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Activating pretreatments are used to tune surface composition and structure of bimetallic-alloy catalysts. Herein, the activation-induced changes in material properties of a nanoporous Ag0.03Au0.97 alloy and their subsequent evolution under steady-state CH3OH oxidation conditions are investigated. Activation using O3 results in AgO and Au2O3, strongly enriching the near-surface region in Ag. These oxides reduce in the O2/CH3OH mixture, yielding CO2 and producing a highly Ag-enriched surface alloy. At the reaction temperature (423 K), Ag realloys gradually with Au but remains enriched (stabilized by surface O) in the top few nanometers, producing methyl formate selectively without significant deactivation. At higher temperatures, bulk diffusion induces sintering and Ag redistribution, leading to a loss of activity. These findings demonstrate that material properties determining catalytic activity are dynamic and that metastable (kinetically trapped) forms of the material may be responsible for catalysis, providing guiding principles concerning the activation of heterogeneous catalysts for selective oxidation.

Despite the broad catalytic relevance of metal-support interfaces, controlling their chemical nature, the interfacial contact perimeter (exposed to reactants), and consequently, their contributions to overall catalytic reactivity, remains challenging, as the nanoparticle and support characteristics are interdependent when catalysts are prepared by impregnation. Here, we decoupled both characteristics by using a raspberry-colloid-templating strategy that yields partially embedded PdAu nanoparticles within well-defined SiO 2 or TiO 2 supports, thereby increasing the metal-support interfacial contact compared to nonembedded catalysts that we prepared by attaching the same nanoparticles onto support surfaces. Between nonembedded PdAu/SiO 2 and PdAu/TiO 2 , we identified a support effect resulting in a 1.4-fold higher activity of PdAu/TiO 2 than PdAu/SiO 2 for benzaldehyde hydrogenation. Notably, partial nanoparticle embedding in the TiO 2 raspberry-colloid-templated support increased the metal-support interfacial perimeter and consequently, the number of Au/TiO 2 interfacial sites by 5.4-fold, which further enhanced the activity of PdAu/TiO 2 by an additional 4.1-fold. Theoretical calculations and in situ surface-sensitive desorption analyses reveal facile benzaldehyde binding at the Au/TiO 2 interface and at Pd ensembles on the nanoparticle surface, explaining the connection between the number of Au/TiO 2 interfacial sites (via the metal-support interfacial perimeter) and catalytic activity. Our results demonstrate partial nanoparticle embedding as a synthetic strategy to produce thermocatalytically stable catalysts and increase the number of catalytically active Au/TiO 2 interfacial sites to augment catalytic contributions arising from metal-support interfaces., Competing Interests: Competing interests statement:C.R. and G.J.H. participated in a Faraday Discussion together in 2021 (Faraday Discuss. 229, 378–421 (2021)].

Improving control over active-site reactivity is a grand challenge in catalysis. Single-atom alloys (SAAs) consisting of a reactive component doped as single atoms into a more inert host metal feature localized and well-defined active sites, but fine tuning their properties is challenging. Here, a framework is developed for tuning single-atom site reactivity by alloying in an additional inert metal, which this work terms an alloy-host SAA. Specifically, this work creates about 5% Pd single-atom sites in a Pd 33 Ag 67 (111) single crystal surface, and then identifies Sn based on computational screening as a suitable third metal to introduce. Subsequent experimental studies show that introducing Sn indeed modifies the electronic structure and chemical reactivity (measured by CO desorption energies) of the Pd sites. The modifications to both the electronic structure and the CO adsorption energies are in close agreement with the calculations. These results indicate that the use of an alloy host environment to modify the reactivity of single-atom sites can allow fine-tuning of catalytic performance and boost resistance against strong-binding adsorbates such as CO., (© 2024 The Author(s). Small published by Wiley‐VCH GmbH.)

Computation has long proven useful in understanding heterogeneous catalysts and rationalizing experimental findings. However, computational design with experimental validation requires somewhat different approaches and has proven more difficult. In recent years, there have been increasing successes in such computational design with experimental validation. In this Perspective, we discuss some of these recent successes and the methodologies used. We also discuss various design strategies more broadly, as well as approximations to consider and pitfalls to try to avoid when designing for experiment. Overall, computation can be a powerful and efficient tool in guiding catalyst design but must be combined with a strong fundamental understanding of catalysis science to be most effective in terms of both choosing the design methodology and choosing which materials to pursue experimentally., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

The use of visible photon fluxes to influence catalytic reactions on metal nanoparticle surfaces has attracted attention based on observations of reaction mechanisms and selectivity not observed under equilibrium heating. These observations suggest that photon fluxes can selectively impact the rates of certain elementary steps, creating nonequilibrium energy distributions among various reaction pathways. However, quantitative studies validating these hypotheses on metal nanoparticle surfaces are lacking. We examine the influence of continuous wave visible photon fluxes on the CO desorption rates from 1 to 2 nm diameter Pt and Pd nanoparticle surfaces supported on γ-Al 2 O 3 . Temperature-programmed desorption measurements quantified via diffuse reflectance infrared Fourier transform spectroscopy demonstrate that visible photon fluxes significantly enhanced the rate of CO desorption from Pt nanoparticles in a wavelength-dependent manner. 440 nm photons most efficiently promoted CO desorption from Pt nanoparticle surfaces, aligning with the excitation energy for the interfacial electronic transition within the Pt-CO bond. Conversely, visible photon fluxes had no measurable influence on CO desorption rates from Pd nanoparticle surfaces after accounting for photon-induced heating. Density functional theory calculations demonstrate that the Pt-CO bond exhibits a narrower LUMO resonance, stronger coupling between the photoexcitation and forces induced on the metal-C bond, and vibrational energy dissipation that more effectively couples to desorption as compared to Pd-CO. These results demonstrate the specificity photons provide in facilitating chemical reactions on metal nanoparticle surfaces and substantiate the idea that photon fluxes can steer processes and outcomes of catalytic reactions in ways not achievable by equilibrium heating.

