Electron Energy Loss Spectroscopy for Single Atom Catalysis

Ambient-condition acetylene hydrogenation to ethylene over WS2-confined atomic Pd sites

Ambient-condition acetylene hydrogenation to ethylene (AC-AHE) is a promising process for ethylene production with minimal additional energy input, yet remains a great challenge due to the difficulty in the coactivation of acetylene and H2 at room temperature. Herein, we report a highly efficient AC-AHE process over robust sulfur-confined atomic Pd species on tungsten sulfide surface. The catalyst exhibits over 99% acetylene conversion with a high ethylene selectivity of 70% at 25 oC, and a record space-time yield of ethylene of 1123 molC2H4 molPd-1 h-1 under ambient conditions, which is nearly four times that of the typical Pd1Ag3/Al2O3 catalyst, and exhibiting superior stability of over 500 h. We demonstrate that the confinement of Pd-S coordination induces positively-charged atomic Pdδ+, which not only facilitates C2H2 hydrogenation but also promotes C2H4 desorption, thereby enabling a high conversion of C2H2 to C2H4 at room temperature while suppressing over-hydrogenation to C2H6.

Low-voltage single-atom electron microscopy with carbon-based nanomaterials

The properties of materials are strongly correlated with their atomic scale structures. Achieving a comprehensive understanding of the atomic-scale structure-property relationship requires advancements of imaging and spectroscopy techniques. Aberration-corrected scanning transmission electron microscopy (STEM) has seen rapid development over the past decades and is now routinely employed for atomic-scale characterization. However, quantitative STEM imaging and spectroscopy analysis at the single-atom level is challenging due to the extremely weak signals generated from individual atom, thus imposing stringent requirements for analysis sensitivity. This review discusses the development and application of low-voltage STEM techniques with single-atom sensitivity, primarily based on recent research presented on an invited talk at the 5th 2D23 SALVE Symposium, including annular dark-field (ADF) imaging, functional imaging and electron energy-loss spectroscopy (EELS) analysis. Carbon-based nanomaterials were chosen as model systems for demonstrating the capabilities of single-atom STEM imaging and EELS analysis, due to their structural stability under low accelerating voltages and their rich physical and chemical properties. Moreover, this review summarizes recent advancements and applications of low-voltage single-atom STEM imaging and spectroscopy in the study of functional materials and discusses prospects for future developments.

Operando Electron Microscopy of Catalysts: The Missing Cornerstone in Heterogeneous Catalysis Research?

Heterogeneous catalysis in thermal gas-phase and electrochemical liquid-phase chemical conversion plays an important role in our modern energy landscape. However, many of the structural features that drive efficient chemical energy conversion are still unknown. These features are, in general, highly distinct on the local scale and lack translational symmetry, and thus, they are difficult to capture without the required spatial and temporal resolution. Correlating these structures to their function will, conversely, allow us to disentangle irrelevant and relevant features, explore the entanglement of different local structures, and provide us with the necessary understanding to tailor novel catalyst systems with improved productivity. This critical review provides a summary of the still immature field of operando electron microscopy for thermal gas-phase and electrochemical liquid-phase reactions. It focuses on the complexity of investigating catalytic reactions and catalysts, progress in the field, and analysis. The forthcoming advances are discussed in view of correlative techniques, artificial intelligence in analysis, and novel reactor designs.

Chemical Electron Microscopy (CEM) for Heterogeneous Catalysis at Nano: Recent Progress and Challenges

Chemical electron microscopy (CEM), a toolbox that comprises imaging and spectroscopy techniques, provides dynamic morphological, structural, chemical, and electronic information about an object in chemical environment under conditions of observable performance. CEM has experienced a revolutionary improvement in the past years and is becoming an effective characterization method for revealing the mechanism of chemical reactions, such as catalysis. Here, we mainly address the concept of CEM for heterogeneous catalysis in the gas phase and what CEM could uniquely contribute to catalysis, and illustrate what we can know better with CEM and the challenges and future development of CEM.

Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks

The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal-noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-loss edges from raw spectra. An EELS spectral database is synthesized by using our forward model to assist in the training and validation of the neural network. To make the synthesized spectra resemble the real spectra, we collected a large library of experimentally acquired EELS core edges. In synthesize the training library, the edges are modeled by fitting the multi-Gaussian model to the real edges from experiments, and the noise and instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM network is tested against both the simulated spectra and real spectra collected from experiments. The high accuracy of the network, 94.9%, proves that, without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM network achieves the automation of core-loss edge recognition for EELS spectra with high accuracy.

The Progress and Outlook of Metal Single-Atom-Site Catalysis

Single-atom-site catalysts (SASCs) featuring maximized atom utilization and isolated active sites have progressed tremendously in recent years as a highly prosperous branch of catalysis research. Varieties of SASCs have been developed that show excellent performance in many catalytic applications. The major goal of SASC research is to establish feasible synthetic strategies for the preparation of high-performance catalysts, to achieve an in-depth understanding of the active-site structures and catalytic mechanisms, and to develop practical catalysts with industrial value. This Perspective describes the up-to-date development of SASCs and related catalysts, such as dual-atom-site catalysts (DASCs) and nano-single-atom-site catalysts (NSASCs), analyzes the current challenges encountered by these catalysts for industrial applications, and proposes their possible future development path.