Processing of electroactive nanostructured films incorporating carbon nanotubes and phthalocyanines for sensing

The use of carbon nanotubes (CNTs) combined with other materials in nanostructured films has demonstrated their versatility in tailoring specific properties. In this study, we produced layer-by-layer (LbL) films of polyamidoamine-PAMAM-incorporating multiwalled carbon nanotubes (PAMAM-NT) alternated with nickel tetrasulfonated metallophthalocyanine (NiTsPc), in which the incorporation of CNTs enhanced the NiTsPc redox process and its electrocatalytic properties for detecting dopamine. Film growth was monitored by UV-vis spectroscopy, which pointed to an exponential growth of the multilayers, whose roughness increased with the number of bilayers according to atomic force microscopy (AFM) analysis. Strong interactions between -NH3+ terminal groups from PAMAM and -SO3- from NiTsPc were observed via infrared spectroscopy, while the micro-Raman spectra confirmed the adsorption of carbon nanotubes (CNTs) onto the LbL film containing NiTsPc. Cyclic voltammograms presented well-defined electroactivity with a redox pair at 0.86 and 0.87 V, reversibility, a charge-transfer controlled process, and high stability up to 100 cycles. The films were employed successfully in dopamine (DA) detection, with limits of detection and quantification of 10(-7) and 10(-6) mol L-1, respectively. Furthermore, films containing immobilized CNTs could distinguish between DA and its natural interferent, ascorbic acid (AA).

Structured minimal-memory inexact quasi-Newton method and secant preconditioners for augmented Lagrangian optimization

Augmented Lagrangian methods for large-scale optimization usually require efficient algorithms for minimization with box constraints. On the other hand, active-set box-constraint methods employ unconstrained optimization algorithms for minimization inside the faces of the box. Several approaches may be employed for computing internal search directions in the large-scale case. In this paper a minimal-memory quasi-Newton approach with secant preconditioners is proposed, taking into account the structure of Augmented Lagrangians that come from the popular Powell-Hestenes-Rockafellar scheme. A combined algorithm, that uses the quasi-Newton formula or a truncated-Newton procedure, depending on the presence of active constraints in the penalty-Lagrangian function, is also suggested. Numerical experiments using the Cute collection are presented.