Benzene, a high-volume chemical, is produced from larger molecules by inefficient and environmentally harmful processes. Recent changes in hydrocarbon feedstocks from oil to gas motivate research into small molecule upgrading. For example, the cyclotrimerization of acetylene reaction has been demonstrated on Pd, Pd alloy, and Cu surfaces and catalysts, but they are not 100% selective to benzene. We discovered that acetylene can be converted to benzene with 100% selectivity on the Ag(111) surface. Our temperature programmed desorption experiments reveal a threshold acetylene surface coverage of ∼one monolayer, above which benzene is formed. Furthermore, additional layers of acetylene increase the amount of benzene produced while retaining 100% selectivity. Our scanning tunneling microscopy images show that acetylene prefers square packing on the Ag(111) surface at low coverages, which converts to hexagonal packing when acetylene multilayers are present. Within this denser layer, features consistent with the proposed C 4 intermediates of the cyclotrimerization process are observed. Density functional theory calculations demonstrate that the barrier for forming the crucial C 4 intermediate generally decreases as acetylene multilayers are formed because the multilayer interacts more strongly with the surface in the transition state than in the initial state. Given that acetylene desorbs from Ag(111) at ∼90 K, the C 4 intermediate on the pathway to benzene must be formed below this temperature, implying that if Ag-based heterogeneous catalysts can be run at sufficiently high pressure and low enough temperature, efficient and selective trimerization of acetylene to benzene may be possible., Competing Interests: There are no conflicts of interest to declare., (This journal is © The Royal Society of Chemistry.)

Machine learning has been successfully applied in recent years to screen materials for a variety of applications. However, despite recent advances, most screening-based machine learning approaches are limited in generality and transferability, requiring new models to be created from scratch for each new application. This is particularly apparent in catalysis, where there are many possible intermediates and transition states of interest in addition to a large number of potential catalytic materials. In this work, we developed a new machine learning framework that is built on chemical principles and allows the creation of general, interpretable, reusable models. Our new architecture uses latent variables to create a set of submodels that each take on a relatively simple learning task, leading to higher data efficiency and promoting transfer learning. This architecture infuses fundamental chemical principles, such as the existence of elements as discrete entities. We show that this architecture allows for the creation of models that can be reused for many different applications, providing significant improvements in efficiency and convenience. For example, our architecture allows simultaneous prediction of adsorption energies for many adsorbates on a broad array of alloy surfaces with mean absolute errors (MAEs) around 0.20-0.25 eV. The integration of latent variables provides physical interpretability, as predictions can be explained in terms of the learned chemical environment as represented by the latent space. Further, these latent variables also serve as new feature representations, allowing efficient transfer learning. For example, new models with useful levels of accuracy can be created with less than 10 data points, including transfer learning to an experimental data set with an MAE less than 0.15 eV. Lastly, we show that our new machine learning architecture is general and robust enough to handle heterogeneous and multifidelity data sets, allowing researchers to leverage existing data sets to speed up screening using their own computational setup., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

Lithium-ion and sodium-ion batteries (LIBs and SIBs) are crucial in our shift toward sustainable technologies. In this work, the potential of layered boride materials (MoAlB and Mo 2 AlB 2 ) as novel, high-performance electrode materials for LIBs and SIBs, is explored. It is discovered that Mo 2 AlB 2 shows a higher specific capacity than MoAlB when used as an electrode material for LIBs, with a specific capacity of 593 mAh g -1 achieved after 500 cycles at 200 mA g -1 . It is also found that surface redox reactions are responsible for Li storage in Mo 2 AlB 2 , instead of intercalation or conversion. Moreover, the sodium hydroxide treatment of MoAlB leads to a porous morphology and higher specific capacities exceeding that of pristine MoAlB. When tested in SIBs, Mo 2 AlB 2 exhibits a specific capacity of 150 mAh g -1 at 20 mA g -1 . These findings suggest that layered borides have potential as electrode materials for both LIBs and SIBs, and highlight the importance of surface redox reactions in Li storage mechanisms., (© 2023 The Authors. Small Methods published by Wiley-VCH GmbH.)

Plasmonic catalysis provides a possible means for driving chemical reactions under relatively mild conditions. Rational design of these systems is impeded by the difficulty in understanding the electron dynamics and their interplay with reactions. Real-time, time-dependent density functional theory (RT-TDDFT) can provide dynamic information on excited states in plasmonic systems, including those relevant to plasmonic catalysis, at time scales and length scales that are otherwise out of reach of many experimental techniques. Here, we discuss previous RT-TDDFT studies of plasmonic systems, focusing on recent work that gains insight into plasmonic catalysis. These studies provide insight into plasmon dynamics, including size effects and the role of specific electronic states. Further, these studies provide significant insight into mechanisms underlying plasmonic catalysis, showing the importance of charge transfer between metal and adsorbate states, as well as local field enhancement, in different systems., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

