2D & 3D in situ study of the calcination of Pd nanocatalysts supported on delta-Alumina in an Environmental Transmission Electron Microscope

Unraveling the Dynamic Properties of New-Age Energy Materials Chemistry Using Advanced In Situ Transmission Electron Microscopy

The field of energy storage and conversion materials has witnessed transformative advancements owing to the integration of advanced in situ characterization techniques. Among them, numerous real-time characterization techniques, especially in situ transmission electron microscopy (TEM)/scanning TEM (STEM) have tremendously increased the atomic-level understanding of the minute transition states in energy materials during electrochemical processes. Advanced forms of in situ/operando TEM and STEM microscopic techniques also provide incredible insights into material phenomena at the finest scale and aid to monitor phase transformations and degradation mechanisms in lithium-ion batteries. Notably, the solid-electrolyte interface (SEI) is one the most significant factors that associated with the performance of rechargeable batteries. The SEI critically controls the electrochemical reactions occur at the electrode-electrolyte interface. Intricate chemical reactions in energy materials interfaces can be effectively monitored using temperature-sensitive in situ STEM techniques, deciphering the reaction mechanisms prevailing in the degradation pathways of energy materials with nano- to micrometer-scale spatial resolution. Further, the advent of cryogenic (Cryo)-TEM has enhanced these studies by preserving the native state of sensitive materials. Cryo-TEM also allows the observation of metastable phases and reaction intermediates that are otherwise challenging to capture. Along with these sophisticated techniques, Focused ion beam (FIB) induction has also been instrumental in preparing site-specific cross-sectional samples, facilitating the high-resolution analysis of interfaces and layers within energy devices. The holistic integration of these advanced characterization techniques provides a comprehensive understanding of the dynamic changes in energy materials. This review highlights the recent progress in employing state-of-the-art characterization techniques such as in situ TEM, STEM, Cryo-TEM, and FIB for detailed investigation into the structural and chemical dynamics of energy storage and conversion materials.

Deep learning revealed statistics of the MgO particles dissolution rate in a CaO-Al2O3-SiO2-MgO slag

Accelerated material development for refractory ceramics triggers possibilities in context to enhanced energy efficiency for industrial processes. Here, the gathering of comprehensive material data is essential. High temperature-confocal laser scanning microscopy (HT-CLSM) displays a highly suitable in-situ method to study the underlying dissolution kinetics in the slag over time. A major drawback concerns the efficient and accurate processing of the collected image data. Here, we introduce an attention encoder-decoder convolutional neural network enabling the fully automated evaluation of the particle dissolution rate with a precision of 99.1%. The presented approach provides accurate and efficient analysis capabilities with high statistical gain and is highly resilient to image quality changes. The prediction model allows an automated diameter evaluation of the MgO particles' dissolution in the silicate slag for different temperature settings and various HT-CLSM data sets. Moreover, it is not limited to HT-CLSM image data and can be applied to various domains.

In Situ Single Particle Reconstruction Reveals 3D Evolution of PtNi Nanocatalysts During Heating

Tailoring nanoparticles' composition and morphology is of particular interest for improving their performance for catalysis. A challenge of this approach is that the nanoparticles' optimized initial structure often changes during use. Visualizing the three dimensional (3D) structural transformation in situ is therefore critical, but often prohibitively difficult experimentally. Although electron tomography provides opportunities for 3D imaging, restrictions in the tilt range of in situ holders together with electron dose considerations limit the possibilities for in situ electron tomography studies. Here, an in situ 3D imaging methodology is presented using single particle reconstruction (SPR) that allows 3D reconstruction of nanoparticles with controlled electron dose and without tilting the microscope stage. This in situ SPR methodology is employed to investigate the restructuring and elemental redistribution within a population of PtNi nanoparticles at elevated temperatures. The atomic structure of PtNi is further examined and a heat-induced transition is found from a disordered to an ordered phase. Changes in structure and elemental distribution are linked to a loss of catalytic activity in the oxygen reduction reaction. The in situ SPR methodology employed here can be extended to a wide range of in situ studies employing not only heating, but gaseous, aqueous, or electrochemical environments to reveal in-operando nanoparticle evolution in 3D.

A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials

In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net's fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.

Deep learning detection of nanoparticles and multiple object tracking of their dynamic evolution during in situ ETEM studies

In situ transmission electron microscopy (TEM) studies of dynamic events produce large quantities of data especially under the form of images. In the important case of heterogeneous catalysis, environmental TEM (ETEM) under gas and temperature allows to follow a large population of supported nanoparticles (NPs) evolving under reactive conditions. Interpreting properly large image sequences gives precious information on the catalytic properties of the active phase by identifying causes for its deactivation. To perform a quantitative, objective and robust treatment, we propose an automatic procedure to track nanoparticles observed in Scanning ETEM (STEM in ETEM). Our approach involves deep learning and computer vision developments in multiple object tracking. At first, a registration step corrects the image displacements and misalignment inherent to the in situ acquisition. Then, a deep learning approach detects the nanoparticles on all frames of video sequences. Finally, an iterative tracking algorithm reconstructs their trajectories. This treatment allows to deduce quantitative and statistical features about their evolution or motion, such as a Brownian behavior and merging or crossing events. We treat the case of in situ calcination of palladium (oxide) / delta-alumina, where the present approach allows a discussion of operating processes such as Ostwald ripening or NP aggregative coalescence.

Developments and advances in in situ transmission electron microscopy for catalysis research

Recent developments and advances in in situ TEM have raised the possibility to study every step during the catalysts' lifecycle. This review discusses the current state, opportunities and challenges of in situ TEM in the realm of catalysis.

Transformation of the active component during oxidative and reductive activation of the palladium hydrogenation catalyst

In this work, the transformation of supported salts on alumina with the formation of palladium metal particles of a hydrogenation catalyst is considered.