Metal-support interactions in metal oxide-supported atomic, cluster, and nanoparticle catalysis

Supported metal catalysts are essential to a plethora of processes in the chemical industry. The overall performance of these catalysts depends strongly on the interaction of adsorbates at the atomic level, which can be manipulated and controlled by the different constituents of the active material (i.e., support and active metal). The description of catalyst activity and the relationship between active constituent and the support, or metal-support interactions (MSI), in heterogeneous (thermo)catalysts is a complex phenomenon with multivariate (dependent and independent) contributions that are difficult to disentangle, both experimentally and theoretically. So-called "strong metal-support interactions" have been reported for several decades and summarized in excellent review articles. However, in recent years, there has been a proliferation of new findings related to atomically dispersed metal sites, metal oxide defects, and, for example, the generation and evolution of MSI under reaction conditions, which has led to the designation of (sub)classifications of MSI deserving to be critically and systematically evaluated. These include dynamic restructuring under alternating redox and reaction conditions, adsorbate-induced MSI, and evidence of strong interactions in oxide-supported metal oxide catalysts. Here, we review recent literature on MSI in oxide-supported metal particles to provide an up-to-date understanding of the underlying physicochemical principles that dominate the observed effects in supported metal atomic, cluster, and nanoparticle catalysts. Critical evaluation of different subclassifications of MSI is provided, along with discussions on the formation mechanisms, theoretical and characterization advances, and tuning strategies to manipulate catalytic reaction performance. We also provide a perspective on the future of the field, and we discuss the analysis of different MSI effects on catalysis quantitatively.

Extraordinary Thermal Stability and Sinter Resistance of Sub-2 nm Platinum Nanoparticles Anchored to a Carbon Support by Selenium

Nanoparticle sintering has long been a major challenge in developing catalytic systems for use at elevated temperatures. Here we report an in situ electron microscopy study of the extraordinary sinter resistance of a catalytic system comprised of sub-2 nm Pt nanoparticles on a Se-decorated carbon support. When heated to 700 °C, the average size of the Pt nanoparticles only increased from 1.6 to 2.2 nm, while the crystal structure, together with the {111} and {100} facets, of the Pt nanoparticles was well retained. Our electron microscopy analyses suggested that the superior resistance against sintering originated from the Pt-Se interaction. Confirmed by energy-dispersive X-ray elemental mapping and electron energy loss spectra, the Se atoms surrounding the Pt nanoparticles could survive the heating. This work not only offers an understanding of the physics behind the thermal behavior of this catalytic material but also sheds light on the future development of sinter-resistant catalytic systems.

Sulfur-Stabilizing Ultrafine High-Entropy Alloy Nanoparticles on MXene for Highly Efficient Ethanol Electrooxidation

High-entropy alloys (HEAs) are significantly promising candidates for heterogeneous catalysis, yet the controllable synthesis of ultrafine HEA nanoparticles (NPs) remains a formidable challenge due to severe thermal sintering during the high-temperature fabrication process. Herein, we report a sulfur-stabilizing strategy to construct ultrafine HEA NPs with an average diameter of 4.02 nm supported on sulfur-modified Ti3C2Tx (S-Ti3C2Tx) MXene, on which the strong interfacial metal-sulfur interactions between HEA NPs and the S-Ti3C2Tx supports significantly increase the interfacial adhesion strength, thus greatly suppressing nanoparticle sintering by retarding both particle migration and metal atom diffusion. The representative quinary PtPdCuNiCo HEA-S-Ti3C2Tx exhibits excellent catalytic performance toward alkaline ethanol oxidation reaction (EOR) with an ultrahigh mass activity of 7.03 A mgPt+Pd-1, which is 4.34 and 5.17 times higher than those of the commercial Pt/C and Pd/C catalysts, respectively. In situ attenuated total reflection-infrared spectroscopy studies reveal that the high intrinsic catalytic activity for the EOR can be ascribed to the synergy of different catalytically active sites of HEA NPs and the well-designed interfacial metal-sulfur interactions.