Single-atom catalysts have received significant attention for their ability to enable highly selective reactions. However, many reactions require more than one adjacent site to align reactants or break specific bonds. For example, breaking a C-O or O-H bond may be facilitated by a dual site containing an oxophilic element and a carbophilic or "hydrogenphilic" element that binds each molecular fragment. However, design of stable and well-defined dual-atom sites with desirable reactivity is difficult due to the complexity of multicomponent catalytic surfaces. Here, we describe a new type of dual-atom system, trimetallic dual-atom alloys, which were designed via computation of the alloying energetics. Through a broad computational screening we discovered that Pt-Cr dimers embedded in Ag(111) can be formed by virtue of the negative mixing enthalpy of Pt and Cr in Ag and the favorable interaction between Pt and Cr. These dual-atom alloy sites were then realized experimentally through surface science experiments that enabled the active sites to be imaged and their reactivity related to their atomic-scale structure. Specifically, Pt-Cr sites in Ag(111) can convert ethanol, whereas PtAg and CrAg are unreactive toward ethanol. Calculations show that the oxophilic Cr atom and the hydrogenphilic Pt atom act synergistically to break the O-H bond. Furthermore, ensembles with more than one Cr atom, present at higher dopant loadings, produce ethylene. Our calculations have identified many other thermodynamically favorable dual-atom alloy sites, and hence this work highlights a new class of materials that should offer new and useful chemical reactivity beyond the single-atom paradigm.

Single-atom alloys (SAAs) have drawn significant attention in recent years due to their excellent catalytic properties. Controlling the geometry and electronic structure of this type of localized catalytic active site is of fundamental and technological importance. Dual-atom alloys (DAAs) consisting of a heterometallic dimer embedded in the surface layer of a metal host would bring increased tunability and a larger active site, as compared to SAAs. Here, we use computational studies to show that DAAs allow tuning of the active site electronic structure and reactivity. Interestingly, combining two SAAs into a dual-atom site can result in molecular-like hybridization by virtue of the free-atom-like electronic d states exhibited by many SAAs. DAAs can inherit the weak d-d interaction between dopants and hosts from the constituent SAAs, but exhibit new electronic and reactive properties due to dopant-dopant interactions in the DAA. We identify many heterometallic DAAs that we predict to be more stable than either the constituent SAAs or homometallic dual-atom sites of each dopant. We also show how both electronic and ensemble effects can modify the strength of CO adsorption. Because of the molecular-like interactions that can occur, DAAs require a different approach for tuning chemical properties compared to what is used for previous classes of alloys. This work provides insights into the unique catalytic properties of DAAs, and opens up new possibilities for tailoring localized and well-defined catalytic active sites for optimal reaction pathways., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

The development of new catalyst materials for energy-efficient chemical synthesis is critical as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive processes specifically using heterogeneous catalysis. Catalytic performance is a complex interplay of phenomena involving temperature, pressure, gas composition, surface composition, and structure over multiple length and time scales. In response to this complexity, the integrated approach to heterogeneous dilute alloy catalysis reviewed here brings together materials synthesis, mechanistic surface chemistry, reaction kinetics, in situ and operando characterization, and theoretical calculations in a coordinated effort to develop design principles to predict and improve catalytic selectivity. Dilute alloy catalysts─in which isolated atoms or small ensembles of the minority metal on the host metal lead to enhanced reactivity while retaining selectivity─are particularly promising as selective catalysts. Several dilute alloy materials using Au, Ag, and Cu as the majority host element, including more recently introduced support-free nanoporous metals and oxide-supported nanoparticle "raspberry colloid templated (RCT)" materials, are reviewed for selective oxidation and hydrogenation reactions. Progress in understanding how such dilute alloy catalysts can be used to enhance selectivity of key synthetic reactions is reviewed, including quantitative scaling from model studies to catalytic conditions. The dynamic evolution of catalyst structure and composition studied in surface science and catalytic conditions and their relationship to catalytic function are also discussed, followed by advanced characterization and theoretical modeling that have been developed to determine the distribution of minority metal atoms at or near the surface. The integrated approach demonstrates the success of bridging the divide between fundamental knowledge and design of catalytic processes in complex catalytic systems, which can accelerate the development of new and efficient catalytic processes.

Single-atom alloys can be effective catalysts and have been compared to supported single-atom catalysts. To rationally design single-atom alloys and other surfaces with localized ensembles, it is crucial to understand variations in reactivity when varying the dopant and the ensemble size. Here, we examined hydrogen adsorption on surfaces embedded with localized clusters and discovered general trends. Counterintuitively, increasing the amount of a more reactive metal sometimes makes a surface site less reactive. This behavior is due to the hybridization and splitting of narrow peaks in the electronic density of states of many of these surfaces, making them analogous to free-standing nanoclusters. When a single-atom alloy has a peak just below the Fermi energy, the corresponding two-dopant cluster often has weaker adsorption than the single-atom alloy due to splitting of this peak across the Fermi energy. Furthermore, single-atom alloys have qualitatively different behaviors than larger ensembles. Specifically, the adsorption energy is a U-shaped function of the dopant's group for single-atom alloys. Additionally, adsorption energies on single-atom alloys correlate more strongly with the dopant's p-band center than with the d-band center.

