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Register a new userEnvironmental high resolution electron microscopy and applications to chemical science
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An environmental cell high resolution electron microscope (EHREM) has been developed for in situ studies of dynamic chemical reactions on the atomic scale. It allows access to metastable intermediate phases of catalysts and to sequences of reversible microstructural and chemical development associated with the activation, deactivation and poisoning of a catalyst. Materials transported through air can be restored or recreated and samples damaged, e.g. by dehydration, by the usual vacuum environment in a conventional electron microscope can be preserved. A Philips C M30 HRTEM and STEM system has been extensively modified in our laboratory to add facilities for in situ gas-solid reaction studies in controlled atmospheres of gas or vapor at pressures of 0 to 50 mbar, instead of the regular TEM high vacuum environment. The integrated new environmental cell capability is combined with the original 0.23 nm TEM resolution, STEM imaging (bright field and annular dark field) and chemical and crystallographic microanalyses. Regular sample holders are used and include hot stages to higher than 1000 C. Examples of applications include direct studies of dynamic reactions with supported metal particle catalysts, the generation of defects and structural changes in practical complex oxide catalyst systems under operating conditions and carbon microstructures.
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Advances in atomic resolution in situ environmental transmission electron microscopy for direct probing of gas-solid reactions, including at very high temperatures are described. In addition, recent developments of dynamic real time in situ studies at the Angstrom level using a hot stage in an aberration corrected environment are presented. In situ data from Pt and Pd nanoparticles on carbon with the corresponding FFT (optical diffractogram) illustrate an achieved resolution of 0.11 nm at 500 C and higher in a double aberration corrected TEM and STEM instrument employing a wider gap objective pole piece. The new results open up opportunities for dynamic studies of materials in an aberration corrected environment.
Understanding the oxidation and reduction mechanisms of catalytically active transition metal nanoparticles is important to improve their application in a variety of chemical processes. In nanocatalysis the nanoparticles can undergo oxidation or reduction in situ, and thus the redox species are not what are observed before and after reactions. We have used the novel environmental scanning transmission electron microscope (ESTEM) with 0.1 nm resolution in systematic studies of complex dynamic oxidation and reduction mechanisms of copper nanoparticles. The oxidation of copper has previously been reported to be dependent on its crystallography and its interaction with the substrate. By following the dynamic oxidation process in situ, in real time, with high-angle annular dark-field imaging in the ESTEM, we use conditions ideal to track the oxidation front as it progresses across a copper nanoparticle by following the changes in the atomic number (z) contrast with time. The oxidation occurs via the nucleation of the oxide phase (Cu2O) from one area of the nanoparticle which then progresses unidirectionally across the particle, with the Cu-to-Cu2O interface having a relationship of Cu{111} parallel to Cu2O{111}. The oxidation kinetics are related to the temperature and oxygen pressure. When the process is reversed in hydrogen, the reduction process is observed to be similar to the oxidation, with the same crystallographic relationship between the two phases. The dynamic observations provide unique insights into redox mechanisms which are important to understanding and controlling the oxidation and reduction of copper-based nanoparticles.
Ultrafast Electron Microscopy (UEM) has been demonstrated to be an effective table-top technique for imaging the temporally-evolving dynamics of matter with subparticle spatial resolution on the time scale of atomic motion. However, imaging the faster motion of electron dynamics in real time has remained beyond reach. Here, we demonstrate more than an order of magnitude (16 times) enhancement in the typical temporal resolution of UEM by generating isolated 30 fs electron pulses, accelerated at 200 keV, via the optical-gating approach, with sufficient intensity for efficiently probing the electronic dynamics of matter. Moreover, we investigate the feasibility of attosecond optical gating to generate isolated subfemtosecond electron pulses, attaining the desired temporal resolution in electron microscopy for establishing the Attomicroscopy to allow the imaging of electron motion in the act.
Single atoms can be considered as basic objects for electron microscopy to test the microscope performance and basic concepts for modeling of image contrast. In this work high-resolution transmission electron microscopy was applied to image single platinum atoms in an aberration-corrected transmission electron microscope. The atoms are deposited on a self-assembled monolayer substrate which induces only negligible contrast. Single-atom contrast simulations were performed on the basis of Weickenmeier-Kohl and Doyle-Turner scattering factors. Experimental and simulated intensities are in full agreement on an absolute scale.
Machine learning techniques applied to chemical reactions has a long history. The present contribution discusses applications ranging from small molecule reaction dynamics to platforms for reaction planning. ML-based techniques can be of particular interest for problems which involve both, computation and experiments. For one, Bayesian inference is a powerful approach to include knowledge from experiment in improving computational models. ML-based methods can also be used to handle problems that are formally intractable using conventional approaches, such as exhaustive characterization of state-to-state information in reactive collisions. Finally, the explicit simulation of reactive networks as they occur in combustion has become possible using machine-learned neural network potentials. This review provides an overview of the questions that can and have been addressed using machine learning techniques and an outlook discusses challenges in this diverse and stimulating field. It is concluded that ML applied to chemistry problems as practiced and conceived today has the potential to transform the way with which the field approaches problems involving chemical reactions, both, in research and academic teaching.
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