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.     

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.     

Computer-assisted identification of multitrace electrophoretic patterns in differential display experiments

Modern multicapillary devices allow researchers to address increasingly complex biological questions involving comparisons of gene expression patterns across electrophoretic samples under various experimental conditions. As labor-intensive visual evaluation of the electrophoretic results is often the bottleneck of large-scale differential display (DD) studies, one way to further streamline this process is to focus only on a highly compressed list of the most potential patterns that are likely to provide reliable findings. To enable the identification of such candidate patterns, we present a computer-assisted method for objective ranking of multitrace peak patterns in DD experiments. The fundamental component of the multitrace pattern ranking method (MRANK) is the multiple alignment algorithm that allows for discovery of patterns involving sets of peak complexes from various electrophoretic samples. A score value is attached to each detected pattern which characterizes how accurately the pattern resembles the desired pattern query, freely defined by the researcher. The ranked pattern list produced by MRANK is validated against visual evaluation in terms of detecting and ranking a group of relevant patterns in a DD analysis of T-helper cell differentiation. We demonstrate high enrichment of the desired patterns on top of the score-ranked list (e.g., 90% of the visually selected patterns are discovered by looking through the first 3% of patterns in the ranked list of all patterns). The results suggest that a substantial amount of manual labor can be saved without compromising the accuracy of the findings by prioritizing the patterns according to MRANK output in the visual confirmation phase.     

Glass microfabricated nebulizer chip for mass spectrometry

A microfluidic nebulizer chip for mass spectrometry is presented. It is an all-glass device which consists of fusion bonded Pyrex wafers with embedded flow channels and a nozzle at the chip edge. A platinum heater is located on the wafer backside. Fabrication of the chip is detailed, especially glass deep etching, wafer bonding, and metal patterning. Various process combinations of bonding and metallization have been considered (anodic bonding vs. fusion bonding; heater inside/outside channel; metallization before/after bonding; platinum lift-off vs. etching). The chip vaporizes the liquid sample (0.1-10 microL min(-1)) and mixes it with a nebulizer gas (ca. 100 sccm N2). Operating temperatures can go up to 500 degrees C ensuring efficient vaporization. Thermal insulation of the glass ensures low temperatures at the far end of the chip, enabling easy interconnections.     

On-line monitoring of continuous beer fermentation process using automatic membrane inlet mass spectrometric system

A fully automatic membrane inlet mass spectrometric (MIMS) on-line instrumentation for the analysis of aroma compounds in continuous beer fermentation processes was constructed and tested. The instrumentation includes automatic filtration of the sample stream, flushing of all tubing between samples and pH control. The calibration standards can be measured periodically. The instrumentation has also an extra sample line that can be used for off-line sample collection or it can be connected to another on-line method. Detection limits for ethanol, acetic acid and eight organic beer aroma compounds were from μg l⁻¹ to low mg l⁻¹ levels and the standard deviations were less than 3.4%. The method has a good repeatability and linearity in the measurement range. Response times are shorter than or equal to 3 min for all compounds except for ethyl caproate, which has a response time of 8 min. In beer aroma compound analysis a good agreement between MIMS and static headspace gas chromatographic (HSGC) measurements was found. The effects of different matrix compounds commonly present in the fermentation media on the MIMS response to acetaldehyde, ethyl acetate and ethanol were studied. Addition of yeast did not have any effect on the MIMS response of ethanol or ethyl acetate. Sugars, glucose and xylose, increased the MIMS response of all studied analytes only slightly, whereas salts, ammonium chloride, ammonium nitrate and sodium chloride, increased the MIMS response of all three studied compounds prominently. The system was used for on-line monitoring of continuous beer fermentation with immobilised yeast. The results show that with MIMS it is possible to monitor the changes in the continuous process as well as delays in the two-phase process.