Understanding the Pathway Switch of the Oxygen Reduction Reaction from Single- to Double-/Triple-Atom Catalysts: A Dual Channel for Electron Acceptance-Backdonation

Recently, a lot of attention has been dedicated to double- or triple-atom catalysts (DACs/TACs) as promising alternatives to platinum-based catalysts for the oxygen reduction reaction (ORR) in fuel cell applications. However, the ORR activity of DACs/TACs is usually theoretically understood or predicted using the single-site association pathway (O2 → OOH* → O* → OH* → H2O) proposed from Pt-based alloy and single-atom catalysts (SACs). Here, we investigate the ORR process on a series of graphene-supported Fe-Co DACs/TACs by means of first-principles calculation and an electrode microkinetic model. We propose that a dual channel for electron acceptance-backdonation on adjacent metal sites of DACs/TACs efficiently promotes O-O bond breakage compared with SACs, which makes ORR switch to proceed through dual-site dissociation pathways (O2 → O* + OH* → 2OH* → OH* → H2O) from the traditional single-site association pathway. Following this revised ORR network, a complete reaction phase diagram of DACs/TACs is established, where the preferential ORR pathways and activity can be described by a three-dimensional volcano plot spanned by the adsorption free energies of ΔG(O*) and ΔG(OH*). Besides, the kinetics preferability of dual-site dissociation pathways is also appropriate for other graphene- or oxide-supported DACs/TACs. The contribution of dual-site dissociation pathways, rather than the traditional single-site association pathway, makes the theoretical ORR activity of DACs/TACs in better agreement with available experiments, rationalizing the superior kinetic behavior of DACs/TACs to that of SACs. This work reveals the origin of ORR pathway switching from SACs to DACs/TACs, which broadens the ideas and lays the theoretical foundation for the rational design of DACs/TACs and may also be heuristic for other reactions catalyzed by DACs/TACs.

Toward a new definition of surface energy for late transition metals

Surface energy is a top-importance stability descriptor of transition metal-based catalysts. Here, we combined density functional theory (DFT) calculations and a tiling scheme measuring surface areas of metal structures to develop a simple computational model predicting the average surface energy of metal structures independently of their shape. The metals considered are W, Ru, Co, Ir, Ni, Pd, Pt, Cu, Ag and Au. Lorentzian trends derived from the DFT data proved effective at predicting the surface energy of metallic surfaces but not for metal clusters. We used machine-learning protocols to build an algorithm that improves the Lorentzian trend's accuracy and is able to predict the surface energies of metal surfaces of any crystal structure, i.e., face-centred cubic, hexagonal close-packed, and body-centred cubic, but also of nanostructures and sub-nanometer clusters. The machine-learning neural network takes easy-to-compute geometric features to predict metallic moieties surface energies with a mean absolute error of 0.091 J m^-2 and an R² score of 0.97.

Improving the HER Activity and Stability of Pt Nanoparticles by Titanium Oxynitride Support

Water electrolysis powered by renewables is regarded as the feasible route for the production of hydrogen, obtained at the cathode side through electrochemical hydrogen evolution reaction (HER). Herein, we present a rational strategy to improve the overall HER catalytic performance of Pt, which is known as the best monometallic catalyst for this reaction, by supporting it on a conductive titanium oxynitride (TiON_x) dispersed over reduced graphene oxide nanoribbons. Characterization of the Pt/TiON_x composite revealed the presence of small Pt particles with diameters between 2 and 3 nm, which are well dispersed over the TiON_x support. The Pt/TiON_x nanocomposite exhibited improved HER activity and stability with respect to the Pt/C benchmark in an acid electrolyte, which was ascribed to the strong metal–support interaction (SMSI) triggered between the TiON_x support and grafted Pt nanoparticles. SMSI between TiON_x and Pt was evidenced by X-ray photoelectron spectroscopy (XPS) through a shift of the binding energies of the characteristic Pt 4f photoelectron lines with respect to Pt/C. Density functional theory (DFT) calculations confirmed the strong interaction between Pt nanoparticles and the TiON_x support. This strong interaction improves the stability of Pt nanoparticles and weakens the binding of chemisorbed H atoms thereon. Both of these effects may result in enhanced HER activity.

