Abstract: Transmission electron microscopy (TEM) is an ideal tool to investigate nanomaterials. The information from TEM experiments allows us to link the structure and composition of nanomaterials to their intrinsic physical properties. However, despite the significant evolution of the TEM field during the last two decades, major progress is still possible through the development of optimal TEM techniques and supports. The results presented in this thesis focus on the optimization of sample supports and their application. Among the different options, graphene has previously been reported as useful sample support for electron microscopy due to its unparalleled properties, for example, it is the thinnest known support and provides a protective effect to the sample under investigation. Unfortunately, commercial graphene grids show poor quality, in terms of intactness and cleanness, inhibiting their wide application within the field. Therefore, this thesis focuses on the application of optimized graphene TEM grids, obtained by transferring high quality graphene using an advanced procedure. This improvement on the transfer has enabled the visualization of materials with low contrast and high sensitivity towards the electron beam, such as surface ligands capping gold nanoparticles or metal halide perovskites. Furthermore, the implemented protocol is not only of interest for conventional TEM grids but also a major benefit for in situ TEM studies, where the sample is investigated in real time under certain stimuli. Hence, the same graphene transfer technology can be also applied to advanced in situ MEMS holders dedicated for both heating and gas experiments, where the thickness and insulating nature of the silicon nitride (Si3N4) support may hamper some applications. By engineering periodic arrays of holes in their Si3N4 membrane by focused ion beam, onto which the graphene is transferred, it has been possible to get proof-of-concept 3D in situ investigations of heat-induced morphological and compositional transformations of complex nanosystems. As an example, it has enabled the investigation of the possible phase-transition of metal halide perovskites upon heating using 2D and 3D structural characterization. Moreover, it has allowed the study of in situ three-dimensional nanoparticle dynamics during gas phase catalysis as well as the first steps that would lead towards the design and creation of the first Graphene Gas Cell. Consequently, implementation of the advanced graphene transfer technology described in this thesis is envisaged to impact a broad range of future experiments.

Abstract: Nanomaterialen kunnen uiterst interessante eigenschappen vertonen voor een verscheidenheid aan veelbelovende toepassingen, gaande van zonnecrème tot batterijen voor elektrische auto’s. Een nanometer is een miljard keer kleiner dan een meter. Op deze schaal kunnen de materiaaleigenschappen volledig verschillen van bulkmaterialen op grotere schaal. Bovendien hangen de eigenschappen van nanomaterialen sterk af van hun exacte grootte en vorm. Kleine verschillen in de posities van de atomen, in de grootte-orde van een picometer (nog eens duizend maal kleiner dan een nanometer), kunnen de fysische eigenschappen al drastisch beïnvloeden. Daarom is een betrouwbare kwantificering van de atomaire structuur van kritisch belang om de evolutie naar materiaalontwerp mogelijk te maken en inzicht te verwerven in de relatie tussen de fysische eigenschappen en de structuur van nanomaterialen. Daarnaast kan de atomaire structuur van nanomaterialen ook veranderen in de loop van de tijd ten gevolge van verschillende fysische processen. Het onderzoek dat in deze thesis gepresenteerd wordt, maakt het mogelijk om de dynamische structuurveranderingen van nanomaterialen betrouwbaar te kwantificeren op atomaire schaal door gebruik te maken van raster transmissie elektronenmicroscopie (STEM). Ik heb dit gerealiseerd door methodes te ontwikkelen waarmee ik het aantal atomen “achter elkaar” kan tellen in elke atoomkolom van een nanomateriaal, en dit op basis van beelden opgenomen met een elektronenmicroscoop. Een belangrijk verschil met telmethodes voor de analyse van een enkel beeld is het schatten van de kans dat een atoomkolom atomen zal verliezen of bijkrijgen van het ene naar het andere beeld in de tijdreeks. Deze kwantitatieve methode kan het ontrafelen van de tijdsafhankelijke structuur-eigenschappen relatie van een nanomateriaal mogelijk maken, wat uiteindelijk kan leiden tot efficiënter design en productie van nanomaterialen voor innovatieve toepassingen.

Abstract: Nanomaterials are materials that have at least one dimension in the nanometer length scale, which corresponds to a billionth of a meter. When three dimensions are confined to the nanometer scale, these materials are referred to as nanoparticles. These materials are of great interest since they exhibit unique physical and chemical properties that cannot be observed for bulk systems. Due to their unique and often superior properties, nanomaterials have become central in the field of electronics, catalysis, and medicine. Moreover, they are expected to be one of the most promising systems to tackle many challenges that our society is facing, such as reducing the emission of greenhouse gases and finding effective treatments for cancer. The unique properties of nanomaterials are linked to their size, shape, structure, and composition. If one is able to measure the positions of the atoms, their chemical nature, and the bonding between them, it becomes possible to predict the physicochemical properties of nanomaterials. In this manner, the development of novel nanostructures can be triggered. However, the morphology and structure of nanomaterials are highly sensitive to the conditions for relevant applications, such as elevated temperatures or intense light illumination. Furthermore, any small change in the local structure at higher temperatures or pressures may significantly modify their performance. Hence, three-dimensional (3D) characterization of nanomaterials under application-relevant conditions is important in designing them with desired functional properties for specific applications. Among different structural characterization approaches, transmission electron microscopy (TEM) is one of the most efficient and versatile tools to investigate the structure and composition of nanomaterials since it can provide atomically resolved images, which are sensitive to the local 3D structure of the investigated sample. However, TEM only provides two-dimensional (2D) images of the 3D nanoparticle, which may lead to an incomplete understanding of their structure-property relationship. The most known and powerful technique for the 3D characterization of nanomaterials is electron tomography, where the images of a nanostructured material taken from different directions are mathematically combined to retrieve its 3D structure. Although these experiments are already state-of-the-art, 3D characterization by TEM is typically performed under ultra-high vacuum conditions and at room temperature. Such conditions are unfortunately not sufficient to understand transformations during synthesis or applications of nanomaterials. This limitation can be overcome by in situ TEM where external stimuli, such as heat, gas, and liquids, can be controllably introduced inside the TEM using specialized holders. However, there are some technical limitations to successful perform 3D in situ electron tomography experiments. For example, the long acquisition time required to collect a tilt series limits this technique when one wants to observe 3D dynamic changes with atomic resolution. A solution for this problem is the estimation of the 3D structure of nanomaterials from 2D projection images acquired along a single viewing direction. For this purpose, annular dark field scanning TEM (ADF STEM) imaging mode provides a valuable tool for quantitative structural investigation of nanomaterials from single 2D images due to its thickness and mass sensitivity. For quantitative analysis, an ADF STEM image is considered as a 2D array of pixels where relative variation of pixel intensity values is proportional to the total number of atoms and the atomic number of the elements in the sample. By applying advanced statistical approaches to these images, structural information, such as the number or types of atoms, can be retrieved with high accuracy and precision. The outcome can then be used to build a 3D starting model for energy minimization by atomistic simulations, for example, molecular dynamics simulations or the Monte Carlo method. However, this methodology needs to be further evaluated for in situ experiments. This thesis is devoted to presenting robust approaches to accurately define the 3D atomic structure of nanoparticles under application-relevant conditions and understand the mechanism behind the atomic-scale dynamics in nanoparticles in response to environmental stimuli.