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

Unveiling the Complex Magnetization Reversal Process in 3D Nickel Nanowire Networks

Understanding the interactions among magnetic nanostructures is one of the key factors to predict and control the advanced functionalities of Three-Dimensional (3D) integrated magnetic nanostructures. In this work, we focus on different interconnected Ni nanowires forming an intricate, but controlled, and ordered magnetic system: Ni 3D Nanowire Networks. These self-ordered systems present striking anisotropic magnetic responses, depending on the interconnections' position between nanowires. To understand their collective magnetic behavior, we studied the magnetization reversal processes within different Ni 3D Nanowire Networks compared to the 1D nanowire array counterparts. We characterized the systems at different angles using first magnetization curves, hysteresis loops, and First Order Reversal Curves techniques, which provided information about the key features that enable macroscopic tuning of the magnetic properties of the 3D nanostructures. In addition, micromagnetic simulations endorsed the experiments, providing an accurate modeling of their magnetic behavior. The results revealed a plethora of magnetic interactions, neither evident nor intuitive, which are the main role players controlling the collective response of the system. The results pave the way for the design and realization of 3D novel metamaterials and devices based on the nucleation and propagation of ferromagnetic domain walls both in 3D self-ordered systems and future nano-lithographied devices

Metal oxide aerogels with controlled crystallinity and faceting from the epoxide-driven cross-linking of colloidal nanocrystals

We present a novel method to produce crystalline oxide aerogels which is based on the cross linking of preformed colloidal nanocrystals (NCs) triggered by propylene oxide (PO). Ceria and titania were used to illustrate this new approach. Ceria and titania colloidal NCs with tuned geometry and crystal facets were produced in solution from the decomposition of a suitable salt in the presence of oleylamine (OAm). The native surface ligands were replaced by amino acids, rendering the NCs colloidally stable in polar solvents. The NC colloidal solution was then gelled by adding PO, which gradually stripped the ligands from the NC surface, triggering a slow NC aggregation. NC-based metal oxide aerogels displayed both high surface areas and excellent crystallinity associated with the crystalline nature of the constituent building blocks, even without any annealing step. Such NC-based metal oxide aerogels showed higher thermal stability compared with aerogels directly produced from ionic precursors using conventional sol-gel chemistry strategies

Insights into interface and bulk defects in a high efficiency kesterite-based device

This work provides a detailed analysis of a high efficiency Cu2ZnSnSe4 device using a combination of advanced electron microscopy and spectroscopy techniques. In particular, a full picture of the different defects present at the interfaces of the device and in the bulk of the absorber is achieved through the combination of high resolution electron microscopy techniques with Raman, X-ray fluorescence and Auger spectroscopy measurements at the macro, micro and nano scales. The simultaneous investigation of the bulk and the interfaces allows assessing the impact of the defects found in each part of the device on its performance. Despite a good crystalline quality and homogeneous composition in the bulk, this work reports, for the first time, direct evidence of twinning defects in the bulk, of micro and nano-voids at the back interface and of grain-to-grain non-uniformities and dislocation defects at the front interface. These, together with other issues observed such as strong absorber thickness variations and a bilayer structure with small grains at the bottom, are shown to be the main factors limiting the performance of CZTSe devices. These results open the way to the identification of new solutions to further developing the kesterite technology and pushing it towards higher performances. Moreover, this study provides an example of how the advanced characterization of emergent multilayer-based devices can be employed to elucidate their main limitations.Peer ReviewedPostprint (author's final draft

Effect of Si3N4-mediated inversion layer on the electroluminescence properties of silicon nanocrystal superlattices