Tabrizi B, Jabbour GE. Fast and Effective Deactivation of Human Coronavirus with Copper Oxide Suspensions

The COVID-19 pandemic has demonstrated the need for versatile and robust countermeasures against viral threats. A wide range of viruses, including SARS-CoV-2, the virus that causes COVID-19, can be deactivated by metal and metal-oxide surface coatings. However, such coatings are expensive and cannot easily be retrofitted to existing infrastructure. Low-cost materials to halt the propagation of a variety of viruses must be produced with minimal quantities of expensive precursors. In this regard, we show that commercially available copper oxide nanoparticle suspensions can deactivate more than 99.55% of the human coronavirus 229E in 30 min, confirming the particles' efficiency as a fast antiviral material.

Adatom Bonding Sites in a Nickel-Fe3 O4 (001) Single-Atom Model Catalyst and O2 Reactivity Unveiled by Surface Action Spectroscopy with Infrared Free-Electron Laser Light

Single-atom (SA) catalysis presently receives much attention with its promise to decrease the cost of the active material while increasing the catalyst's performance. However, key details such as the exact location of SA species and their stability are often unclear due to a lack of atomic level information. Here, we show how vibrational spectra measured with surface action spectroscopy (SAS) and density functional theory (DFT) simulations can differentiate between different adatom binding sites and determine the location of Ni and Au single atoms on Fe3 O4 (001). We reveal that Ni and Au adatoms selectively bind to surface oxygen ions which are octahedrally coordinated to Fe ions. In addition, we find that the Ni adatoms can activate O2 to superoxide in contrast to the bare surface and Ni in subsurface positions. Overall, we unveil the advantages of combining SAS and DFT for improving the understanding of single-atom catalysts.

A rapid feature selection method for catalyst design: Iterative Bayesian additive regression trees (iBART)

Feature selection (FS) methods often are used to develop data-driven descriptors (i.e., features) for rapidly predicting the functional properties of a physical or chemical system based on its composition and structure. FS algorithms identify descriptors from a candidate pool (i.e., feature space) built by feature engineering (FE) steps that construct complex features from the system's fundamental physical properties. Recursive FE, which involves repeated FE operations on the feature space, is necessary to build features with sufficient complexity to capture the physical behavior of a system. However, this approach creates a highly correlated feature space that contains millions or billions of candidate features. Such feature spaces are computationally demanding to process using traditional FS approaches that often struggle with strong collinearity. Herein, we address this shortcoming by developing a new method that interleaves the FE and FS steps to progressively build and select powerful descriptors with reduced computational demand. We call this method iterative Bayesian additive regression trees (iBART), as it iterates between FE with unary/binary operators and FS with Bayesian additive regression trees (BART). The capabilities of iBART are illustrated by extracting descriptors for predicting metal-support interactions in catalysis, which we compare to those predicted in our previous work using other state-of-the-art FS methods (i.e., least absolute shrinkage and selection operator + l0, sure independence screening and sparsifying operator, and Bayesian FS). iBART matches the performance of these methods yet uses a fraction of the computational resources because it generates a maximum feature space of size O(102), as opposed to O(106) generated by one-shot FE/FS methods.

