Silicon nanowire arrays as learning chemical vapour classifiers.

Nanowire field-effect transistors are a promising class of devices for various sensing applications. Apart from detecting individual chemical or biological analytes, it is especially interesting to use multiple selective sensors to look at their collective response in order to perform classification into predetermined categories. We show that non-functionalised silicon nanowire arrays can be used to robustly classify different chemical vapours using simple statistical machine learning methods. We were able to distinguish between acetone, ethanol and water with 100% accuracy while methanol, ethanol and 2-propanol were classified with 96% accuracy in ambient conditions.

Lysozyme binding onto cat-anionic vesicles.

Mixing aqueous sodium dodecylsulfate with cetyltrimethylammonium bromide solutions in mole ratios close to (1.7/1.0) allows the formation of cat-anionic vesicles with an excess of negative charges on the outer surface. The vesicular dispersions are mixed with lysozyme, and interact electrostatically with the positive charges on the protein, forming lipo-plexes. Dielectric relaxation, zeta-potential, and light scattering indicate the occurrence of interactions between vesicles and the protein. According to CD, the vesicle-adsorbed protein retains its native conformation. Binding and surface saturation, inferred by dielectric relaxation and zeta-potential, fulfil a charge neutralisation stoichiometry. Adsorbed lysozyme promotes the vesicle clustering and is concomitant with the lipo-plexes flocculation. Above the charge neutralisation threshold, lysozyme in excess remains dispersed in molecular form. Attempts were made to determine in what conditions protein release from the vesicles occurs. Accordingly, the full neutralisation of sodium dodecylsulfate in excess by cetyltrimethylammonium bromide ensures the lipo-plexes break-up, the precipitation of the mixed surfactants and the protein release in native form.