Machine Learning Method Reveals Hidden Strong Metal-Support Interaction in Microscopy Datasets

In Situ and Emerging Transmission Electron Microscopy for Catalysis Research

Catalysts are the primary facilitator in many dynamic processes. Therefore, a thorough understanding of these processes has vast implications for a myriad of energy systems. The scanning/transmission electron microscope (S/TEM) is a powerful tool not only for atomic-scale characterization but also in situ catalytic experimentation. Techniques such as liquid and gas phase electron microscopy allow the observation of catalysts in an environment conducive to catalytic reactions. Correlated algorithms can greatly improve microscopy data processing and expand multidimensional data handling. Furthermore, new techniques including 4D-STEM, atomic electron tomography, cryogenic electron microscopy, and monochromated electron energy loss spectroscopy (EELS) push the boundaries of our comprehension of catalyst behavior. In this review, we discuss the existing and emergent techniques for observing catalysts using S/TEM. Challenges and opportunities highlighted aim to inspire and accelerate the use of electron microscopy to further investigate the complex interplay of catalytic systems.

Nonclassical Strong Metal-Support Interactions for Enhanced Catalysis

Strong metal-support interaction (SMSI), which encompasses reversible encapsulation and de-encapsulation and modulation of surface adsorption properties, imposes great impacts on the performance of heterogeneous catalysts. Recent development of SMSI has surpassed the prototypical encapsulated Pt-TiO₂ catalyst, affording a series of conceptually novel and practically advantageous catalytic systems. Here we provide our perspective on recent progress in nonclassical SMSIs for enhanced catalysis. Unravelling the structural complexity of SMSI necessitates the combination of multiple characterization techniques at different scales. Synthesis strategies leveraging chemical, photonic, and mechanochemical driving forces further expand the definition and application scope of SMSI. Exquisite structure engineering permits elucidation of the interface, entropy, and size effect on the geometric and electronic characteristics. Materials innovation places the atomically thin two-dimensional materials at the forefront of interfacial active site control. A broader space is awaiting exploration, where exploitation of metal-support interactions brings compelling catalytic activity, selectivity, and stability.

Engineering Heterogeneous Catalysis with Strong Metal-Support Interactions: Characterization, Theory and Manipulation

Strong metal-support interactions (SMSI) represent a classic yet fast-growing area in catalysis research. The SMSI phenomenon results in the encapsulation and stabilization of metal nanoparticles (NPs) with the support material that significantly impacts the catalytic performance through regulation of the interfacial interactions. Engineering SMSI provides a promising approach to steer catalytic performance in various chemical processes, which serves as an effective tool to tackle energy and environmental challenges. Our Minireview covers characterization, theory, catalytic activity, dependence on the catalytic structure and inducing environment of SMSI phenomena. By providing an overview and outlook on the cutting-edge techniques in this multidisciplinary research field, we not only want to provide insights into the further exploitation of SMSI in catalysis, but we also hope to inspire rational designs and characterization in the broad field of material science and physical chemistry.

Adsorbate-Induced Strong Metal-Support Interactions: Implications for Catalyst Design

Since the discovery of strong metal-support interactions (SMSIs) over supported metal catalysts in the 1970s, researchers have studied ways to harness this type of catalyst reconstruction to achieve enhanced stability of metal particles against sintering and to create catalytic sites with novel electronic and bonding properties. The motivation to elucidate performance-structure relationships in catalytic transformations has led researchers to take a closer look into catalytic surfaces under reaction conditions rather than a postreaction analysis. These investigations of operating catalysts have made it clear that SMSIs are more common than initially thought. Recent reports show how various adsorbed species, rather than traditional H₂/O₂ treatment, can promote SMSI in various catalytic systems, a phenomenon named adsorbate-induced SMSI (A-SMSI). Researching the occurrence of A-SMSI has allowed fundamental understanding of catalyst stability, catalytic rates, and product selectivity. The present Perspective discusses the state-of-the-art regarding A-SMSI, the current challenges, and the opportunities ahead in heterogeneous catalysis.

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providing powerful resources towards automation, exploration, and development. In this review, we evaluate the state-of-the-art of machine learning applied to electron microscopy (and obliquely, to materials and nano-sciences). We start from the traditional imaging techniques to reach the newest higher-dimensionality ones, also covering the recent advances in spectroscopy and tomography. Additionally, the present review provides a practical guide for microscopists, and in general for material scientists, but not necessarily advanced machine learning practitioners, to straightforwardly apply the offered set of tools to their own research. To conclude, we explore the state-of-the-art of other disciplines with a broader experience in applying artificial intelligence methods to their research (e.g., high-energy physics, astronomy, Earth sciences, and even robotics, videogames, or marketing and finances), in order to narrow down the incoming future of electron microscopy, its challenges and outlook.

RapidEELS: machine learning for denoising and classification in rapid acquisition electron energy loss spectroscopy

Recent advances in detectors for imaging and spectroscopy have afforded in situ, rapid acquisition of hyperspectral data. While electron energy loss spectroscopy (EELS) data acquisition speeds with electron counting are regularly reaching 400 frames per second with near-zero read noise, signal to noise ratio (SNR) remains a challenge owing to fundamental counting statistics. In order to advance understanding of transient materials phenomena during rapid acquisition EELS, trustworthy analysis of noisy spectra must be demonstrated. In this study, we applied machine learning techniques to denoise high frame rate spectra, benchmarking with slower frame rate "ground truths". The results provide a foundation for reliable use of low SNR data acquired in rapid, in-situ spectroscopy experiments. Such a tool-set is a first step toward both automation in microscopy as well as use of these methods to interrogate otherwise poorly understood transformations.