Identification of Nanoscale Processes Associated with the Disorder-to-Order Transformation of Carbon-Supported Alloy Nanoparticles

Due to their ordered crystal structures and high structural stabilities, intermetallic nanoparticles often display enhanced catalytic, magnetic, and optical properties compared to their random alloy counterparts. Intermetallic nanoparticles can be achieved by thermal annealing of their disordered (random alloy) counterparts. However, high temperatures and long annealing times needed to achieve the disorder-to-order transition often lead to a loss of sample monodispersity and an increase in the average size of nanoparticles. Here, we performed ex situ powder X-ray diffraction (XRD) and in situ annealing transmission electron microscopy (TEM) experiments to elucidate nanoscale processes that contribute to the ordering of carbon-supported PdCu nanoparticles as a model system. Random alloy PdCu nanoparticles supported on carbon were thermally annealed for various lengths of time at the disorder-to-order phase transition temperature, where changes in nanoparticle size and the crystal phase were monitored. The nanoparticles were only completely transformed to the intermetallic phase by undertaking measures to deliberately increase their size by increasing the number of nanoparticles on the carbon support. In situ annealing TEM experiments reveal nanoscale processes that account for the disorder-to-order phase transformation. Five different processes were observed at 400 °C. Isolated nanoparticles remained in the random alloy phase or underwent a phase transformation to the intermetallic phase. Nanoparticles fused with neighboring nanoparticles resulting in no change in phase or conversion to the intermetallic phase. Evidence of vapor transport was also observed, as some isolated nanoparticles were found to diminish in size upon heating. These variable processes account for the heterogeneity often observed for intermetallic nanoparticle samples achieved through annealing and motivate the development of synthetic routes that suppress particle-particle coalescence, as well as investigating metal-support interactions to facilitate the disorder-to-order phase transformation under mild conditions. Overall, this work furthers our knowledge of the formation of intermetallic nanoparticles by thermal annealing approaches, which could accelerate the development of electrocatalysts and the application of intermetallic nanoparticles in magnetic storage devices.

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for example, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3-55 atoms with mean absolute errors in the range of 0.11-0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure-property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal-metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3-55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.

Quantification of critical particle distance for mitigating catalyst sintering

Supported metal nanoparticles are of universal importance in many industrial catalytic processes. Unfortunately, deactivation of supported metal catalysts via thermally induced sintering is a major concern especially for high-temperature reactions. Here, we demonstrate that the particle distance as an inherent parameter plays a pivotal role in catalyst sintering. We employ carbon black supported platinum for the model study, in which the particle distance is well controlled by changing platinum loading and carbon black supports with varied surface areas. Accordingly, we quantify a critical particle distance of platinum nanoparticles on carbon supports, over which the sintering can be mitigated greatly up to 900 °C. Based on in-situ aberration-corrected high-angle annular dark-field scanning transmission electron and theoretical studies, we find that enlarging particle distance to over the critical distance suppress the particle coalescence, and the critical particle distance itself depends sensitively on the strength of metal-support interactions.

Deactivation of supported metal catalysts via thermally induced sintering is a major concern in the catalysis community. Here, the authors demonstrate that enlarging particle distance to over the critical distance could suppress the particle coalescence greatly up to 900 °C.

Strong Metal-Support Interaction for 2D Materials: Application in Noble Metal/TiB2 Heterointerfaces and their Enhanced Catalytic Performance for Formic Acid Dehydrogenation

Strong metal-support interaction (SMSI) is a phenomenon commonly observed on heterogeneous catalysts. Here, direct evidence of SMSI between noble metal and 2D TiB₂ supports is reported. The temperature-induced TiB₂ overlayers encapsulate the metal nanoparticles, resulting in core-shell nanostructures that are sintering-resistant with metal loadings as high as 12.0 wt%. The TiO_x -terminated TiB₂ surfaces are the active sites catalyzing the dehydrogenation of formic acid at room temperature. In contrast to the trade-off between stability and activity in conventional SMSI, TiB₂ -based SMSI promotes catalytic activity and stability simultaneously. By optimizing the thickness and coverage of the overlayer, the Pt/TiB₂ catalyst displays an outstanding hydrogen productivity of 13.8 mmol g^-1 _cat h^-1 in 10.0 m aqueous solution without any additive or pH adjustment, with >99.9% selectivity toward CO₂ and H₂ . Theoretical studies suggest that the TiB₂ overlayers are stabilized on different transition metals through an interplay between covalent and electrostatic interactions. Furthermore, the computationally determined trends in metal-TiB₂ interactions are fully consistent with the experimental observations regarding the extent of SMSI on different transition metals. The present research introduces a new means to create thermally stable and catalytically active metal/support interfaces for scalable chemical and energy applications.

