Understanding the Origin of Enhanced Piezoelectric Response in PVDF Matrices with Embedded ZnO Nanoparticles, from Polarizable Molecular Dynamics Simulations.

The pervasive use of portable electronic devices, powered from rechargeable batteries, represents a significant portion of the electricity consumption in the world. A sustainable and alternative energy source for these devices would require unconventional power sources, such as harvesting kinetic/potential energy from mechanical vibrations, ultrasound waves, and biomechanical motion, to name a few. Piezoelectric materials transform mechanical deformation into electric fields or, conversely, external electric fields into mechanical motion. Therefore, accurate prediction of elastic and piezoelectric properties of materials, from the atomic structure and composition, is essential for studying and optimizing new piezogenerators. Here, we demonstrate the application of harmonic-covalent and reactive force fields (FF), Dreiding and ReaxFF, respectively, coupled to the polarizable charge equilibration (PQEq) model for predicting the elastic moduli and piezoelectric response of crystalline zinc oxide (ZnO) and polyvinylidene difluoride (PVDF). Furthermore, we parametrized the ReaxFF atomic interactions for Zn-F in order to characterize the interfacial effects in hybrid PVDF matrices with embedded ZnO nanoparticles (NPs). We capture the nonlinear piezoelectric behavior of the PVDF-ZnO system at different ZnO concentrations and the enhanced response that was recently observed experimentally, between 5 and 7 wt % ZnO concentrations. From our simulation results, we demonstrate that the origin of this enhancement is due to an increase in the total atomic stress distribution at the interface between the two materials. This result provides valuable insight into the design of new and improved piezoelectric nanogenerators and demonstrates the practical value of these first-principles based modeling methods in materials science.

A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops.

Traditional methods to measure spatio-temporal variations in biomass rely on a labor-intensive destructive sampling of the crop. In this paper, we present a high-throughput phenotyping approach for the estimation of Above-Ground Biomass Dynamics (AGBD) using an unmanned aerial system. Multispectral imagery was acquired and processed by using the proposed segmentation method called GFKuts, that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo based K-means, and a guided image filtering. Accurate plot segmentation results enabled the extraction of several canopy features associated with biomass yield. Machine learning algorithms were trained to estimate the AGBD according to the growth stages of the crop and the physiological response of two rice genotypes under lowland and upland production systems. Results report AGBD estimation correlations with an average of r = 0.95 and R2 = 0.91 according to the experimental data. We compared our segmentation method against a traditional technique based on clustering. A comprehensive improvement of 13% in the biomass correlation was obtained thanks to the segmentation method proposed herein.