44 research outputs found

    Superhydrophobic paper in the development of disposable labware and lab-on-paper devices

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    Traditionally in superhydrophobic surfaces history, the focus has frequently settled on the use of complex processing methodologies using nonbiodegradable and costly materials. In light of recent events on lab-on-paper emergence, there are now some efforts for the production of superhydrophobic paper but still with little development and confined to the fabrication of flat devices. This work gives a new look at the range of possible applications of bioinspired superhydrophobic paper-based substrates, obtained using a straightforward surface modification with poly(hydroxybutyrate). As an end-of-proof of the possibility to create lab-on-chip portable devices, the patterning of superhydrophobic paper with different wettable shapes is shown with low-cost approaches. Furthermore, we suggest the use of superhydrophobic paper as an extremely low-cost material to design essential nonplanar lab apparatus, including reservoirs for liquid storage and manipulation, funnels, tips for pipettes, or accordion-shaped substrates for liquid transport or mixing. Such devices take the advantage of the self-cleaning and extremely water resistance properties of the surfaces as well as the actions that may be done with paper such as cut, glue, write, fold, warp, or burn. The obtained substrates showed lower propensity to adsorb proteins than the original paper, kept superhydrophobic character upon ethylene oxide sterilization and are disposable, suggesting that the developing devices could be especially adequate for use in contact with biological and hazardous materials

    Microfluidic Paper-Based Analytical Devices (μPADs) and Micro Total Analysis Systems (μTAS): Development, Applications and Future Trends

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    Geologic interpretation of gravity profiles in the western marquette district, northern michigan

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    The presence of elongate troughs of Precambrian X (middle Precambrian) rocks in Precambrian W (lower Precambrian) rocks in the western Marquette district of northern Michigan has been known since the late 1800s. However, little data can be brought to bear on estimates of the depth and cross-sectional configuration of these features. For this reason, gravity models and geologic interpretations were made from gravity profiles measured over the Marquette Trough, Republic Trough, and Mitchigan River Trough. Gravity-model studies, combined with geologic studies, indicate that near Humboldt, Michigan, the Marquette Trough is ∼2,438 m deep and asymetrically shaped, the deepest section being near the southern edge; near the west end of Lake Michigamme, the Marquette Trough is about 1,097 m deep at its northern edge; the Republic Trough is about 1,524 m deep at a point ∼2.4 km northwest of its southeastern end; and the Mitchigan River Trough is probably a fault-bounded westward-dipping monocline ∼610 m deep. © 1974 Geological Society of America

    A General Dimension for Exact Learning

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    We introduce a new combinatorial dimension that gives a good approximation of the number of queries needed to learn in the exact learning model, no matter what set of queries is used. This new dimension generalizes previous dimensions providing upper and lower bounds for all sorts of queries, and not for just example-based queries as in previous works. Our new approach gives also simpler proofs for previous results. We present specific applications of our general dimension for the case of unspecified attribute value queries, and show that unspecified attribute value membership and equivalence queries are not more powerful than standard membership and equivalence queries for the problem of learning DNF formulas. Work supported in part by the EC through the Esprit Program EU BRA program under project 20244 (ALCOM-IT), the EC Working Group EP27150 (NeuroColt II) and the spanish government grant PB980937 -C04-04. y Part of this work was done while this author was still in LSI, UPC.
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