Comparative study of single-atom gold and iridium on CeO2{111}

Oxide-supported single-atom catalysts have shown promise for a variety of heterogeneous processes. In addition to their inherent activity and selectivity, these materials come at much lower financial cost, avoiding the use of full-bodied precious-metal catalysts, but at the conceptual expense that more complex structural and electronic considerations need to be understood if we are to exploit their full potential. Here, we focus on the adsorption of single-atom iridium at both stoichiometric and defective CeO₂{111} surfaces, by means of first-principles density functional theory. Reference calculations for the adsorption of single-atom gold, on the same set of substrates, provide a valuable set of benchmarks against which to interpret our iridium results.

Manipulation of CO adsorption over Me1/CeO2 by heterocation doping: Key roles of single-atom adsorption energy

The performance of metal atoms chemically bonded to oxide supports cannot be explained solely by the intrinsic properties of the metals such as the d-band center. Herein, we present an in-depth study of the correlation between metal-oxide interactions and the properties of the supported metal using CO adsorption on Me₁ (Fe₁, Co₁, and Ni₁) loaded over CeO₂ (111) doped with divalent (Ca, Sr, and Ba), trivalent (Al, Ga, Sc, Y, and La), and quadrivalent (Hf and Zr) heterocations. CO adsorption over Me₁ is strongly dependent on the binding energies of Me₁. Two factors led to this trend. First, the extent of the Me₁-surface oxygen (Me₁-O) bond relaxation during CO adsorption played a key role. Second, the d-band center shifted drastically because of charge transfer to the oxides. The shift is related to the oxophilicity of metals. Adsorption energies of Me₁ over oxides include the contributions of the factors described above. Therefore, we can predict the activities of Me₁ using the strength of anchoring by oxide supports. Results show that smaller ionic radii of the doped heterocations were associated with more tightly bound Me₁. This finding sheds light on the possibility of heterocation-doping manipulating the reactivity of the Me₁ catalyst based on theoretical predictions.

Titanium-Anchored Gold on Silica for Enhanced Catalytic Activity in Aqueous Ethanol Oxidation

The heterogeneously catalyzed oxidation of bioethanol offers a promising route to bio-based acetic acid. Here, we assess an alternative method to support gold nanoparticles, which aims to improve selectivity to acetic acid through minimizing over-oxidation to carbon dioxide. The most promising support system is 5 wt % titanium on silica, which combines the high surface area of silica with the stabilizing effect of titania on the gold particles. Compared to gold–silica systems, which require a complex synthesis method, small quantities of titanium promoted the formation of gold nanoparticles during a simple deposition–precipitation. Characterization of the catalyst with X-ray absorption spectroscopy shows that titanium is highly dispersed in the form of small, possibly dimeric, titanium(IV) structures, which are isolated and stabilize gold nanoparticles, possibly minimizing sintering effects during synthesis. The size of the gold particles depends on the pre-treatment of the titanium–silica support before gold deposition, with larger titanium structures hosting larger gold particles. Acetic acid yield over the titanium–silica-supported gold systems improved by about 1.6 times, compared to pure titania-supported gold. The high activity of those catalysts suggests that bulk, crystalline titania is not required for the reaction, encouraging the use of mixed supports to combine their benefits. Those support systems, besides improving selectivity, offer high surface area and a low-cost filler material, which brings ethanol oxidation one step further to the industry. Additionally, the low loading of titanium permits studying the reaction mechanisms on the gold–titanium interface with bulk characterization techniques.

