Solving the structure of "single-atom" catalysts using machine learning - assisted XANES analysis

Synchrotron Radiation Based X-ray Absorption Spectroscopy: Fundamentals and Applications in Photocatalysis

Photocatalysis is one of the most promising green technologies to utilize solar energy for clean energy achievement and environmental governance. There is a knotty problem to rational designing high-performance photocatalyst, which largely depends on an in-depth insight into their structure-activity relationships and complex photocatalytic reaction mechanisms. Synchrotron radiation based X-ray absorption spectroscopy (XAS) is an important characterization method for photocatlayst to offer the element-specific key geometric and electronic structural information at the atomic level, on this basis, time-resolved XAS technique has a huge impact on mechanistic understanding of photochemical reaction owing to their powerful ability to probe, in real-time, the electronic and geometric structures evolution within photocatalysis reactions. This review will focus on the fundamentals of XAS and their applications in photocatalysis. The detailed applications obtained from XAS is described through the following aspects: 1) identifying local structure of photocatalyst; 2) uncovering in situ structure and chemical state evolution during photocatalysis; 3) revealing the photoexcited process. We will provide an in depth understanding on how the XAS method can guide the rational design of highly efficient photocatalyst. Finally, a systematic summary of XAS and related significance is made and the research perspectives are suggested.

Machine-Learning-Assisted Nanozyme Design: Lessons from Materials and Engineered Enzymes

Nanozymes are nanomaterials that exhibit enzyme-like biomimicry. In combination with intrinsic characteristics of nanomaterials, nanozymes have broad applicability in materials science, chemical engineering, bioengineering, biochemistry, and disease theranostics. Recently, the heterogeneity of published results has highlighted the complexity and diversity of nanozymes in terms of consistency of catalytic capacity. Machine learning (ML) shows promising potential for discovering new materials, yet it remains challenging for the design of new nanozymes based on ML approaches. Alternatively, ML is employed to promote optimization of intelligent design and application of catalytic materials and engineered enzymes. Incorporation of the successful ML algorithms used in the intelligent design of catalytic materials and engineered enzymes can concomitantly facilitate the guided development of next-generation nanozymes with desirable properties. Here, recent progress in ML, its utilization in the design of catalytic materials and enzymes, and how emergent ML applications serve as promising strategies to circumvent challenges associated with time-expensive and laborious testing in nanozyme research and development are summarized. The potential applications of successful examples of ML-aided catalytic materials and engineered enzymes in nanozyme design are also highlighted, with special focus on the unified aims in enhancing design and recapitulation of substrate selectivity and catalytic activity.

Structural Analysis of Single-Atom Catalysts by X-ray Absorption Spectroscopy

ConspectusMetal nanoparticles (NPs) are one of the most frequently used heterogeneous catalysts. However, only the surface atoms in the NPs can participate in catalytic reactions. To maximize the atomic efficiency, the active sites can be reduced to single atoms. Generally, catalysts that have isolated metal atoms on the surface of a support are called single-atom catalysts (SACs). Many techniques have been developed and applied to probe the structures of SACs. Nevertheless, the structural characterization of SACs is still challenging as it requires the analysis of their structure and properties with atomic and sometimes even subatomic resolution. X-ray absorption spectroscopy (XAS) is a powerful tool in investigating the local coordination environment of SACs since it is element-specific and can provide accurate structural information at the subatomic level (∼0.01 Å).In this Account, we present our perspectives on the structural analysis of SACs from some unique features in the X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS). We first highlight the importance of the XANES peak features in the sensitive analysis of SAC structures. Such analysis is illustrated to be even more useful in the joint applications of experimental and theoretical XAS. The inspection of the metal-metal (M-M) peak in Fourier transformed EXAFS (FT-EXAFS) spectra is a widely used method to identify the single-atom structure, but this method is not always reliable. Thus, we point out the importance of fitting EXAFS and the thorough interpretation of structural parameters such as coordination numbers (CNs, the number of neighboring atoms next to a chosen atom), bond distances, and the Debye-Waller factor (σ2). The small FT-EXAFS peak for the M-M shell is often ignored in the structural analysis of SACs. Here, it is demonstrated that a careful analysis of these small peaks could help more reliably analyze the SAC structure, and it would be particularly useful in the analysis of a single-atom alloy (SAA). Next, the usefulness of bond distance and σ2 analysis is highlighted, and such analysis is shown to be particularly helpful for the analysis of SAAs, which is rarely discussed in the literature. Given the advantage that XAS data can be collected under various conditions, we show that in situ XAS can provide important information about the catalytic mechanism of the SAC catalyst. In particular, we emphasize the significance of using an advanced in situ technique to extract detailed structural information that is difficult to obtain from regular XAS experiments. Finally, we highlight the importance of jointly using XAS with other complementary methods in a more complete understanding of the structure and properties of SACs. It is anticipated that with further development of XAS techniques and improved data analysis, XAS will become even more powerful in providing insights into the structure-property relationships of SACs, which can advance their practical applications.

