Roll-to-roll synthesis of multielement heterostructured catalysts
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Effects of nitrogen vacancy sites of oxynitride support on the catalytic activity for ammonia decomposition
Nitrogen-containing compounds such as imides and amides have been reported as efficient materials that promote ammonia decomposition over nonnoble metal catalysts. However, these compounds decompose in an air atmosphere and become inactive, which leads to difficulty in handling. Here, we focused on perovskite oxynitrides as air-stable and efficient supports for ammonia decomposition catalysts. Ni-loaded oxynitrides exhibited 2.5–18 times greater catalytic activity than did the corresponding oxide-supported Ni catalysts, even without noticeable differences in the Ni particle size and surface area of the supports. The catalytic performance of the Ni-loaded oxynitrides is well correlated with the nitrogen desorption temperature during N2 temperature-programmed desorption, which suggests that the lattice nitrogen in the oxynitride support rather than the Ni surface is the active site for ammonia decomposition. Furthermore, NH3 temperature-programmed surface reactions and density functional theory (DFT) calculations revealed that NH3 molecules are preferentially adsorbed on the nitrogen vacancy sites on the support surface rather than on the Ni surface. Thus, the ammonia decomposition reaction is facilitated by a vacancy-mediated reaction mechanism.
Modeling the impact of structure and coverage on the reactivity of realistic heterogeneous catalysts
Adsorbates often cover the surfaces of catalysts densely as they carry out reactions, dynamically altering their structure and reactivity. Understanding adsorbate-induced phenomena and harnessing them in our broader quest for improved catalysts is a substantial challenge that is only beginning to be addressed. Here we chart a path toward a deeper understanding of such phenomena by focusing on emerging in silico modeling methodologies, which will increasingly incorporate machine learning techniques. We first examine how adsorption on catalyst surfaces can lead to local and even global structural changes spanning entire nanoparticles, and how this affects their reactivity. We then evaluate current efforts and the remaining challenges in developing robust and predictive simulations for modeling such behavior. Last, we provide our perspectives in four critical areas—integration of artificial intelligence, building robust catalysis informatics infrastructure, synergism with experimental characterization, and adaptive modeling frameworks—that we believe can help surmount the remaining challenges in rationally designing catalysts in light of these complex phenomena.
Photo-assisted technologies for environmental remediation
Industrial processes can lead to air and water pollution, particularly from organic contaminants such as toluene and antibiotics, posing threats to human health. Photo-assisted chemical oxidation technologies leverage light energy to mineralize these contaminants. In this Review, we discuss the mechanisms and efficiencies of photo-assisted advanced oxidation processes for wastewater treatment and photothermal technologies for air purification. The integration of solar energy enhances degradation efficiency and reduces energy consumption, enabling more efficient remediation methods. We evaluate the technological aspects of photo-assisted technologies, such as photo-Fenton, photo-persulfate activation, photo-ozonation and photoelectrochemical oxidation, emphasizing their potential for practical applications. Finally, we discuss the challenges in scaling up photo-assisted technologies for specific environmental remediation needs. Photo-assisted technologies have demonstrated effectiveness in environmental remediation, although large-scale applications remain constrained by high costs. Future potential applications of photo-assisted technologies will require that technology selection be tailored to specific pollution scenarios and engineering processes optimized to minimize costs.
Anion vacancies activate N2 to ammonia on Ba–Si orthosilicate oxynitride-hydride
Anion vacancies on metal oxide surfaces have been studied as either active sites or promoting sites in various chemical reactions involving oxidation/reduction processes. However, oxide materials rarely work effectively as catalysts in the absence of transition metal sites. Here we report a Ba–Si orthosilicate oxynitride–hydride as a transition-metal-free catalyst for efficient ammonia synthesis via an anion-vacancy–mediated mechanism. The facile desorption of H− and N3− anions plus the flexibility of the crystal structure can accommodate a high density of electrons at vacancy sites, where N2 can be captured and directly activated to ammonia through hydrogenation processes. The ammonia synthesis rates reach 40.1 mmol g−1 h−1 at 300 °C by loading ruthenium nanoparticles. Although not found to dissociate N2, Ru instead facilitates the formation of anion vacancies at the Ru–support interface. This demonstrates a new route for anion-vacancy–mediated heterogeneous catalysis.
First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes
MXenes are a versatile family of 2D inorganic materials with applications in energy storage, shielding, sensing, and catalysis. This review highlights computational studies using density functional theory and machine-learning approaches to explore their structure (stacking, functionalization, doping), properties (electronic, mechanical, magnetic), and application potential. Key advances and challenges are critically examined, offering insights into applying computational research to transition these materials from the lab to practical use.
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