Catalysis Research Research Articles

4-Halomethyl-Substituted Imidazolium Salts: A Versatile Platform for the Synthesis of Functionalized NHC Precursors.

N,N'-Diarylimidazolium salts containing haloalkyl functional groups that are reactive with various nucleophiles are considered to be promising reagents for the preparation of functionalized N-heterocyclic carbene (NHC) ligands, which are in demand in catalysis, materials science, and biomedical research. Recently, 4-chloromethyl-functionalized N,N'-diarylimidazolium salts became readily available via the condensation of N,N'-diaryl-2-methyl-1,4-diaza-1,3-butadienes with ethyl orthoformate and Me3SiCl, but these compounds were found to have insufficient reactivity in reactions with many nucleophiles. These chloromethyl salts were studied as precursors in the synthesis of bromo- and iodomethyl-functionalized imidazolium salts by halide anion exchange. The 4-ICH2-functionalized products were found to be unstable, whereas a series of novel 4-bromomethyl functionalized N,N'-diarylimidazolium salts were obtained in good yields. These bromomethyl-functionalized imidazolium salts were found to be significantly more reactive towards various N, O and S nucleophiles than the chloromethyl counterparts and enabled the preparation of previously inaccessible heteroatom-functionalized imidazolium salts, some of which were successfully used as NHC proligands in the preparation of Pd/NHC and Au/NHC complexes.

Molecular Architecture @ MOFs: Designing a Multifunctional Catalyst for the Cascade Reaction of Olefins via Epoxides to Cyclic Carbonates

Employing enzymatic reaction cascade principles to synthesize artificial materials with multiple autonomously operating active sites is one of the holy grails in modern catalysis research. In this regard, metal‐organic frameworks (MOFs) are promising host platforms. Yet, applying MOFs as enzyme‐mimicking catalysts is synthetically challenging. Herein, we present a design strategy for the synthesis of porphyrin‐based MOFs for the cascade catalysis of the conversion of olefins to epoxides and their cycloaddition with CO2 to cyclic carbonates. The MOFs feature tunable dual active sites with synthetically controllable metal variations. A clear dependence of the metal combination on the catalytic performance of the MOF catalysts is shown. This work advances the understanding essential for designing sophisticated, multifunctional porphyrin MOFs for efficient one‐pot cascade catalysis.

Design and regulation of defective electrocatalysts.

Electrocatalysts are the key components of electrochemical energy storage and conversion devices. High performance electrocatalysts can effectively reduce the energy barrier of the chemical reactions, thereby improving the conversion efficiency of energy devices. The electrocatalytic reaction mainly experiences adsorption and desorption of molecules (reactants, intermediates and products) on a catalyst surface, accompanied by charge transfer processes. Therefore, surface control of electrocatalysts plays a pivotal role in catalyst design and optimization. In recent years, many studies have revealed that the rational design and regulation of a defect structure can result in rearrangement of the atomic structure on the catalyst surface, thereby efficaciously promoting the electrocatalytic performance. However, the relationship between defects and catalytic properties still remains to be understood. In this review, the types of defects, synthesis methods and characterization techniques are comprehensively summarized, and then the intrinsic relationship between defects and electrocatalytic performance is discussed. Moreover, the application and development of defects are reviewed in detail. Finally, the challenges existing in defective electrocatalysts are summarized and prospected, and the future research direction is also suggested. We hope that this review will provide some principal guidance and reference for researchers engaged in defect and catalysis research, better help researchers understand the research status and development trends in the field of defects and catalysis, and expand the application of high-performance defective electrocatalysts to the field of electrocatalytic engineering.

