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

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

A General Dimension for Exact Learning

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.