Nanosystems are gaining momentum in pharmaceutical sciences because of the wide variety of possibilities for designing these systems to have specific functions. Specifically, studies of new cancer cotherapy drug-vitamin release nanosystems (DVRNs) including anticancer compounds and vitamins or vitamin derivatives have revealed encouraging results. However, the number of possible combinations of design and synthesis conditions is remarkably high. In addition, a large number of anticancer and vitamin derivatives have been already assayed, but a notably less number of cases of DVRNs were assayed as a whole (with the anticancer compound and the vitamin linked to them). Our approach combines with the perturbation theory and machine learning (PTML) model to predict the probability of obtaining an interesting DVRN by changing the anticancer compound and/or the vitamin present in a DVRN that is already tested for other anticancer compounds or vitamins that have not been tested yet as part of a DVRN. In a previous work, we built a linear PTML model useful for the design of these nanosystems. In doing so, we used information fusion (IF) techniques to carry out data enrichment of DVRN data compiled from the literature with the data for preclinical assays of vitamins from the ChEMBL database. The design features of DVRNs and the assay conditions of nanoparticles (NPs) and vitamins were included as multiplicative PT operators (PTOs) to the system, which indicates the importance of these variables. However, the previous work omitted experiments with nonlinear ML techniques and different types of PTOs such as metric-based PTOs. More importantly, the previous work does not consider the structure of the anticancer drug to be included in the new DVRNs. In this work, we are going to accomplish three main objectives (tasks). In the first task, we found a new model, alternative to the one published before, for the rational design of DVRNs using metric-based PTOs. The most accurate PTML model was the artificial neural network model, which showed values of specificity, sensitivity, and accuracy in the range of 90-95% in training and external validation series for more than 130,000 cases (DVRNs vs ChEMBL assays). Furthermore, in the second task, we used IF techniques to carry out data enrichment of our previous data set. In doing so, we constructed a new working data set of >970,000 cases with the data of preclinical assays of DVRNs, vitamins, and anticancer compounds from the ChEMBL database. All these assays have multiple continuous variables or descriptors d k and categorical variables c j (conditions of the assays) for drugs ( d ac k , c ac j ), vitamins ( d v k , c v j ), and NPs ( d n k , c n j ). These data include >20,000 potential anticancer compounds with >270 protein targets ( c ac1 ), >580 assay cell organisms ( c ac2 ), and so forth. Furthermore, we include >36,000 assay vitamin derivatives in >6200 types of cells ( c 2vit ), >120 assay organisms ( c 3vit ), >60 assay strains ( c 4vit ), and so forth. The enriched data set also contains >20 types of DVRNs ( c 5n ) with 9 NP core materials ( c 4n ), 8 synthesis methods ( c 7n ), and so forth. We expressed all this information with PTOs and developed a qualitatively new PTML model that incorporates information of the anticancer drugs. This new model presents 96-97% of accuracy for training and external validation subsets. In the last task, we carried out a comparative study of ML and/or PTML models published and described how the models we are presenting cover the gap of knowledge in terms of drug delivery. In conclusion, we present here for the first time a multipurpose PTML model that is able to select NPs, anticancer compounds, and vitamins and their conditions of assay for DVRN design.

Adsorption energies on surfaces are excellent descriptors of their chemical properties, including their catalytic performance. High-throughput adsorption energy predictions can therefore help accelerate first-principles catalyst design. To this end, we present over 5000 DFT calculations of H adsorption energies on dilute Ag alloys and describe a general machine learning approach to rapidly predict H adsorption energies for new Ag alloy structures. We find that random forests provide accurate predictions and that the best features are combinations of traditional chemical and structural descriptors. Further analysis of our model errors and the underlying forest kernel reveals unexpected finite-size electronic structure effects: embedded dopant atoms can display counterintuitive behavior such as nonmonotonic trends as a function of composition and high sensitivity to dopants far from the adsorbing H atom. We explain these behaviors with simple tight-binding Hamiltonians and d-orbital densities of states. We also use variations among forest leaves to predict the uncertainty of predictions, which allows us to mitigate the effects of larger errors.

Copper surfaces exhibit high catalytic selectivity but have poor hydrogen dissociation kinetics; therefore, we consider icosahedral Cu 13 nanoclusters to understand how nanoscale structure might improve catalytic prospects. We find that the spin state is a surprisingly important design consideration. Cu 13 clusters have large magnetic moments due to finite size and symmetry effects and exhibit magnetization-dependent catalytic behavior. The most favorable transition state for hydrogen dissociation has a lower activation energy than that on single-crystal copper surfaces but requires a magnetization switch from 5 to 3 μ B . Without this switch, the activation energy is higher than that on single-crystal surfaces. Weak spin-orbit coupling hinders this switch, decreasing the kinetic rate of hydrogen dissociation by a factor of 16. We consider strategies to facilitate magnetization switches through optical excitations, substitution, charge states, and co-catalysts; these considerations demonstrate how control of magnetic properties could improve catalytic performance.

The activation of O 2 on metal surfaces is a critical process for heterogeneous catalysis and materials oxidation. Fundamental studies of well-defined metal surfaces using a variety of techniques have given crucial insight into the mechanisms, energetics, and dynamics of O 2 adsorption and dissociation. Here, trends in the activation of O 2 on transition metal surfaces are discussed, and various O 2 adsorption states are described in terms of both electronic structure and geometry. The mechanism and dynamics of O 2 dissociation are also reviewed, including the importance of the spin transition. The reactivity of O 2 and O toward reactant molecules is also briefly discussed in the context of catalysis. The reactivity of a surface toward O 2 generally correlates with the adsorption strength of O, the tendency to oxidize, and the heat of formation of the oxide. Periodic trends can be rationalized in terms of attractive and repulsive interactions with the d-band, such that inert metals tend to feature a full d band that is low energy and has a large spatial overlap with adsorbate states. More open surfaces or undercoordinated defect sites can be much more reactive than close-packed surfaces. Reactions between O and other species tend to be more prevalent than reactions between O 2 and other species, particularly on more reactive surfaces.

The surface structure and composition of a multi-component catalyst are critical factors in determining its catalytic performance. The surface composition can depend on the local pressure of the reacting species, leading to the possibility that the flow through a nanoporous catalyst can affect its structure and reactivity. Here, we explore this possibility for oxidation reactions on nanoporous gold, an AgAu bimetallic catalyst. We use microscopy and digital reconstruction to obtain the morphology of a two-dimensional slice of a nanoporous gold sample. Using lattice Boltzmann fluid dynamics simulations along with thermodynamic models based on first-principles total-energy calculations, we show that some sections of this sample have low local O 2 partial pressures when exposed to reaction conditions, which leads to a pure Au surface in these regions, instead of the active bimetallic AgAu phase. We also explore the effect of temperature on the surface structure and find that moderate temperatures (≈300-450 K) should result in the highest intrinsic catalytic performance, in apparent agreement with experimental results.

