Synthesis and characterization of a silver nanoparticle-containing polymer composite with antimicrobial abilities for application in prosthetic and orthotic devices.

Background: The presence of skin problems in patients using external lower limb prosthesis is recurrent. This has generated the need to develop interfaces for prosthesis with the ability to control microbial growth. Silver nanoparticles (AgNPs) have been implemented in the development of biomaterials because of their high antimicrobial activity.

Optimization of silver nanoparticle synthesis by chemical reduction and evaluation of its antimicrobial and toxic activity.

Background: Chemical reduction has become an accessible and useful alternative to obtain silver nanoparticles (AgNPs). However, its toxicity capacity depends on multiple variables that generate differences in the ability to inhibit the growth of microorganisms. Thus, optimazing parameters for the synthesis of AgNPs can increase its antimicrobial capacity by improving its physical-chemical properties.

Quantifying the degree of locking in weakly forced stochastic systems.

Controlling an stochastic nonlinear system with a small amplitude signal is a fundamental problem with many practical applications. Quantifying locking is challenging, and current methods, such as spectral or correlation analysis, do not provide a precise measure of the degree of locking. Here we study locking in an experimental system, consisting of a semiconductor laser with optical feedback operated in the regime where it randomly emits abrupt spikes.

Differentiating resting brain states using ordinal symbolic analysis.

Symbolic methods of analysis are valuable tools for investigating complex time-dependent signals. In particular, the ordinal method defines sequences of symbols according to the ordering in which values appear in a time series. This method has been shown to yield useful information, even when applied to signals with large noise contamination.

State space reconstruction of spatially extended systems and of time delayed systems from the time series of a scalar variable.

The space-time representation of high-dimensional dynamical systems that have a well defined characteristic time scale has proven to be very useful to deepen the understanding of such systems and to uncover hidden features in their output signals. By using the space-time representation many analogies between one-dimensional spatially extended systems (1D SESs) and time delayed systems (TDSs) have been found, including similar pattern formation and propagation of localized structures. An open question is whether such analogies are limited to the space-time representation, or it is also possible to recover similar evolutions in a low-dimensional pseudo-space.