The achievement of an efficient all-Si electrically-pumped light emitter is a major milestone in present optoelectronics still to be fulfilled. Silicon nanocrystals (Si NCs) are an attractive material which, by means of the quantum confinement effect, allow attaining engineered bandgap visible emission from Si by controlling the NC size. In this work, SiO2-embedded Si NCs are employed as an active layer within a light-emitting device structure. It is demonstrated that the use of an additional thin Si3N4 interlayer within the metal-insulator-semiconductor device design induces an enhanced minority carrier injection from the substrate, which in turn increases the efficiency of sequential carrier injection under pulsed electrical excitation. This results in a substantial increase in the electroluminescence efficiency of the device. Here, the effect of this Si3N4 interlayer on the structural, optical, electrical, and electro-optical properties of a Si NC-based light emitter is reported, and the physics underlying these results is discussed

Advanced computational tools for EELS data reduction and clustering, quantitative analysis and 3D reconstructions

[eng] This thesis has been primarily dedicated to the exploration and implementation of new computational analysis tools and techniques for the characterisation of nanomaterials and devices via transmission electron microscopy (TEM). In particular, the focus is set on the fields of electron energy loss spectroscopy (EELS) and electron tomography (ET). In the context of this PhD, EELS is used for the quantitative and qualitative analysis of elemental distributions at the nanoscale for several different materials, mainly transition metal and rare earth oxides. It is also used for the investigation of the distribution of elemental oxidation states at the nanoscale through the analysis of the so-called energy-loss near-edge structures (ELNES) of core-loss edges. The ever-growing size and complexity of the acquired spectral datasets, as well as a paradigmatic change towards the acquisition of larger but noisier spectral datasets, is the driving force behind the continuous push by the TEM community towards the implementation of new analysis techniques from the field of machine learning into the standard EELS characterization pipelines. The linear matrix factorization algorithms of principal component analysis (PCA) and non-negative matrix factorization (NMF) are among the first algorithms implemented for EELS analysis. Recently, several clustering analysis algorithms, such as K-means and hierarchical agglomerative clustering, have been used for the spectral segmentation of EELS spectrum images (SI) as well. In this thesis the combined use of a non-linear dimensionality reduction algorithm called uniform manifold approximation and projection (UMAP) for dimension reduction, and a clustering algorithm called hierarchical density-based spatial clustering of applications with noise (HDBSCAN), was explored as a viable solution towards a fully-data driven methodology for the spectral segmentation of EELS SI. Furthermore, a systematic revision of these new DRM and clustering methods (UMAP and HDBSCAN), the already stablished ones (PCA, NMF, K-means, etc.), and some of the possible combinations between them, was conducted. This revision includes several qualitative and quantitative performance analysis experiments, which are carried out for a series of specially designed synthetic datasets. The acquired experience with these techniques is later applied to characterize a wide variety of materials. Also, the combination of clustering and non-linear least-squares (NLLS) fitting has also been proven as a promising solution to improve the stability of the latter. This methodology was addressed as part of work done during this PhD to provide a ready-to-go software solution for all these machine learning methodologies applied to EELS and ELNES analysis, leading to the development of a complete and independent software solution called WhatEELS. This modular tool provides the resources required to quantitatively resolve complex problems involving ELNES analysis. A clear example of its powerful capabilities is showcased through the characterization of a set of Pr-Gd doped CeO2 mesoporous materials. In this series of experiments, the local changes in the Ce oxidation state and the localized dopant segregation were successfully resolved. The field of ET provides the materials scientist with one of the most versatile toolsets for the characterization of materials at the nanoscale, as it allows the reconstruction of 3D volumes from a limited set of 2D projections acquired. In this PhD thesis, the work is mainly focused on the implementation of advanced algorithms for the ET reconstruction of nanomaterials in Python programming language. The attention is centred on the TVAL3 algorithm, a solver for the total variation minimization (TVM) problem with its theoretical foundations in the mathematical field of compressed sensing. This methodology based on the TVAL3 algorithm is used for the experimental characterisation of the 3D morphology and chemical composition of a wide variety of different nanomaterials, such as the 3D resolution of the dopant segregation in the CeO2 mesoporous material.[spa] Esta tesis se centra, principalmente, en la exploración e implementación de nuevas herramientas y técnicas de análisis computacional para la caracterización de nanomateriales mediante espectroscopía de pérdida de energía de los electrones (EELS) y tomografía electrónica (TE). En el contexto de este doctorado, el EELS se utiliza para el análisis cuantitativo y cualitativo de distribuciones elementales a la nanoescala, principalmente para óxidos de metales de transición y de tierras raras. También se utiliza para la investigación de la distribución de estados de oxidación mediante el análisis de la estructura fina (ELNES). Para hacer dichos análisis, en esta tesis se demuestra que el uso combinado de un algoritmo de reducción de dimensionalidad (DRM) no lineal, UMAP, y un algoritmo jerárquico de agrupamiento (‘clustering’), HDBSCAN, es una solución viable hacia una metodología totalmente basada en los datos para la segmentación de imágenes de espectros de EELS. Además, se realizó una revisión sistemática de estos nuevos métodos de DRM y ‘clustering’ (UMAP y HDBSCAN), de los ya establecidos (PCA, NMF, K-means, etc.), y de algunas de sus posibles combinaciones. El uso combinado de ‘clustering’ y del ajuste de mínimos cuadrados no lineales (NLLS) se muestra también como una solución prometedora para el análisis de ELNES. Dicha metodología fue implementada como parte de una nueva herramienta de software llamada WhatEELS. Esta herramienta modular y de acceso gratuito proporciona los recursos necesarios para resolver cuantitativamente problemas complejos que involucran el análisis ELNES. Su potencial fue demostrado mediante el análisis de la segregación de dopantes y del cambio localizado del estado de oxidación catiónico para una serie de muestras de Ceria mesoporosa. Finalmente, parte de esta tesis también está dedicada a la implementación de algoritmos para el muestreo disperso en TE. En concreto, el algoritmo TVAL3 fue traducido al lenguaje de programación Python y utilizado para varios experimentos, incluida la resolución de la segregación de dopantes en 3D para los materiales mesoporosos de ceria ya mencionados