One of the most critical factors in oxidation catalysis is controlling the state of oxygen on the surface. Au and Ag are both effective selective oxidation catalysts for various reactions, and their interactions with oxygen are critical for determining their catalytic performance. Here, we show that the state of oxygen on a catalytic surface can be controlled by alloying Au and Ag. Using temperature programmed desorption, density functional theory (DFT), and Monte Carlo simulations, we examine how alloying Au into an Ag(110) surface affects O 2 dissociation, O coverage, and O stability. DFT calculations indicate that Au resides in the second layer, in agreement with previous experimental findings. The minimum ensemble size for O 2 dissociation is 2 Ag atoms in adjacent rows of the second layer. Surprisingly, adsorbed O 2 and the dissociation transition state interact directly with metal atoms in the adjacent trough, such that Au in this position inhibits O 2 dissociation by direct repulsion with oxygen electronic states. Using Monte Carlo simulations based on DFT energetics, we create models of the surface that agree closely with our experimental results. Both show that the O 2 uptake decreases nearly linearly as the Au concentration increases, and no O 2 uptake occurs for Au concentrations above 50%. For Au concentrations greater than 18%, increasing the Au concentration also decreases the stability of the adsorbed O. Based on these results, the O coverage and O stability can be tuned, in some cases independently. We also study how the reactivity of the surface is affected by these factors using CO 2 oxidation as a simple test reaction.

Hydrocarbon chains are important intermediates in various aqueous-phase surface processes, such as CO2 electroreduction, aqueous Fischer-Tropsch synthesis, and aqueous phase reforming of biomass-derived molecules. Further, the interaction between water and adsorbed hydrocarbons represents a difficult case for modern computational methods. Here, we explore various methods for calculating the energetics of this interaction within the framework of density functional theory and explore trade-offs between the use of low water coverages, molecular dynamics approaches, and minima hopping for identification of low energy structures. An effective methodology for simulating low temperature processes is provided by using a unit cell in which the vacuum space is filled with water, employing the minima hopping algorithm to search for low-lying minima, and including dispersion (van der Waals) interactions. Using this methodology, we show that a high coverage of adsorbed alkyls is destabilized by the presence of water, while a low coverage of alkyls is stabilized. Solvation has a small effect on the energetics of hydrocarbon chain growth, generally decreasing its favorability at low temperatures. We studied higher temperatures by running molecular dynamics simulations starting at the minima found by the minima hopping algorithm and found that increased temperatures facilitate chain growth. The self-consistent continuum solvation method effectively describes the alkyl-water interaction and is in general agreement with the explicit solvation results in most cases, but care should be taken at high alkyl coverage.

Decreasing energy consumption in the production of platform chemicals is necessary to improve the sustainability of the chemical industry, which is the largest consumer of delivered energy. The majority of industrial chemical transformations rely on catalysts, and therefore designing new materials that catalyse the production of important chemicals via more selective and energy-efficient processes is a promising pathway to reducing energy use by the chemical industry. Efficiently designing new catalysts benefits from an integrated approach involving fundamental experimental studies and theoretical modelling in addition to evaluation of materials under working catalytic conditions. In this review, we outline this approach in the context of a particular catalyst-nanoporous gold (npAu)-which is an unsupported, dilute AgAu alloy catalyst that is highly active for the selective oxidative transformation of alcohols. Fundamental surface science studies on Au single crystals and AgAu thin-film alloys in combination with theoretical modelling were used to identify the principles which define the reactivity of npAu and subsequently enabled prediction of new reactive pathways on this material. Specifically, weak van der Waals interactions are key to the selectivity of Au materials, including npAu. We also briefly describe other systems in which this integrated approach was applied., (© 2016 The Author(s).)

Mood disorders affect the daily lives of millions of people worldwide. The search for more efficient therapies for mood disorders remains an active field of research. In silico approaches can accelerate the search for inhibitors against protein targets related to mood disorders. Here, we developed the first model perturbation-theory machine learning model based on a multiplayer perceptron network (PTML-MLP) for the simultaneous prediction and design of virtual dual-target inhibitors against two proteins associated with mood disorders, namely norepinephrine and serotonin transporters (NET and SERT, respectively). The PTML-MLP model had an accuracy of around 80%. From a chemical point of view, the PTML-MLP model could accurately identify both single- and dual-target inhibitors present in the dataset used to build it. Through the application of the fragment-based topological design (FBTD) approach, the molecular descriptors (multi-label graph-based indices) present in the PTML-MLP model were physicochemically and structurally interpreted. Such interpretations enabled (a) the extraction of different molecular fragments with a positive influence on the enhancement of the dual-target activity and (b) the design of four new drug-like molecules by assembling (fusing and/or connecting) several suitable molecular fragments. The designed molecules were predicted by the PTML-MLP model to exhibit dual-target activity against the NET and SERT proteins. These predictions, together with the estimated druglikeness suggest that the designed molecules could be new promising chemotypes to be considered for future synthesis and biological experimentation in the context of treatments for mood disorders. [ABSTRACT FROM AUTHOR]

