Ronaldo Giro

Designing conducting polymers using genetic algorithms

We have developed a new methodology to design conducting polymers with pre-specified properties. The methodology is based on the use of genetic algorithms (GAs) coupled to Negative Factor Counting technique. We present the results for a case study of polyanilines, one of the most important families of conducting polymers. The methodology proved to be able of generating automatic solutions for the problem of determining the optimum relative concentration for binary and ternary disordered polyaniline alloys exhibiting metallic properties. The methodology is completely general and can be used to design new classes of materials.

Using Artificial Intelligence Methods to Design New Conducting Polymers

In the last years the possibility of creating new conducting polymers exploring the concept of copolymerization (different structural monomeric units) has attracted much attention from experimental and theoretical points of view. Due to the rich carbon reactivity an almost infinite number of new structures is possible and the procedure of trial and error has been the rule. In this work we have used a methodology able of generating new structures with pre-specified properties. It combines the use of negative factor counting (NFC) technique with artificial intelligence methods (genetic algorithms - GAs). We present the results for a case study for poly(phenylenesulfide phenyleneamine) (PPSA), a copolymer formed by combination of homopolymers: polyaniline (PANI) and polyphenylenesulfide (PPS). The methodology was successfully applied to the problem of obtaining binary up to quinternary disordered polymeric alloys with a pre-specific gap value or exhibiting metallic properties. It is completely general and can be in principle adapted to the design of new classes of materials with pre-specified properties.

Emergence of prime numbers as the result of evolutionary strategy

We investigate by means of a simple theoretical model the emergence of prime numbers as life cycles, as those seen for some species of cicadas. The cicadas, more precisely the Magicicadas, spend most of their lives below the ground and then emerge and die in a short period of time. The Magicicadas display an uncommon behavior: their emergence is synchronized and these periods are usually prime numbers. In the current work, we develop a spatially extended model at which preys and predators coexist and can change their evolutionary dynamics through the occurrence of mutations. We verified that prime numbers as life cycles emerge as a result of the evolution of the population. Our results seem to be a first step in order to prove that the development of such strategy is selectively advantageous, especially for those organisms that are highly vulnerable to attacks of predators.

Adsorption energy as a metric for wettability at the nanoscale

Wettability is the affinity of a liquid for a solid surface. For energetic reasons, macroscopic drops of liquid form nearly spherical caps. The degree of wettability is then captured by the contact angle where the liquid-vapor interface meets the solid-liquid interface. As droplet volumes shrink to the scale of attoliters, however, surface interactions become significant, and droplets assume distorted shapes. In this regime, the contact angle becomes ambiguous, and a scalable metric for quantifying wettability is needed, especially given the emergence of technologies exploiting liquid-solid interactions at the nanoscale. Here we combine nanoscale experiments with molecular-level simulation to study the breakdown of spherical droplet shapes at small length scales. We demonstrate how measured droplet topographies increasingly reveal non-spherical features as volumes shrink. Ultimately, the nanoscale droplets flatten out to form layer-like molecular assemblies at the solid surface. For the lack of an identifiable contact angle at small scales, we introduce a droplet’s adsorption energy density as a new metric for a liquid’s affinity for a surface. We discover that extrapolating the macroscopic idealization of a drop to the nanoscale, though it does not geometrically resemble a realistic droplet, can nonetheless recover its adsorption energy if line tension is included.

A Platform for Analysis of Nanoscale Liquids with an Array of Sensor Devices Based on Two-Dimensional Material

Analysis of nanoscale liquids, including wetting and flow phenomena, is a scientific challenge with far reaching implications for industrial technologies. We report the conception, development, and application of an integrated platform for the experimental characterization of liquids at the nanometer scale. The platform combines the functionalities of a two-dimensional electronic array of sensor devices with in situ application of highly sensitive optical microspectroscopy and atomic force microscopy. We demonstrate the performance capabilities of the platform with an embodiment based on an array of optically transparent graphene sensors. The application of electronic and optical sensing in the platform allows for differentiating between liquids electronically, for determining a liquids molecular fingerprint, and for monitoring surface wetting dynamics in real time. In order to explore the platforms sensitivity limits, we record topographies and optical spectra of individual, spatially isolated sessile oil emulsion droplets having volumes of less than ten attoliters. The results demonstrate that integrated measurement functionalities based on two-dimensional materials have the potential to push lab-on-chip based analysis from the microscale to the nanoscale.

Nanoscale Flow Chip Platform for Laboratory Evaluation of Enhanced Oil Recovery Materials

We present a lab-on-chip platform for the experimental evaluation of Enhanced Oil Recovery (EOR) methods from the nanoscale to the scale of reservoir rock pore networks. We have employed semiconductor process technology to build lab-on-chip flow devices with features at the nanometer scale that allow us to perform controlled flow experiments for calibrating multiscale flow models. The platform built on silicon semiconductor technology is highly customizable and allows for design adaptation of different physical model representations. The approach enables us to experimentally investigate and validate liquid flow in porous media below the micrometer scale and to deploy calibrated, multi-scale flow simulations in a digital representation of a given rock pore network. The chip implementations of the nanoscale, porous rock network enable systematic flow studies covering various parameters (e.g. effective porosity, viscosity, surface properties) under controlled conditions of physical parameters (e.g. temperature, pressure). High resolution optical microscopy measurement techniques enable us to track individual nanometer size fluorescent tags which allow us to directly determine fluid flow speeds even in sub-micrometer constrictions. We introduce the architecture of the flow chip, discuss how the flow experiments are performed and how the experimental results are used to calibrate the flow simulations. Ultimately, the calibrated flow simulations will be used for predicting the efficiency of a specific EOR agent for improving oil displacement in a pore scale network of reservoir rock.