure has been synthesized on AISI 316 type austenitic stainless steel by immersing boehmite gel film into boiling water. When further coated with hydrolyzed (heptadecafluoro-1,1,2,2-tetrahydrodecyl) trimethoxysilane (FAS), the boehmite film becomes superhydrophobic with a contact angle for water of 152°. The superhydrophobic property results from both the nanoscale surface flake structure and the low surface energy of the FAS top layer. The topography of such film was revealed by atomic force microscope (AFM) and a set of roughness parameters of such film was discussed. The degradation of superhydrophobicity of the surface was studied as a function of the heat-treatment temperatures. Below 600 °C, the surface remained to be superhydrophobic with the FAS top layer. Above 700 °C, the surface was not superhydrophobic anymore due to a gradual loss in surface roughness which was revealed by field emission scanning electron microscope (FESEM). A phase change from boehmite to γ-Al₂O₃ occurred during the heat-treatments from 700 to 900 °C which was studied by the selected area electron diffraction (SAED) patterns from the transmission electron microscope (TEM) measurement.     

Characterization of water exposed plasma sprayed oxide coating materials using XPS

The surface compositions and oxidation states of non-exposed and water exposed plasma sprayed oxide coatings were studied using X-ray photoelectron spectroscopy (XPS). Coating materials were TiO₂, Al₂O₃ and Cr₂O₃ and their mixtures. Water exposures were performed for free standing coating disks at mild electrolyte (1 mmol NaCl solution) at pH 4, 7 and 9. The exposure time was two weeks.It was observed that pure plasma sprayed TiO₂ material was chemically stable over whole experiment pH range and only slight surface hydroxylation was observed for this material.In case of plasma sprayed Al₂O₃ materials the surface O/Al ratio increased considerably during water exposure especially at exposure pH 7. This was probably result of surface conversion to hydrous form. No surface oxidation state changes were observed for this material.The non-exposed Cr₂O₃ materials contained both Cr(III) and Cr(VI) oxides. The water exposures increased the surface oxygen and Cr(VI) contents at the expense of Cr(III). The most probable reason for that was the dissolution of surface Cr(VI) oxide phase during water exposures and the (re)adsorption of dissolved Cr(VI) species back to the surface.     

Solvent desorption of asphaltenes from solid surfaces

The main goal of this paper is to compare the ability of different organic solvents to desorb asphaltenes from stainless steel surfaces. The asphaltenes were extracted from a North Sea crude oil by precipitation. The organic solvents are characterized based on their Hansen solubility parameters (HSPs). The adsorption of asphaltenes was followed by means of a Quartz Crystal Microbalance with Dissipation (QCM-D). The asphaltene desorption efficiency of the solvents tested varied between 20% and 70%, with pyridine as the most efficient solvent. Carbon disulfide was found to be a poor desorption solvent, indicating the importance of solvent polarity. A simple model based on the HSPs seemed to give a good quantitative explanation of experimental desorption experiments.