Scale-up and control of droplet production in coupled microfluidic flow-focusing geometries

A single microfluidic chip consisting of six microfluidic flow-focusing devices operating in parallel was developed to investigate the feasibility of scaling microfluidic droplet generation up to production rates of hundreds of milliliters per hour. The design utilizes a single inlet channel for both the dispersed aqueous phase and the continuous oil phase from which the fluids were distributed to all six flow-focusing devices. The exit tubing for each of the six flow-focusing devices is separate and individually plumbed to each device. Within each flow-focusing device, the droplet size was monodisperse, but some droplet size variations were observed across devices. We show that by modifying the flow resistance in the outlet channel of an individual flow-focusing device it is possible to control both the droplet size and frequency of droplet production. This can be achieved through the use of valves or, as is done in this study, by changing the length of the exit tubing plumbed to the outlet of the each device. Longer exit tubing and larger flow resistance is found to lead to larger droplets and higher production frequencies. The devices can thus be individually tuned to create a monodisperse emulsion or an emulsion with a specific drop size distribution.     

Effect of the exposure angle in the corrosion rate of plain carbon steel in a marine atmosphere

This investigation aims to analyse the effect of the exposure angle on the corrosion rate of mild steel. Test samples were exposed to a marine environment at 0°, 30°, 45° and 90° inclination. To determine the effects of contaminants on the protective characteristics of the rusts, Cl and SO₂ contents in the atmosphere as well as SEM-EDX analyses and polarisation curves on the weathered samples were performed. Results demonstrated that the exposure angle influences the corrosion rate, as also the morphology of the rust, but with no effect on rust composition.     

Quantification of secondary structure prediction improvement using multiple alignments

The use of multiple sequence alignments for secondary structurepredictions is analysed. Seven different protein families, containingonly sequences of known structure, were considered to providea range of alignment and prediction conditions. Using alignmentsobtained by spatial superposition of main chain atoms in knowntertiary protein structures allowed a mean of 8% in secondarystructure prediction accuracy, when compared to those obtainedfrom the individual sequences. Substitution of these alignmentsby those determined directly from an automated sequence alignmentalgorithm showed variations in the prediction accuracy whichcorrelated with the quality of the multiple alignments and distanceof the primary sequence. Secondary structure predictions canbe reliably improved using alignments from an automatic alignmentprocedure with a mean increase of 6.87percnt;, giving an overallprediction accuracy of 68.5%, if there is a minimum of 25% sequenceidentity between all sequences in a family.     

Good Enough is Good Enough: Overcoming Disaster Response Organizations’ Slow Social Media Data Adoption

Organizations that respond to disasters hold unreasonable standards for data arising from technology-enabled citizen contributions. This has strong negative potential for the ability of these responding organizations to incorporate these data into appropriate decision points. We argue that the landscape of the use of social media data in crisis response is varied, with pockets of use and acceptance among organizations. In this paper we present findings from interviews conducted with representatives from large international disaster response organizations concerning their use of social media data in crisis response. We found that emergency responders already operate with less than reliable, or “good enough,” information in offline practice, and that social media data are useful to responders, but only in specific crisis situations. Also, responders do use social media, but only within their known community and extended network. This shows that trust first begins with people and not data. Lastly, we demonstrate the barriers used by responding organizations have gone beyond discussions of trustworthiness and data quality to that of more operational issues.     

Microstructure and Microchemistry of Polymer-Derived Crystalline SiC Fibers

Stoichiometric and near-stoichiometric silicon carbide fibers, derived from the polymer polycarbosilane, have been characterized by scanning Auger and transmission electron microscope techniques. Excess carbon usually appeared in the fibers, but it could be eliminated by controlling processing conditions. The fibers consisted mainly of submicrometer-sized SiC microcrystals, with a considerably twinned and stacking-faulted β-SiC structure. Free carbon, which appeared in the form of graphite, was the only detectable intergranular phase in the nonstoichioinetric SiC fibers, while the stoichiometric SiC fibers contained no detectable second phase whatsoever.     