Catalysis in the digital age: Unlocking the power of data with machine learning

AbstractThe design and discovery of new and improved catalysts are driving forces for accelerating scientific and technological innovations in the fields of energy conversion, environmental remediation, and chemical industry. Recently, the use of machine learning (ML) in combination with experimental and/or theoretical data has emerged as a powerful tool for identifying optimal catalysts for various applications. This review focuses on how ML algorithms can be used in computational catalysis and materials science to gain a deeper understanding of the relationships between materials properties and their stability, activity, and selectivity. The development of scientific data repositories, data mining techniques, and ML tools that can navigate structural optimization problems are highlighted, leading to the discovery of highly efficient catalysts for a sustainable future. Several data‐driven ML models commonly used in catalysis research and their diverse applications in reaction prediction are discussed. The key challenges and limitations of using ML in catalysis research are presented, which arise from the catalyst's intrinsic complex nature. Finally, we conclude by summarizing the potential future directions in the area of ML‐guided catalyst development.This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Data Science > Artificial Intelligence/Machine Learning Electronic Structure Theory > Density Functional Theory

Site heterogeneity and broad surface-binding isotherms in modern catalysis: Building intuition beyond the Sabatier principle

Learning the science of heterogeneous catalysis and electrocatalysis always starts with the simple case of a flat, uniform surface with an ideal adsorbate. It has of course been recognized for a century that real catalysts are more complicated. For the increasingly complex catalysts of the 21st century, this Perspective argues that surface heterogeneity and non-ideal binding isotherms are central features, and their implications need to be incorporated in current thinking. A variety of systems are described herein where catalyst complexity leads to broad, non-Langmuirian surface isotherms for the binding of hydrogen atoms – and this occurs even for ideal, flat Pt(111) surfaces. Modern catalysis employs nanoscale materials whose surfaces have substantial step, edge, corner, impurity, and other defect sites, and they increasingly have both metallic and non-metallic elements MnXm, including metal oxides, chalcogenides, pnictides, carbides, doped carbons, etc. The surfaces of such catalysts are often not crystal facets of the bulk phase underneath, and they typically have a variety of potential active sites. Catalytic surfaces in operando are often non-stoichiometric, amorphous, dynamic, and impure, and often vary from one part of the surface to another. Understanding of the issues that arise at such nanoscale, multi-element catalysts is just beginning to emerge. Yet these catalysts are widely discussed using Brønsted/Bell-Evans-Polanyi (BEP) relations, volcano plots, Tafel slopes, the Butler-Volmer equation, and other linear free energy relations (LFERs), which all depend on the implicit assumption that the active sites are “similar” and that surface adsorption is close to ideal. These assumptions underly the ubiquitous intuition based on the Sabatier Principle, that the fastest catalysis will occur when key intermediates have free energies of adsorption that are not too strong nor too weak. Current catalysis research often aims to minimize the complexity of non-ideal isotherms through experimental and computational design (e.g., the use of single crystal surfaces), and these studies are the foundation of the field. In contrast, this Perspective argues that the heterogeneity of binding sites and binding energies is an inherent strength of these catalysts. This diversity makes many nanoscale catalysts inherently a high-throughput screen wrapped in a tiny package. Only by making the heterogeneity part of the foundation of catalysis models, sorting the types of active sites and dissecting non-ideal binding isotherms, will modern catalysis learn to harness the inherent diversity of real catalysts. Controlling and exploiting diversity rather than avoiding it will help to optimize complex modern catalysts and catalytic conditions.

Open-cage metallo-azafullerenes as efficient single-atom catalysts toward oxygen reduction reaction.

Very recently, open-cage metallo-azafullerenes PbC100N4H4 and Pb2C100N4H4 containing one Pb-N4-C moiety have been synthesized via the electron beam. Herein, we utilized density functional theory calculations in combination with abinitio molecular dynamics (AIMD) simulations to study the geometric and electronic structures, bonding properties, thermodynamic stability, and catalytic performance of MC100N4H4 and M2C100N4H4 (M = Ge, Sn, Pb). Metal-nitrogen distances and metal-metal distances increase along with the metal radius while the metal atom is positively charged. Energy decomposition analysis revealed that the bonding interactions between M and the C100N4H4 fragment could be described as the donor-acceptor interaction between M(ns0(n-1)d10np4) and C100N4H4 fragment, in which the orbital interactions terms contribute more than the electrostatic interactions. AIMD simulations demonstrate that those metallo-azafullerenes exhibit thermodynamic stability at room temperature. These metallo-azafullerenes, which could serve as typical carbon-supported single-atom catalysts, possess enhanced catalytic performance toward the oxygen reduction reaction (ORR) compared to the planar catalysts, which is attributed to the curvature of metallo-azafullerenes. GeC100N4H4 and SnC100N4H4 exhibit high catalytic performance in the 4e-ORR pathway to H2O, whereas only PbC100N4H4 is suitable for the 2e-ORR reaction pathway because of the difficulty in obtaining electrons. All M2C100N4H4 favors the 4e-reaction pathway due to the presence of the axial metal atom. Our finding of open-cage metallo-azafullerenes as efficient single-atom catalysts holds profound implications for both fundamental research in catalysis and practical applications in fuel cells and other electrochemical devices.