Metal Clusters on Semiconductor Surfaces and Application in Catalysis with a Focus on Au and Ru

Metal clusters typically consist of two to a few hundred atoms and have unique properties that change with the type and number of atoms that form the cluster. Metal clusters can be generated with a precise number of atoms, and therefore have specific size, shape, and electronic structures. When metal clusters are deposited onto a substrate, their shape and electronic structure depend on the interaction with the substrate surface and thus depend on the properties of both the clusters and those of the substrate. Deposited metal clusters have discrete, individual electron energy levels that differ from the electron energy levels in the constituting individual atoms, isolated clusters, and the respective bulk material. The properties of clusters with a focus on Au and Ru, the methods to generate metal clusters, and the methods of deposition of clusters onto substrate surfaces are covered. The properties of cluster-modified surfaces are important for their application. The main application covered here is catalysis, and the methods for characterization of the cluster-modified surfaces are described.

Revealing Sintering Kinetics of MoS2-Supported Metal Nanocatalysts in Atmospheric Gas Environments via Operando Transmission Electron Microscopy

The decoration of two-dimensional (2D) substrates with nanoparticles (NPs) serve as heterostructures for various catalysis applications. Deep understanding of catalyst degradation mechanisms during service conditions is crucial to improve the catalyst durability. Herein, we studied the sintering behavior of Pt and bimetallic Au-core Pt-shell (Au@Pt core-shell) NPs on MoS₂ supports at high temperatures under vacuum, nitrogen (N₂), hydrogen (H₂), and air environments by in situ gas-cell transmission electron microscopy (TEM). The key observations are summarized as effect of environment: while particle migration and coalescence (PMC) was the main mechanism that led to Pt and Au@Pt NPs degradation under vacuum, N₂, and H₂ environments, the degradation of MoS₂ substrate was prominent under exposure to air at high temperatures. Pt NPs were less stable in H₂ environment when compared with the Pt NPs under vacuum or N₂, due to Pt-H interactions that weakened the adhesion of Pt on MoS₂. Effect of NP composition: under H₂, the stability of Au@Pt NPs was higher in comparison to Pt NPs. This is because H₂ promotes the alloying of Pt-Au, thus reducing the number of Pt at the surface (reducing H₂ interactions) and increasing Pt atoms in contact with MoS₂. Effect of NP size: The alloying effect promoted by H₂ was more pronounced in small size Au@Pt NPs resulting in their higher sintering resistance in comparison to large size Au@Pt NPs and similar size Pt NPs. The present work provides key insights into the parameters affecting the catalyst degradation mechanisms on 2D supports.

Predicting metal-metal interactions. II. Accelerating generalized schemes through physical insights

Operando-computational frameworks that integrate descriptors for catalyst stability within catalyst screening paradigms enable predictions of rates and selectivity on chemically faithful representations of nanoparticles under reaction conditions. These catalyst stability descriptors can be efficiently predicted by density functional theory (DFT)-based models. The alloy stability model, for example, predicts the stability of metal atoms in nanoparticles with site-by-site resolution. Herein, we use physical insights to present accelerated approaches of parameterizing this recently introduced alloy-stability model. These accelerated approaches meld quadratic functions for the energy of metal atoms in terms of the coordination number with linear correlations between model parameters and the cohesive energies of bulk metals. By interpolating across both the coordination number and chemical space, these accelerated approaches shrink the training set size for 12 fcc p- and d-block metals from 204 to as few as 24 DFT calculated total energies without sacrificing the accuracy of our model. We validate the accelerated approaches by predicting adsorption energies of metal atoms on extended surfaces and 147 atom cuboctahedral nanoparticles with mean absolute errors of 0.10 eV and 0.24 eV, respectively. This efficiency boost will enable a rapid and exhaustive exploration of the vast material space of transition metal alloys for catalytic applications.