Ammoxidation of toluene over Y-Ba-Cu-Co-O perovskites

Ammoxidation of toluene over the perovskites YBa₂Cu₃O_6.1, YBa₂Cu₂CoO_6.7 and YBaCuCoO_4.9 was investigated at 400 °C. At low partial pressures of O₂ benzonitrile was selectively formed, while CO₂ was the main product at high pressures of O₂. Systematic differences in activity were observed for the three phases and are related to the crystal contents of Cu and Co. At low O₂ pressures, Cu-sites are active for nitrile formation, while Co-sites give CO₂. At high O₂ pressures, the activity for CO₂ of Cu-sites increases more than that of Co-sites due to filling of near-surface oxygen vacancies.     

Terpolymers. II. Mechanical properties and transition temperatures of terpolymers of vinyl stearate,vinyl acetate,and vinyl chloride

Vinyl stearate was studied as a major internal plasticizer in terpolymers containing vinyl acetate and vinyl chloride. The terpolymers were prepared by systematically replacing vinyl acetate by close increments of vinyl stearate starting with combinations of vinyl acetate and vinyl chloride, in increments, over all compositions. For comparison of properties, a complete range of copolymers of vinyl stearate and vinyl chloride, as well as mixtures of poly(vinyl chloride) and di-2-ethylhexyl phthalate (DOP) were also made. The external plasticizer was more efficient in reducing the glass temperature than was vinyl stearate. The decline in T_g with weight fraction of plasticizer was linear for the copolymers and terpolymers but concave downward with the liquid diluent. The linear decline was shown to involve mere additivity of the free volume contributed by each side-chain methylene (or methyl) group in both vinyl esters to reducing T_g. The mechanism of the diluent system was more complex. However, the magnitude of the reduction of tensile modulus at a given weight fraction of DOP could be equaled or exceeded by the same amount of vinyl stearate, by increasing the vinyl acetate content of the base copolymer to 40 mole-% or more. Unfortunately, the ultimate strengths and elongations of internally plasticized systems were reduced more than those of the mixtures at comparable compositions. Vinyl stearate was found to markedly retard photolytic degradation compared to both vinyl acetate and the external plasticizer in unstabilized samples having nearly the same thermal treatment. The effect was greater than could be ascribed to dilution by the long alkyl group. The production of a stearoyl radical more stable than the radicals initiating dehydrochlorination is suggested as a possible mechanism.     

The role of the automation development group in analytical research and development at Dupont Merck

Laboratory robotics has been firmly established in many non-QC laboratories as a valuable tool for automating pharmaceutical dosage form analysis. Often a single project or product line is used to justify an initial robot purchase thus introducing robotics to the laboratory for the first time. However, to gain widespread acceptance within the laboratory and to justify further investment in robotics, existing robots must be used to develop analyses for existing manual methods as well as new projects beyond the scope off the original purchase justification. The Automation Development Group in Analytical Research and Development is a team of analysts primarily devoted to developing new methods and adapting existing methods for the robot. This team approach developed the expertise and synergy necessary to significantly expand the contribution of robotics to automation in the authors'' laboratory.     

Isolation kernel: the X factor in efficient and effective large scale online kernel learning

Large scale online kernel learning aims to build an efficient and scalable kernel-based predictive model incrementally from a sequence of potentially infinite data points. Current state-of-the-art large scale online kernel learning focuses on improving efficiency. Two key approaches to gain efficiency through approximation are (1) limiting the number of support vectors, and (2) using an approximate feature map. They often employ a kernel with a feature map with intractable dimensionality. While these approaches can deal with large scale datasets efficiently, this outcome is achieved by compromising predictive accuracy because of the approximation. We offer an alternative approach that puts the kernel used at the heart of the approach. It focuses on creating a sparse and finite-dimensional feature map of a kernel called Isolation Kernel. Using this new approach, to achieve the above aim of large scale online kernel learning becomes extremely simple—simply use Isolation Kernel instead of a kernel having a feature map with intractable dimensionality. We show that, using Isolation Kernel, large scale online kernel learning can be achieved efficiently without sacrificing accuracy.