Superbugs in groundwater are posing severe health risks through waterborne pathways. An emerging approach for green disinfection lies at photocatalysis, which levers the locally generated superoxide radical (·O2−) for neutralization. However, the spin-forbidden feature of O2 hinders the photocatalytic generation of active ·O2−, and thus greatly limited the disinfection efficiency, especially for real groundwater with a low dissolved oxygen (DO) concentration. Herein, we report a class of strained Mo4/3B2-xTz MBene (MB) with enhanced adsorption/activation of molecular O2 for photocatalytic disinfection, and find the strain induced spin polarization of In2S3/Mo4/3B2-xTz (IS/MB) can facilitate the spin-orbit hybridization of Mo sites and O2 to overcome the spin-forbidden of O2, which results in a 16.59-fold increase in ·O2− photocatalytic production in low DO condition (2.46 mg L-1). In particular, we demonstrate an In2S3/Mo4/3B2-xTz (50 mg)-based continuous-flow-disinfection system stably operates over 62 h and collects 37.2 L bacteria-free groundwater, which represents state-of-the-art photodisinfection materials for groundwater disinfection. Most importantly, the disinfection capacity of the continuous-flow-disinfection system is 25 times higher than that of commercial sodium hypochlorite (NaOCl), suggesting the practical potential for groundwater purification. Photocatalytic disinfection is a known pollution-free water treatment strategy. However, its efficiency is limited due to the spin-forbidden feature of O2, which hinders photocatalytic ·O2− formation and limits its disinfection efficiency. Here the authors produce a strained Mo4/3B2-xTz MBene that can increase ·O2− photocatalytic production, which results in high levels of photocatalytic disinfection. [ABSTRACT FROM AUTHOR]

Hydrogen has emerged as a promising energy source for low-carbon and sustainable mobility purposes. However, its applications are still limited by modest conversion efficiency in the electrocatalytic oxygen reduction reaction (ORR) within fuel cells. The complex nature of the ORR and the presence of strong electronic correlations present challenges to atomistic modelling using classical computers. This scenario opens new avenues for the implementation of novel quantum computing workflows. Here, we present a state-of-the-art study that combines classical and quantum computational approaches to investigate ORR on platinum-based surfaces. Our research demonstrates, for the first time, the feasibility of implementing this workflow on the H1-series trapped-ion quantum computer and identify the challenges of the quantum chemistry modelling of this reaction. The results highlight the great potentiality of quantum computers in solving notoriously difficult systems with strongly correlated electronic structures and suggest platinum/cobalt as ideal candidate for showcasing quantum advantage in future applications. [ABSTRACT FROM AUTHOR]

Active learning comprises machine learning-based approaches that integrate surrogate model inference, exploitation and exploration strategies with active experimental feedback into a closed-loop framework. This approach aims at describing and predicting specific material properties, without requiring lengthy, expensive or repetitive experiments. Recently, active learning has shown potential as an approach for the design of sustainable materials, such as scrap-compatible alloys, and for enhancing the longevity of metallic materials. However, in-depth investigations into suited best-practice strategies of active learning for sustainable materials science are still scarce. This study aims to present and discuss active learning strategies for developing and improving sustainable alloys, addressing single-objective and multi-objective learning and modelling scenarios. As model cases, we discuss active learning strategies for optimizing Invar and magnetic alloys, representing single-objective scenarios, and more general steel design approaches, exemplifying multi-objective optimization. We discuss the significance of finding the right balance between exploitation and exploration strategies in active learning and suggest strategies to reduce the number of iterations across diverse scenarios. This kind of research aims to find metrics for a more effective application of active learning and is used here to advance the field of sustainable alloy design. This article is part of the discussion meeting issue 'Sustainable metals: science and systems'. [ABSTRACT FROM AUTHOR]

A key issue in catalyst design is understanding how adsorption energies of surface intermediates vary across both different surfaces and various types of adsorbing atoms. In this work, we examine trends in adsorption energies of a wide variety of adsorbates that attach to transition metal surfaces through different atoms (H, C, N, O, F, S, etc.). All adsorption energies, as calculated by density functional theory, have nearly identical dependence on the metal bands (the d-band center and the number of p electrons) and the adsorbates' highest occupied molecular orbital (HOMO) energies. However, the dependence on the adsorbate-surface coupling and the d-band filling varies with the energy of the HOMO. Adsorbates with low HOMOs experience a higher level of Pauli repulsion than those with higher HOMOs. This leads to a classification of adsorbates into two groups, where adsorption energies in each group correlate. Even across the groups, adsorbates with similar HOMO energies are likely to have correlated adsorption energies.

To better understand the nature of alkyl intermediates often invoked in reactions involving hydrocarbon reactants and products, the adsorption of linear and branched C(1)-C(4) alkyls on Cu(111) at 1/4 ML and 1/9 ML coverages was studied using density functional theory. The adsorption energy and site preference are found to be coverage-dependent, and both direct alkyl-alkyl interactions and changes in the Cu electronic structure play a role in these trends. It was found that methyl strongly prefers the hollow sites, the branched alkyls strongly prefer the top site, and the linear C(2)-C(4) alkyls have weak site preferences that change with coverage. To explain these differences, rationalize alkyl adsorption trends, and predict the binding energy of other alkyls, a simple model was developed in which the binding energy is fit as a linear function of the number of C-Cu and C-H-Cu interactions as well as the C-H bond energy in the corresponding alkane. Site preference can be understood as a compromise between C-Cu interactions and C-H-Cu interactions. Density of states analysis was used to gain a molecular-orbital understanding of the bonding of alkyls to Cu(111).