Do we really need ligands in Ir-catalyzed C-H borylation?

Direct borylation of C-H bonds is a privileged strategy to access versatile building blocks and valuable derivatives of complex molecules (late-stage functionalization, metabolite synthesis). This perspective aims to provide an overview and classification of the catalytic systems developed in this fast-growing area of research. Unexpected selectivity differences between two established directed-borylation systems have been discovered using high-throughput experimentation highlighting the importance of classical control experiments in catalysis research.

Universal Predictive Power: Introducing the Electronic Structure Decomposition Approach for CO Adsorption and Activation on Al2O3-Supported Ru Nanoparticles.

Accurate prediction of catalyst performance is crucial for designing materials with specific catalytic functions. While the density functional theory (DFT) method is widely used for its accuracy, modeling heterogeneous systems, especially supported transition metals, poses significant computational challenges. To address these challenges, we introduce the Electronic Structure Decomposition Approach (ESDA), a novel method that identifies specific density of states (DOS) areas responsible for adsorbate interaction and activation on the catalyst. As a case study, we investigate the influence of α-Al2O3(0001) as a support material on CO adsorption energy and the stretching frequency of the C-O bond on Ru nanoparticles (NPs). Using multiple linear regression analysis, ESDA models were trained with data from isolated Ru NPs and adjusted using supported NP sample data. The ESDA models accurately predict the CO adsorption energies and C-O vibrational frequencies, demonstrating strong linear correlations between predicted and DFT-calculated values with low errors across various adsorption sites for both isolated and supported Ru NPs. Beyond pinpointing the DOS areas responsible for CO adsorption and C-O bond activation, this study provides insights into manipulating these DOS areas to control CO activation, hence facilitating CO dissociation. Additionally, ESDA significantly accelerates the characterization and prediction of CO adsorption and activation on both isolated and supported Ru NPs compared to DFT calculations, expediting the design of new catalytic materials and advancing catalysis research. Furthermore, ESDA's reliance on the electronic structure as a descriptor suggests its potential for predicting various properties beyond catalysis, broadening its applicability across diverse scientific domains.

Hydrogels and Aerogels for Versatile Photo-/Electro-Chemical and Energy-Related Applications.

The development of photo-/electro-chemical and flexible electronics has stimulated research in catalysis, informatics, biomedicine, energy conversion, and storage applications. Gels (e.g., aerogel, hydrogel) comprise a range of polymers with three-dimensional (3D) network structures, where hydrophilic polyacrylamide, polyvinyl alcohol, copolymers, and hydroxides are the most widely studied for hydrogels, whereas 3D graphene, carbon, organic, and inorganic networks are widely studied for aerogels. Encapsulation of functional species with hydrogel building blocks can modify the optoelectronic, physicochemical, and mechanical properties. In addition, aerogels are a set of nanoporous or microporous 3D networks that bridge the macro- and nano-world. Different architectures modulate properties and have been adopted as a backbone substrate, enriching active sites and surface areas for photo-/electro-chemical energy conversion and storage applications. Fabrication via sol-gel processes, module assembly, and template routes have responded to professionalized features and enhanced performance. This review presents the most studied hydrogel materials, the classification of aerogel materials, and their applications in flexible sensors, batteries, supercapacitors, catalysis, biomedical, thermal insulation, etc.