Catalytic Stability Studies Employing Dedicated Model Catalysts

Long-term stability of heterogeneous catalysts is an omnipresent and pressing concern in industrial processes. Catalysts with high activity and selectivity can be searched for by high-throughput screening methods based maybe on educated guesses provided by ab initio thermodynamics or scaling relations. However, high-throughput screening is not feasible and is hardly able to identify long-term stable catalyst so that a rational and knowledge-driven approach is called for to identify potentially stable and active catalysts. Unfortunately, our current microscopic understanding on stability issues is quite poor. We propose that this gap in knowledge can be at least partly closed by investigating dedicated model catalyst materials with well-defined morphology that allow for a tight link to theory and the application of standard characterization methods. This topic is highly interdisciplinary, combining sophisticated inorganic synthesis with catalysis research, surface chemistry, and powerful theoretical modeling. In this Account, we focus on the stability issues of Deacon catalysts (RuO₂ and CeO₂-based materials) for recovering Cl₂ from HCl by aerobic oxidation and how to deepen our microscopic insight into the underlying processes. The main stability problems under harsh Deacon reaction conditions concomitant with a substantial loss in activity arise from deep chlorination of the catalyst, leaching of volatile chlorides and oxychlorides, and decrease in active surface area by particle sintering. In general, powder materials with undefined particle shape are not well suited for examining catalyst stability, because changes in the morphology are difficult to recognize, for instance, by electron microscopy. Rather, we focus here on model materials with well-defined starting morphologies, including electrospun nanofibers, shape-controlled nanoparticles, and well-defined ultrathin crystalline layers. CeO₂ is able to stabilize shape-controlled particles, exposing a single facet orientation so that comparing activity and stability studies can reveal structure sensitive properties. We develop a quasi-steady-state kinetic approach that allows us to model the catalyst chlorination as a function of temperature and gas feed composition. For the case of pure CeO₂ nanocubes, this simple approach predicts chlorination to be efficiently suppressed by addition of little amounts of water in the reaction feed or by keeping the catalyst at higher temperature. Both process parameters have great impact on the actual reactor design. Thermal stabilization of CeO₂ by intermixing Zr has been known in automotive exhaust catalysis for decades, but this does not necessarily imply also chemical stabilization of CeO₂ against bulk-chlorination since Zr can readily form volatile ZrCl₄ and may quickly lose its stabilizing effect. Nevertheless, with model experiments the stabilizing effect of Zr in the Deacon process over mixed Ce_xZr_1-xO₂ nanorods is clearly evidenced. Even higher stability can be accomplished with ultrathin CeO₂ coatings on preformed ZrO₂ particles, demonstrating the great promise of atomic layer deposition (ALD) in catalysis synthesis.

Understanding chemical and physical mechanisms in atomic layer deposition

Atomic layer deposition (ALD) is a powerful tool for achieving atomic level control in the deposition of thin films. However, several physical and chemical phenomena can occur which cause deviation from "ideal" film growth during ALD. Understanding the underlying mechanisms that cause these deviations is important to achieving even better control over the growth of the deposited material. Herein, we review several precursor chemisorption mechanisms and the effect of chemisorption on ALD growth. We then follow with a discussion on diffusion and its impact on film growth during ALD. Together, these two fundamental processes of chemisorption and diffusion underlie the majority of mechanisms which contribute to material growth during a given ALD process, and the recognition of their role allows for more rational design of ALD parameters.

Tuning the oxygen release properties of CeO2-based catalysts by metal–support interactions for improved gasoline soot combustion

CeO₂-supported Cu and Rh catalysts showed high soot combustion activities by their high oxygen release properties depending on moderate metal–oxygen bond energy.

A redox interaction-engaged strategy for multicomponent nanomaterials

The review article focuses on the redox interaction-engaged strategy that offers a powerful way to construct multicomponent nanomaterials with precisely-controlled size, shape, composition and hybridization of nanostructures.