Water-electrolysis is a promising technology for hydrogen production, but the precious metal catalysts (such as Ir and Pt) used in water-electrolysis limit its commercial application at a large scale. In order to further reduce the cost of hydrogen production by water-electrolysis, the development of efficient, stable and low-cost electrocatalysts is the focus of current research. Theoretical calculation is helpful to find out the general principle between catalysts structure, adsorption properties of intermediates and catalytic activity to reduce the research and development cost of novel catalysts. The volcano relationships are widely used in hydrogen evolution reaction(HER) and oxygen evolution reaction(OER) to provide a fundamental theory for high-throughput screening of high-active catalysts. The reaction mechanism of HER and OER is summarized, the basic concept of the volcano relationships of HER and OER is expounded, and the general method constructing the volcano relationships is also summarized. [ABSTRACT FROM AUTHOR]

The successful large-scale implementation of hydrogen as an energy vector requires high performance electrodes and catalysts made of abundant materials. Rational materials design strategies are the most efficient means of reaching this goal. Here we present a study on the adsorption of H-atoms onto fcc transition metal surfaces and propose descriptors for the rational design of electrodes and catalysts by means of correlations between fundamental properties of the materials and among other properties, their experimentally measured performance as hydrogen evolution electrodes (HEE). A large set of quantum mechanical modelling data at the DFT level was produced, covering the adsorption of H-atoms onto the most stable surfaces (100), (110) and (111) of: Ag, Au, Co, Cu, Ir, Ni, Pd, Pt and Rh. For each material and surface, a coverage dependent set of minimum energy structures was produced and chemical potentials for adsorption of H-atoms were obtained. Averaging procedures are here proposed to approach modelling to the experiments. Several correlations between the computed data and experimentally measured quantities are done to validate our methodology: surface plane dependent adsorption energies, chemical potentials and experimentally determined surface energies and work functions. We search for descriptors of catalytic activity by testing correlations between the DFT data obtained from our averaging procedures and experimental data on HEE performance. Our methodology allows us to obtain linear correlations between the adsorption energy of H-atoms and the exchange current density (i0) in a HEE, avoiding the volcano-like plots. We show that the chemical potential has limitations as a descriptor of i0 because it reaches an early plateau in terms of i0. Simple quantities obtained from database data such as the first stage electronegativity (χ) as devised by Mulliken has a strong linear correlation i0. With a quantity we denominate modified second-stage electronegativity (χ2m) we can reproduce the typical volcano plot in a correlation with i0. A theoretical and conceptual framework is presented. It shows that both χ and χ2m, that depend on the first ionization potential, second ionization potential and electron affinity of the elements can be used as descriptors in rational design of electrodes or of catalysts for hydrogen systems. [ABSTRACT FROM AUTHOR]

The recent applications discovered in the use of silver and bismuth nanoalloys, as well as the new environmentally friendly techniques used in their production, have aroused the interest of the scientific community in this study. In this study, we used ab initio density functional theory (DFT) implemented in the Quantum Espresso software to study the structural and electronic properties of silver icosahedral clusters with a bismuth atom by maintaining a similar percentage of bismuth in the existing nanoparticles (∼ 6%). The results indicate a decrease in the magnetization of the cluster with bismuth both in the lateral and central positions, going from 5 [μB] in the silver cluster to 3 [μB], and also to a distortion of the structure that leads to a doubling of energy levels, associated with a Jahn–Teller effect, when the bismuth atom was positioned in the lateral position, bringing the structure to a state of lower energy, which agrees with the experimental results. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe3O4_A and Fe3O4_B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe3O4_A and Fe3O4_B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures. [ABSTRACT FROM AUTHOR]

Steady-state, differential reaction rates for CO oxidation, with stoichiometric O2:CO feeds, were measured in the presence and absence of H2O over Pt catalysts supported on thin films of CeFeOx, FeOx, CeOx, and LaFeO3 that had been deposited on γ-Al2O3 by Atomic Layer Deposition. Prior to rate measurements, each catalyst was oxidized and reduced five times at 1073 K. Only the oxidized Pt/CeFeOx/γ-Al2O3 catalyst exhibited significantly increased CO oxidation rates upon the introduction of H2O. A relatively small deuterium isotope effect was observed and suggests that O–H bond cleavage is not involved in the kinetically relevant step of this process. The nature of the catalyst support, the pretreatment of the catalyst, and the effects of Pt particle size are discussed in relation to the observed H2O promotion of CO oxidation. [ABSTRACT FROM AUTHOR]

BACKGROUND: Recycling waste polyethylene terephthalate (PET) plastic can prolong its life cycle, reduce the consumption of non‐renewable resources, prevent plastic pollution and also protect the ecological environment. Traditional chemical recycling of waste PET plastic can depolymerize PET into terephthalic acid (TPA) and ethylene glycol (EG). RESULTS: A strategy was proposed to directly depolymerize PET into TPA and acid, which shows significantly higher values than conventional EG. This is achieved over a carbon nanotube (CNT)‐supported AuCu catalyst (AuCu/CNT) prepared using an evaporative impregnation method. Comprehensive analyses of the catalyst were conducted using X‐ray diffraction, N2 adsorption/desorption, transmission electron microscopy, X‐ray photoelectron spectroscopy, H2‐temperature=programmed reduction, and so on. It was found that Cu species in the AuCu/CNT catalyst mainly existed in the form of CuO, forming new reaction adsorption sites and providing active AuCu alloy sites, thereby improving the catalytic performance of AuCu/CNT. This results in simultaneously superior acid selectivity and EG conversion over AuCu/CNT compared with Au/CNT and Cu/CNT. CONCLUSION: This study indicates that the hydrolysis of PET is highly significant in preparing value‐added products. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The atomic-thick anticorrosion coating for copper (Cu) electrodes is essential for the miniaturisation in the semiconductor industry. Graphene has long been expected to be the ultimate anticorrosion material, however, its real anticorrosion performance is still under great controversy. Specifically, strong electronic couplings can limit the interfacial diffusion of corrosive molecules, whereas they can also promote the surficial galvanic corrosion. Here, we report the enhanced anticorrosion for Cu simply via a bilayer graphene coating, which provides protection for more than 5 years at room temperature and 1000 h at 200 °C. Such excellent anticorrosion is attributed to a nontrivial Janus-doping effect in bilayer graphene, where the heavily doped bottom layer forms a strong interaction with Cu to limit the interfacial diffusion, while the nearly charge neutral top layer behaves inertly to alleviate the galvanic corrosion. Our study will likely expand the application scenarios of Cu under various extreme operating conditions. Atomically thick anticorrosion coatings on Cu are desired for future applications, but still at its infancy. Here, the authors report a Janus-doping mechanism in bilayer graphene on Cu substrate that results in an enhanced anticorrosion performance. [ABSTRACT FROM AUTHOR]