A Versatile Electrochemical Sensor Polyaniline−Polypyrrole Co-Polymer for the Ultra-Sensitive Quercetin Detection

Quercetin has been shown to enhance inflammation, blood pressure, and blood sugar control. It may also have brain-protective, anti-allergy, and anticancer effects. In this work, a chemical oxidative polymerization approach is employed to synthesis of PANI-PPy copolymer for electrochemical biosensing of quercetin molecule. Unique, sustainable, low-cost, and simple to use, a synthesis of PANI-pPY copolymer has been achieved. The prepared PANI-PPy copolymer characterized with different technique like XRD, FT-IR, Raman, XPS, FESEM with EDAX and HR-TEM for identifying crystalline nature, functional group band gap, chemical composition, and morphology respectively. The surface morphology of the synthesized copolymers were in homogenous sphere like architecture. Synthesized copolymer is modified on glassy carbon electrode (GCE) to senses the biomolecule (Quercetin) by electrochemical method. PANI-PPy copolymer will greatly enhance the electrocatalytic oxidation of Quercetin (QR). The detection limit and quantification limit have been worked out to be 11.2 and 37.34 µM µA-1, respectively at neutral pH. All of the findings suggest that the PANI-PPy modified GCE is more sensitive to the oxidation of Quercetin. Eventually, the synthesized PANI-PPy hybrid may be used in catalysis research in addition to analytical laboratories due to its advantageous properties of enhanced catalytic activity and electrochemical sensitivity.

15 Years of Evolution of ChemCatChem

AbstractChemCatChem continues to serve an increasingly interdisciplinary and collaborative community. In our 15th year of existence, we share with you some of the initiatives that will bring our journal closer to the global and diverse community of scientists in catalysis research and adjacent fields.

Recent progress of operando transmission electron microscopy in heterogeneous catalysis

As a highly intricate process encompassing multiple length scales, catalysis research evolves into a comprehensive understanding of reaction kinetics across microscopic to atomic dimensions when electron microscopy, particularly the in situ transmission electron microscopy (TEM), emerges to be increasingly relevant. Meanwhile, the absence of effective methodologies for measuring reaction products during catalysis complicates efforts to elucidate the operational state and catalytic activity of the catalyst. With ongoing advancements of refined gas-cell design within TEM and other in situ accessories, diverse methodologies have emerged to ascertain the occurrence of chemical reactions. In this review, we summarized the recent progress of operando TEM while further extending its conceptual boundaries by including newly emerged reaction-detecting approaches capable of bridging microstructure to the reaction process. These methods involve not only traditional ones of product detection, e.g., in situ mass spectrometry and electron energy loss spectroscopy, but also other reaction-correlative characterizations, such as directly imaging reactant molecule, modified in situ reactor for thermogravimetry and temperature-programmed reaction, and TEM image-based microstructure quantification and activity correlation. Applications, inherent challenges, and our perspectives within these operando TEM techniques are deliberated.

Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems

The optimization of yields in multi-reactor systems, which are advanced tools in heterogeneous catalysis research, presents a significant challenge due to hierarchical technical constraints. To this respect, this work introduces a novel approach called process-constrained batch Bayesian optimization via Thompson sampling (pc-BO-TS) and its generalized hierarchical extension (hpc-BO-TS). This method, tailored for the efficiency demands in multi-reactor systems, integrates experimental constraints and balances between exploration and exploitation in a sequential batch optimization strategy. It offers an improvement over other Bayesian optimization methods. The performance of pc-BO-TS and hpc-BO-TS is validated in synthetic cases as well as in a realistic scenario based on data obtained from high-throughput experiments done on a multi-reactor system available in the REALCAT platform. The proposed methods often outperform other sequential Bayesian optimizations and existing process-constrained batch Bayesian optimization methods. This work proposes a novel approach to optimize the yield of a reaction in a multi-reactor system, marking a significant step forward in digital catalysis and generally in optimization methods for chemical engineering.

Technology Readiness and Emerging Prospects of Coupled Catalytic Reactions for Sustainable Chemical Value Chains.

Transitioning from both the direct and indirect use of fossil fuels to the renewable and sustainable resources of the near future demands a focal shift in catalysis research - from investigating catalytic reactions in isolation to developing coupled reactions for modern chemical value chains. In this Perspective, we discuss the status and emerging prospects of coupled catalytic reactions across various scales and provide key examples. Besides being a sustainable and essential alternative to current fossil-based processes, the coupling of catalytic reactions offers novel and scalable pathways to value-added chemicals. By emphasizing the specific requirements and challenges arising from coupled reactions, we aim to identify and underscore research needs that are critical to expedite their development and to fully unlock their potential for chemical and fuel production.

