CuGaS2 and CuGaS2–ZnS porous layers from solution-processed nanocrystals

The manufacturing of semiconducting films using solution-based approaches is considered a low cost alternative to vacuum-based thin film deposition strategies. An additional advantage of solution processing methods is the possibility to control the layer nano/microstructure. Here, we detail the production of mesoporous CuGaS2 (CGS) and ZnS layers from spin-coating and subsequent cross-linking through chalcogen-chalcogen bonds of properly functionalized nanocrystals (NCs). We further produce NC-based porous CGS/ZnS bilayers and NC-based CGS–ZnS composite layers using the same strategy. Photoelectrochemical measurements are used to demonstrate the efficacy of porous layers, and particularly the CGS/ZnS bilayers, for improved current densities and photoresponses relative to denser films deposited from as-produced NCs.Peer ReviewedPostprint (published version

Engineering Transport in Manganites by Tuning Local Non-Stoichiometry in Grain Boundaries

Interface-dominated materials such as nanocrystalline thin films have emerged as an enthralling class of materials able to engineer functional properties of transition metal oxides widely used in energy and information technologies. In particular, it has been proved that strain-induced defects in grain boundaries of manganites deeply impact their functional properties by boosting their oxygen mass transport while abating their electronic and magnetic order. In this work, the origin of these dramatic changes is correlated for the first time with strong modifications of the anionic and cationic composition in the vicinity of strained grain boundary regions. We are also able to alter the grain boundary composition by tuning the overall cationic content in the films, which represents a new and powerful tool, beyond the classical space charge layer effect, for engineering electronic and mass transport properties of metal oxide thin films useful for a collection of relevant solid state devices

Tuning branching in ceria nanocrystals

Branched nanocrystals (NCs) enable high atomic surface exposure within a crystalline network that provides avenues for charge transport. This combination of properties makes branched NCs particularly suitable for a range of applications where both interaction with the media and charge transport are involved. Herein we report on the colloidal synthesis of branched ceria NCs by means of a ligand-mediated overgrowth mechanism. In particular, the differential coverage of oleic acid as an X-type ligand at ceria facets with different atomic density, atomic coordination deficiency, and oxygen vacancy density resulted in a preferential growth in the [111] direction and thus in the formation of ceria octapods. Alcohols, through an esterification alcoholysis reaction, promoted faster growth rates that translated into nanostructures with higher geometrical complexity, increasing the branch aspect ratio and triggering the formation of side branches. On the other hand, the presence of water resulted in a significant reduction of the growth rate, decreasing the reaction yield and eliminating side branching, which we associate to a blocking of the surface reaction sites or a displacement of the alcoholysis reaction. Overall, adjusting the amounts of each chemical, well-defined branched ceria NCs with tuned number, thickness, and length of branches and with overall size ranging from 5 to 45 nm could be produced. We further demonstrate that such branched ceria NCs are able to provide higher surface areas and related oxygen storage capacities (OSC) than quasi-spherical NCs

Sphericity and roundness computation for particles using the extreme vertices model

Shape is a property studied for many kinds of particles. Among shape parameters, sphericity and roundness indices had been largely studied to understand several processes. Some of these indices are based on length measurements of the particle obtained from its oriented bounding box (OBB). In this paper we follow a discrete approach based on Extreme Vertices Model and devise new methods to compute the OBB and the mentioned indices. We apply these methods to synthetic sedimentary rocks and to a real dataset of silicon nanocrystals (Si NC) to analyze the obtained results and compare them with those obtained with a classical voxel model.Peer ReviewedPostprint (author's final draft

Comparative of machine learning classification strategies for electron energy loss spectroscopy: Support vector machines and artificial neural networks