Reversible Structural Evolution of Metal-Nitrogen-Doped Carbon Catalysts During CO2 Electroreduction: An Operando X-ray Absorption Spectroscopy Study

Electrochemical CO2 reduction (CO2 RR) is a rising technology, aiming to reduce the energy sector dependence on fossil fuels and to produce carbon-neutral raw materials. Metal-nitrogen-doped carbons (M-N-C) are emerging, cost-effective catalysts for this reaction; however, their long-term stability is a major issue. To overcome this, understanding their structural evolution is crucial, requiring systematic in-depth operando studies. Here a series of M-N-C catalysts (M = Fe, Sn, Cu, Co, Ni, Zn) is investigated using operando X-ray absorption spectroscopy. It is found that the Fe-N-C and Sn-N-C are prone to oxide clusters formation even before CO2 RR. In contrast, the respective metal cations are singly dispersed in the as-prepared Cu-N-C, Co-N-C, Ni-N-C, and (Zn)-N-C. During CO2 RR, metallic clusters/nanoparticles reversibly formed in all catalysts, except for the Ni-N-C. This phenomenon, previously observed only in Cu-N-C, thus is ubiquitous in M-N-C catalysts. The competition between M-O and M-N interactions is an important factor determining the mobility of metal species in M-N-C. Specifically, the strong interaction between the Ni centers and the N-functional groups of the carbon support results in higher stability of the Ni single-sites, leading to the excellent performance of Ni-N-C in the CO2 to CO conversion, in comparison to other transition metals.

Single-Atom Heterogeneous Catalysts: Human- and AI-Driven Platform for Augmented Designs, Analytics and Reality-Enabled Manufacturing

Heterogeneous catalysts with targeted functionality can be designed with atomic precision, but it is challenging to retain the structure and performance upon the scaled-up manufacturing. Particularly challenging is to ensure the "atomic economy", where every catalytic site is most gainfully utilized. Given the emerging synergistic integration of human- and artificial intelligence (AI)-driven augmented designs (AD), augmented analytics (AA), and augmented reality manufacturing (AM) platforms, this minireview focuses on single-atom heterogeneous catalysts (SAHCs) and examines the current status, challenges, and future perspectives of translating atomic-level structural precision and data-driven discovery to next-generation industrial manufacturing. We critically examine the atomistic insights into structure-driven SAHCs functionality and discuss the opportunities and challenges on the way towards the synergistic human-AI collaborative data-driven platform capable of monitoring, analyzing, manufacturing, and retaining the atomic-scale structure and functions. Enhanced by the atomic-level AD, AA, and AM, evolving from the current high-throughput capabilities and digital materials manufacturing acceleration, this synergistic human-AI platform is promising to enable atom-efficient and atomically precise heterogeneous catalyst production.

Single-Atom Cobalt Catalysts Coupled with Peroxidase Biocatalysis for C-H Bond Oxidation

This paper reports a robust strategy to catalyze in situ C-H oxidation by combining cobalt (Co) single-atom catalysts (SACs) and horseradish peroxidase (HRP). Co SACs were synthesized using the complex of Co phthalocyanine with 3-propanol pyridine at the two axial positions as the Co source to tune the coordination environment of Co by the stepwise removal of axial pyridine moieties under thermal annealing. These structural features of Co sites, as confirmed by infrared and X-ray absorption spectroscopy, were strongly correlated to their reactivity. All Co catalysts synthesized below 300 °C were inactive due to the full coordination of Co sites in octahedral geometry. Increasing the calcination temperature led to an improvement in catalytic activity for reducing O2, although molecular Co species with square planar coordination obtained below 600 °C were less selective to reduce O2 to H2O2 through the two-electron pathway. Co SACs obtained at 800 °C showed superior activity in producing H2O2 with a selectivity of 82-85% in a broad potential range. In situ production of H2O2 was further coupled with HRP to drive the selective C-H bond oxidation in 2-naphthol. Our strategy provides new insights into the design of highly effective, stable SACs for selective C-H bond activation when coupled with natural enzymes.

Tracking the Evolution of Single-Atom Catalysts for the CO2 Electrocatalytic Reduction Using Operando X-ray Absorption Spectroscopy and Machine Learning

Transition metal-nitrogen-doped carbons (TMNCs) are a promising class of catalysts for the CO2 electrochemical reduction reaction. In particular, high CO2-to-CO conversion activities and selectivities were demonstrated for Ni-based TMNCs. Nonetheless, open questions remain about the nature, stability, and evolution of the Ni active sites during the reaction. In this work, we address this issue by combining operando X-ray absorption spectroscopy with advanced data analysis. In particular, we show that the combination of unsupervised and supervised machine learning approaches is able to decipher the X-ray absorption near edge structure (XANES) of the TMNCs, disentangling the contributions of different metal sites coexisting in the working TMNC catalyst. Moreover, quantitative structural information about the local environment of active species, including their interaction with adsorbates, has been obtained, shedding light on the complex dynamic mechanism of the CO2 electroreduction.

Design of Single-Atom Catalysts and Tracking Their Fate Using Operando and Advanced X-ray Spectroscopic Tools

The potential of operando X-ray techniques for following the structure, fate, and active site of single-atom catalysts (SACs) is highlighted with emphasis on a synergetic approach of both topics. X-ray absorption spectroscopy (XAS) and related X-ray techniques have become fascinating tools to characterize solids and they can be applied to almost all the transition metals deriving information about the symmetry, oxidation state, local coordination, and many more structural and electronic properties. SACs, a newly coined concept, recently gained much attention in the field of heterogeneous catalysis. In this way, one can achieve a minimum use of the metal, theoretically highest efficiency, and the design of only one active site-so-called single site catalysts. While single sites are not easy to characterize especially under operating conditions, XAS as local probe together with complementary methods (infrared spectroscopy, electron microscopy) is ideal in this research area to prove the structure of these sites and the dynamic changes during reaction. In this review, starting from their fundamentals, various techniques related to conventional XAS and X-ray photon in/out techniques applied to single sites are discussed with detailed mechanistic and in situ/operando studies. We systematically summarize the design strategies of SACs and outline their exploration with XAS supported by density functional theory (DFT) calculations and recent machine learning tools.