WhatEELS. A new software solution for ELNES analysis

WhatEELS is a free software tool based in Python, specifically designed to facilitate the combined use of clustering and non-linear least squares (NLLS) fitting for the analysis of Energy Loss Near Edge Structures (ELNES) in Electron Energy Loss Spectroscopy (EELS). It includes a set of tools for white-lines analysis and elemental quantification

Data treatment proceedures for EELS analysis combining dimensionality reduction and clustering

This notebook exemplifies the combined use of dimensionality reduction techinques and clustering analysis strategies showcased in - Strategies for EELS data analysis. Dimensionality reduction and clustering analysis. Introducing UMAP and HDBSCAN

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 Reviewe

Atomic-Scale Determination of Cation Inversion in Spinel-Based Oxide Nanoparticles

The atomic structure of nanoparticles can be easily determined by transmission electron microscopy. However, obtaining atomic-resolution chemical information about the individual atomic columns is a rather challenging endeavor. Here, crystalline monodispersed spinel FeO/MnO core-shell nanoparticles have been thoroughly characterized in a high-resolution scanning transmission electron microscope. Electron energy-loss spectroscopy (EELS) measurements performed with atomic resolution allow the direct mapping of the Mn/Mn ions in the shell and the Fe/Fe in the core structure. This enables a precise understanding of the core-shell interface and of the cation distribution in the crystalline lattice of the nanoparticles. Considering how the different oxidation states of transition metals are reflected in EELS, two methods of performing a local evaluation of the cation inversion in spinel lattices are introduced. Both methods allow the determination of the inversion parameter in the iron oxide core and manganese oxide shell, as well as detecting spatial variations in this parameter, with atomic resolution. X-ray absorption measurements on the whole sample confirm the presence of cation inversion. These results present a significant advance toward a better correlation of the structural and functional properties of nanostructured spinel oxides