Machine Learning (ML) strategies applied to Scanning and conventional Transmission Electron Microscopy have become a valuable tool for analyzing the large volumes of data generated by various S/TEM techniques. In this work, we focus on Electron Energy Loss Spectroscopy (EELS) and study two ML techniques for classifying spectra in detail: Support Vector Machines (SVM) and Artificial Neural Networks (ANN). Firstly, we systematically analyze the optimal configurations and architectures for ANN classifiers using random search and the tree-structured Parzen estimator methods. Secondly, a new kernel strategy is introduced for the soft-margin SVMs, the cosine kernel, which offers a significant advantage over the previously studied kernels and other ML classification strategies. This kernel allows us to bypass the normalization of EEL spectra, achieving accurate classification. This result is highly relevant for the EELS community since we also assess the impact of common normalization techniques on our spectra using Uniform Manifold Approximation and Projection (UMAP), revealing a strong bias introduced in the spectra once normalized. In order to evaluate and study both classification strategies, we focus on determining the oxidation state of transition metals through their EEL spectra, examining which feature is more suitable for oxidation state classification: the oxygen K peak or the transition metal white lines. Subsequently, we compare the resistance to energy loss shifts for both classifiers and present a strategy to improve their resistance. The results of this study suggest the use of soft-margin SVMs for simpler EELS classification tasks with a limited number of spectra, as they provide performance comparable to ANNs while requiring lower computational resources and reduced training times. Conversely, ANNs are better suited for handling complex classification problems with extensive training data

Direct correlation of crystal structure and optical properties in wurtzite/zinc-blende GaAs nanowire heterostructures

A novel method for the direct correlation at the nanoscale of structural and optical properties of single GaAs nanowires is reported. Nanowires consisting of 100% wurtzite and nanowires presenting zinc-blende/wurtzite polytypism are investigated by photoluminescence spectroscopy and transmission electron microscopy. The photoluminescence of wurtzite GaAs is consistent with a band gap of 1.5 eV. In the polytypic nanowires, it is shown that the regions that are predominantly composed of either zinc-blende or wurtzite phase show photoluminescence emission close to the bulk GaAs band gap, while regions composed of a nonperiodic superlattice of wurtzite and zinc-blende phases exhibit a redshift of the photoluminescence spectra as low as 1.455 eV. The dimensions of the quantum heterostructures are correlated with the light emission, allowing us to determine the band alignment between these two crystalline phases. Our first-principles electronic structure calculations within density functional theory, employing a hybrid-exchange functional, predict band offsets and effective masses in good agreement with experimental results

Surface chemistry and nano-/microstructure engineering on photocatalytic In2S3 nanocrystals

Colloidal nanocrystals (NCs) compete with molecular catalysts in the field of homogenous catalysis, offering easier recyclability and a number of potentially advantageous functionalities, such as tunable band gaps, plasmonic properties, or a magnetic moment. Using high-throughput printing technologies, colloidal NCs can also be supported onto substrates to produce cost-effective electronic, optoelectronic, electrocatalytic, and sensing devices. For both catalytic and technological application, NC surface chemistry and supracrystal organization are key parameters determining final performance. Here, we study the influence of the surface ligands and the NC organization on the catalytic properties of In2S3, both as a colloid and as a supported layer. As a colloid, NCs stabilized by inorganic ligands show the highest photocatalytic activities, which we associate with their large and more accessible surfaces. On the other hand, when NCs are supported on a substrate, their organization becomes an essential parameter determining performance. For instance, NC-based films produced through a gelation process provided five-fold higher photocurrent densities than those obtained from dense films produced by the direct printing of NCs.Peer ReviewedPostprint (author's final draft

Determination of shape and sphericity of silicon quantum dots imaged by EFTEM-tomography

The shape of size-controlled silicon nanocrystals (Si NCs) embedded in SiO2 is investigated by tomographic energy-filtered transmission electron microscopy (EFTEM). The sphericity of the quantum dots is determined by computational analyses. In contrast to other fabrication methods, we demonstrate that the NCs in superlattices are non-agglomerated, individual clusters with slightly oblate spheroidal shape. This allows for low surface-to-volume ratios and thereby low non-radiative defect densities as required by optoelectronic or sensing applications. A near-spherical shape is also a prerequisite for the direct comparison of Si quantum dots (QDs) with theoretical simulationsPeer ReviewedPostprint (author's final draft