Synthesis, structure, and opto-electronic properties of organic-based nanoscale heterojunctions

Enormous research effort has been put into optimizing organic-based opto-electronic systems for efficient generation of free charge carriers. This optimization is mainly due to typically high dissociation energy (0.1-1 eV) and short diffusion length (10 nm) of excitons in organic materials. Inherently, interplay of microscopic structural, chemical, and opto-electronic properties plays crucial role. We show that employing and combining advanced scanning probe techniques can provide us significant insight into the correlation of these properties. By adjusting parameters of contact- and tapping-mode atomic force microscopy (AFM), we perform morphologic and mechanical characterizations (nanoshaving) of organic layers, measure their electrical conductivity by current-sensing AFM, and deduce work functions and surface photovoltage (SPV) effects by Kelvin force microscopy using high spatial resolution. These data are further correlated with local material composition detected using micro-Raman spectroscopy and with other electronic transport data. We demonstrate benefits of this multi-dimensional characterizations on (i) bulk heterojunction of fully organic composite films, indicating differences in blend quality and component segregation leading to local shunts of photovoltaic cell, and (ii) thin-film heterojunction of polypyrrole (PPy) electropolymerized on hydrogen-terminated diamond, indicating covalent bonding and transfer of charge carriers from PPy to diamond

Guided assembly of nanoparticles on electrostatically charged nanocrystalline diamond thin films

We apply atomic force microscope for local electrostatic charging of oxygen-terminated nanocrystalline diamond (NCD) thin films deposited on silicon, to induce electrostatically driven self-assembly of colloidal alumina nanoparticles into micro-patterns. Considering possible capacitive, sp2 phase and spatial uniformity factors to charging, we employ films with sub-100 nm thickness and about 60% relative sp2 phase content, probe the spatial material uniformity by Raman and electron microscopy, and repeat experiments at various positions. We demonstrate that electrostatic potential contrast on the NCD films varies between 0.1 and 1.2 V and that the contrast of more than ±1 V (as detected by Kelvin force microscopy) is able to induce self-assembly of the nanoparticles via coulombic and polarization forces. This opens prospects for applications of diamond and its unique set of properties in self-assembly of nano-devices and nano-systems

Estimates and relationships between aboveground and belowground resource exchange surface areas in a Sitka spruce managed forest

Our knowledge of the nature of belowground competition for moisture and nutrients is limited. In this study, we used an earth impedance method to determine the root absorbing area of Sitka spruce (Picea sitchensis (Bong.) Carr.) trees, making measurements in stands of differing density (2-, 4- and 6-m inter-tree spacing). We compared absorbing root area index (RAIabsorbing; based on the impedance measure) with fine root area index (RAIfine; based on estimates of total surface area of fine roots) and related these results to investment in conductive roots. Root absorbing area was a near-linear function of tree stem diameter at 1.3 m height. At the stand level, RAIabsorbing, which is analogous to and scaled with transpiring leaf area index (maximum stomatal pore area per unit ground area; LAItranspiring), increased proportionally with basal area across the three stands. In contrast, RAIfine was inversely propotional to basal area. The ratio of RAIabsorbing to LAItranspiring ranged from 7.7 to 17.1, giving an estimate of the relative aboveground versus belowground resource exchange areas. RAIabsorbing provides a way of characterizing ecosystem functioning as a physiologically meaningful index of belowground absorbing area. © The Author 2010. Published by Oxford University Press. All rights reserved.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

High-resolution characterization of deformation induced martensite in large areas of fatigued austenitic stainless steel using deep learning

Abstract This paper aims to demonstrate a novel technique enabling the accurate visualization and fast mapping of deformation-induced α′-martensite produced during cyclic straining of a metastable austenitic stainless steel, refined by reversion annealing to different grain sizes. The technique is based on energy and angular separation of the signal electrons in a scanning electron microscope (SEM). Collection of the inelastic backscattered electrons emitted under high take-off angles from a sample surface results in the acquisition of micrographs with high sensitivity to structural defects, such as dislocations, grain boundaries, and other imperfections. The areas with a high density of lattice imperfections reduce the penetration depth of the primary electrons, and simultaneously affect the signal electrons leaving the specimen. This results in an increase in the inelastic backscattered electrons yielded from the vicinity of α′-martensite, and a bright halo surrounds this phase. The α′-martensite phase can thus be separated from the austenitic matrix in SEM micrographs. In this work, we propose a deep learning method for a precise α′-martensite mapping within a large area. Various deep learning-based methods have been tested, and the best result measured by both Dice loss and IoU scores has been achieved using the U-Net architecture extended by the ResNet encoder