5-Hydroxymethylfurfural and its Downstream Chemicals: A Review of Catalytic Routes.

Biomass assumes an increasingly vital role in the realm of renewable energy and sustainable development due to its abundant availability, renewability, and minimal environmental impact. Within this context, 5-hydroxymethylfurfural (HMF), derived from sugar dehydration, stands out as a critical bio-derived product. It serves as a pivotal multifunctional platform compound, integral in synthesizing various vital chemicals, including furan-based polymers, fine chemicals, and biofuels. The high reactivity of HMF, attributed to its highly active aldehyde, hydroxyl, and furan ring, underscores the challenge of selectively regulating its conversion to obtain the desired products. This review highlights the research progress on efficient catalytic systems for HMF synthesis, oxidation, reduction, and etherification. Additionally, it outlines the techno-economic analysis (TEA) and prospective research directions for the production of furan-based chemicals. Despite significant progress in catalysis research, and certain process routes demonstrating substantial economics, with key indicators surpassing petroleum-based products, a gap persists between fundamental research and large-scale industrialization. This is due to the lack of comprehensive engineering research on bio-based chemicals, making the commercialization process a distant goal. These findings provide valuable insights for further development of this field.

Reac4Cat-Ontology: Harnessing the Power of Ontological Description Logic in Catalysis Research as a Practical Approach to Knowledge Inferences

Maximizing the use of digitally captured data is a key requirement for many of the late adopters of digital infrastructure. One of the newcomers is the chemical industry in the area of digitized laboratories. Here, tools and services that satisfy individual needs still need to be developed and distributed within the community. This work explores the potential of using graph databases — specifically those modeled via ontological knowledge graphs — to describe complex data linkages and draw logical conclusions. While knowledge graphs are not widely utilized in catalysis research, this study introduces a methodology to highlight their usability for semantic description and integration into diverse value chains with contact to the domain of (bio)chemistry and catalysis.A demonstration is performed how ontologies and their knowledge graphs can be applied to perform essential functions of semantic annotation to chemical reactions, which are difficult to model relational. Traditional data description methods can be neglected using description logic, showing how logical inferences at the machine level can enrich data. This work also illustrates the seamless integration of this enhanced data into process simulations, connecting semantic description with practical applications. The immediate benefits for catalysis research are emphasized and the development of new tools and services envisioned. By clarifying how these graphs can be integrated into existing workflows, researchers are empowered to make the most of digitally acquired data in catalytic processes. This practical methodology lays the foundation for improved decision-making and innovation, fostering advancements in the field of catalysis research.

One-pot synthesis of quinazolinone heterocyclic compounds using functionalized SBA-15 with natural material ellagic acid as a novel nanocatalyst

The nanoporous compound SBA-15 was functionalized using (3-aminopropyl)trimethoxysilane (APTES). Then the obtained product was modified with ellagic acid (ELA), a bioactive polyphenolic compound. The structure of the prepared nanoporous composition SBA-15@ELA was extensively characterized and confirmed by various techniques, such as Fourier-transform infrared (FT-IR) spectroscopy, Energy dispersive X-ray (EDX) elemental analysis, scanning electron microscopy (SEM), thermogravimetric analysis (TGA), X-ray diffraction (XRD), transmission electron microscopy (TEM) and N2 adsorption–desorption isotherms (BET). The novel, recoverable, heterogenous SBA-15@ELA nanoporous compound was used to investigate its catalytic effect in the synthesis of 4-oxo-quinazoline derivatives (19 examples) with high yields (78–96%), as an important class of nitrogen-containing heterocyclic compounds. The use of an inexpensive mesoporous catalyst with a high surface area, along with easy recovery by simple filtration are among the advantages of this catalysis research work. The catalyst has been used in at least 6 consecutive runs without a significant loss of its activity.