New Strategies for the Preparation of Sinter-Resistant Metal-Nanoparticle-Based Catalysts

Supported metal nanoparticles are widely used as catalysts in the industrial production of chemicals, but still suffer from deactivation because of metal leaching and sintering at high temperature. In recent years, serious efforts have been devoted to developing new strategies for stabilizing metal nanoparticles. Recent developments for preparing sinter-resistant metal-nanoparticle catalysts via strong metal-support interactions, encapsulation with oxide or carbon layers and within mesoporous materials, and fixation in zeolite crystals, are briefly summarized. Furthermore, the current challenges and future perspectives for the preparation of highly efficient and extraordinarily stable metal-nanoparticle-based catalysts, and suggestions regarding the mechanisms involved in sinter resistance, are proposed.

Theoretical investigation of the ORR on boron-silicon nanotubes (B-SiNTs) as acceptable catalysts in fuel cells

Here, the potential of boron doped silicon nanotubes (7, 0) as ORR catalysts is examined. Acceptable paths for the ORR on studied catalysts are examined through DFT. The optimum mechanism of the ORR on the surface of B2-SiNT (7, 0) is shown. The ORR on the surface of B2-SiNTs (7, 0) can continue through LH and ER mechanisms. The calculated beginning voltage for the ORR on B2-SiNTs (7, 0) is 0.37 V and it is smaller than the beginning voltage (0.45 V) for platinum-based catalysts. In the acidic solution the beginning voltage for the oxygen reduction process can be evaluated to be 0.97 V, which corresponds to 0.37 V as a minimum overvoltage for the ORR. The B2-SiNTs (7, 0) are suggested as an ORR catalyst in acidic environments.

Investigation of performance of aluminum doped carbon nanotube (8, 0) as adequate catalyst to oxygen reduction reaction

The discovery of novel nano-catalysts to oxygen reduction reaction with high performance due to various application of fuel cells is very important. In present study the potential of aluminum doped carbon nanotube (8, 0) to oxygen reduction reaction in acidic condition was examined through theoretical models. The possible paths to oxygen reduction reaction on Al₂-CNT (8, 0) surfaces were investigated and optimal path was identified from thermodynamic standpoint. Results indicated that the Al₂-CNT (8, 0) catalyzed the oxygen reduction reaction through the Eley-Rideal and Langmuir-Hinshelwood mechanisms. The Al₂-CNT (8, 0) catalyst has much better methanol and CO tolerance than platinum-based catalysts. In this study, the overpotential value of oxygen reduction reaction on aluminum doped carbon nanotube (8, 0) surface (ca 0.38 V) is lower than corresponding values on platinum-based catalysts (ca 0.45 V). Finally, results demonstrated that the Al₂-CNT (8, 0) can be proposed as efficiency catalyst to oxygen reduction reaction.

Predicting Adsorption Properties of Catalytic Descriptors on Bimetallic Nanoalloys with Site-Specific Precision

Bimetallic nanoparticles present a vastly tunable structural and compositional design space rendering them promising materials for catalytic and energy applications. Yet it remains an enduring challenge to efficiently screen candidate alloys with atomic level specificity while explicitly accounting for their inherent stabilities under reaction conditions. Herein, by leveraging correlations between binding energies of metal adsorption sites and metal-adsorbate complexes, we predict adsorption energies of typical catalytic descriptors (OH*, CH₃*, CH*, and CO*) on bimetallic alloys with site-specific resolution. We demonstrate that our approach predicts adsorption energies on top and bridge sites of bimetallic nanoparticles having generic morphologies and chemical environments with errors between 0.09 and 0.18 eV. By forging a link between the inherent stability of an alloy and the adsorption properties of catalytic descriptors, we can now identify active site motifs in nanoalloys that possess targeted catalytic descriptor values while being thermodynamically stable under working conditions.