A mini review on the applications of artificial intelligence (AI) in surface chemistry and catalysis

Abstract This review critically analyzes the incorporation of artificial intelligence (AI) in surface chemistry and catalysis to emphasize the revolutionary impact of AI techniques in this field. The current review examines various studies that using AI techniques, including machine learning (ML), deep learning (DL), and neural networks (NNs), in surface chemistry and catalysis. It reviews the literature on the application of AI models in predicting adsorption behaviours, analyzing spectroscopic data, and improving catalyst screening processes. It combines both theoretical and empirical studies to provide a comprehensive synthesis of the findings. It demonstrates that AI applications have made remarkable progress in predicting the properties of nanostructured catalysts, discovering new materials for energy conversion, and developing efficient bimetallic catalysts for CO2 reduction. AI-based analyses, particularly using advanced NNs, have provided significant insights into the mechanisms and dynamics of catalytic reactions. It will be shown that AI plays a crucial role in surface chemistry and catalysis by significantly accelerating discovery and enhancing process optimization, resulting in enhanced efficiency and selectivity. This mini-review highlights the challenges of data quality, model interpretability, scalability, and ethical, and environmental concerns in AI-driven research. It highlights the importance of continued methodological advancements and responsible implementation of artificial intelligence in catalysis research.

Steering sp-Carbon Content in Graphdiynes for Enhanced Two-Electron Oxygen Reduction to Hydrogen Peroxide.

Compared to sp2-hybridized graphene, graphdiynes (GDYs) composed of sp and sp2 carbon are highly promising as efficient catalysts for electrocatalytic oxygen reduction into oxygen peroxide because of the high catalytic reactivity of the electron-rich sp-carbon atoms. The desired catalytic capacity of GDY, such as catalytic selectivity and efficiency, can theoretically be achieved by strategically steering the sp-carbon contents or the topological arrangement of the acetylenic linkages and aromatic bonds. Herein, we successfully tuned the electrocatalytic activity of GDYs by regulating the sp-to-sp2 carbon ratios with different organic monomer precursors. As the active sp-carbon atoms possess electron-sufficient π orbitals, they can donate electrons to the lowest unoccupied molecular orbital (LUMO) orbitals of O2 molecules and initiate subsequent O2 reduction, GDY with the high sp-carbon content of 50 at % exhibits excellent capability of catalyzing O2 reduction into H2O2. It demonstrates exceptional H2O2 selectivity of over 95.0 % and impressive performance in practical H2O2 production, Faraday efficiency (FE) exceeding 99.0 %, and a yield of 83.3 nmol s-1 cm-2. Our work holds significant importance in effectively steering the inherent properties of GDYs by purposefully adjusting the sp-to-sp2 carbon ratio and highlights their immense potential for research and applications in catalysis and other fields.

Steering sp‐Carbon Content in Graphdiynes for Enhanced Two‐Electron Oxygen Reduction to Hydrogen Peroxide

AbstractCompared to sp2‐hybridized graphene, graphdiynes (GDYs) composed of sp and sp2 carbon are highly promising as efficient catalysts for electrocatalytic oxygen reduction into oxygen peroxide because of the high catalytic reactivity of the electron‐rich sp‐carbon atoms. The desired catalytic capacity of GDY, such as catalytic selectivity and efficiency, can theoretically be achieved by strategically steering the sp‐carbon contents or the topological arrangement of the acetylenic linkages and aromatic bonds. Herein, we successfully tuned the electrocatalytic activity of GDYs by regulating the sp‐to‐sp2 carbon ratios with different organic monomer precursors. As the active sp‐carbon atoms possess electron‐sufficient π orbitals, they can donate electrons to the lowest unoccupied molecular orbital (LUMO) orbitals of O2 molecules and initiate subsequent O2 reduction, GDY with the high sp‐carbon content of 50 at % exhibits excellent capability of catalyzing O2 reduction into H2O2. It demonstrates exceptional H2O2 selectivity of over 95.0 % and impressive performance in practical H2O2 production, Faraday efficiency (FE) exceeding 99.0 %, and a yield of 83.3 nmol s−1 cm−2. Our work holds significant importance in effectively steering the inherent properties of GDYs by purposefully adjusting the sp‐to‐sp2 carbon ratio and highlights their immense potential for research and applications in catalysis and other fields.