In this article we shed light on newly emerging perspectives to characterize and understand the interplay of diffusive mass transport and surface catalytic processes in pores of gas phase metal catalysts. As a case study, nanoporous gold, as an interesting example exhibiting a well-defined pore structure and a high activity for total and partial oxidation reactions is considered. PFG NMR (pulsed field gradient nuclear magnetic resonance) measurements allowed here for a quantitative evaluation of gas diffusivities within the material. STEM (scanning transmission electron microscopy) tomography furthermore provided additional insight into the structural details of the pore system, helping to judge which of its features are most decisive for slowing down mass transport. Based on the quantitative knowledge about the diffusion coefficients inside a porous catalyst, it becomes possible to disentangle mass transport contributions form the measured reaction kinetics and to determine the kinetic rate constant of the underlying catalytic surface reaction. In addition, predictions can be made for an improved effectiveness of the catalyst, i.e., optimized conversion rates. This approach will be discussed at the example of low-temperature CO oxidation, efficiently catalysed by npAu at 30 °C. The case study shall reveal that novel porous materials exhibiting well-defined micro- and mesoscopic features and sufficient catalytic activity, in combination with modern techniques to evaluate diffusive transport, offer interesting new opportunities for an integral understanding of catalytic processes. [ABSTRACT FROM AUTHOR]

Despite the extremely high hardness of recently proposed high-entropy carbides (HECs), the low fracture toughness limits their applications in harsh mechanical environment. Here, we introduce a metastability engineering strategy to achieve superhard HECs with enhanced toughness via in-situ metastable particles. This is realized by developing a (WTaNbZrTi)C HEC showing a solid solution matrix with uniformly dispersed in-situ tetragonal and monoclinic ZrO2 particles. Apart from a high hardness of 21.0 GPa, the HEC can obtain an enhanced fracture toughness of 5.89 MPa·m1/2, significantly exceeding the value predicted by rule of mixture and that of other reported HECs. The toughening effect is primarily attributed to the transformation of the metastable tetragonal ZrO2 particles under mechanical loading, which promotes crack tip shielding mechanisms including crack deflection, crack bridging and crack branching. The work demonstrates the concept of using in-situ metastable particles for toughening bulk high-entropy ceramics by taking advantage of their compositional flexibility. High-entropy carbides (HECs) with high hardness usually suffer from low fracture toughness. Here, the authors demonstrate a metastability engineering strategy for toughening superhard HECs by introducing in situ metastable ceramic particles, which are transformable under mechanical loading. [ABSTRACT FROM AUTHOR]

As supported CuO is well-known for low temperature activity, CuO/CeO2 nanosphere catalysts were synthesized and tested for CO oxidation and preferential oxidation of CO (PROX) in excess H2. For the first reaction, ignition was observed at 95 °C, whereas selective PROX occurred in a temperature window from 50 to 100 °C. The catalytic performance was independent of the initial oxidation state of the catalyst (CuO vs. Cu0), suggesting that the same active phase is formed under reaction conditions. Density functional modeling was applied to elucidate the intermediate steps of CO oxidation, as well as those of the comparably less feasible H2 transformation. In the simulations, various Cu and vacancy sites were probed as reactive centers enabling specific pathways. [ABSTRACT FROM AUTHOR]

High-entropy materials, for both complexity in structure and superiority in performance, have been widely confirmed to be one possible kind of advanced electrocatalyst. Significant efforts have been dedicated to modeling the atomic-level details of high-entropy catalysts to improve the viability for bottom-up design of advanced electrocatalysts. In this review, first, we survey developments in various modeling methods that are based on density functional theory. We review progress in density functional theory simulations for emulating different high-entropy electrocatalysts. Then, we review the advancements in simulations of high-entropy materials for electrocatalytic applications. Finally, we present prospects in this field. Abbreviations: HEMs: high-entropy materials; CCMs: compositionally complex materials; DFT: density functional theory; LDA: local density approximation; GGA: generalized gradient approximation; VASP: Vienna Ab initio simulation package; ECP: effective core potential; PAW: projector-augmented wave potential; VCA: virtual crystal approximation; CPA: coherent potential approximation; SQS: special quasi-random structures; SSOS: small set of ordered structures; SLAE: similar local atomic environment; HEAs: high-entropy alloys; FCC: face-centered cubic; BCC: body-centered cubic; HCP: hexagonal close-packed; ORR: oxygen reduction reaction; OER: oxide evolution reaction; HER: hydrogen evolution reaction; RDS: rate-limiting step; AEM: adsorbate evolution mechanism; LOM: lattice oxygen oxidation mechanism; HEOs: high-entropy oxides; OVs: oxygen vacancies; PDOS: projected densities of states; ADR: ammonia decomposition reaction; NRR: nitrogen reduction reaction; CO2RR: CO2 reduction reaction; TMDC: transition metal dichalcogenide; TM: transition metal; AOR: alcohol oxidation reaction; GOR: glycerol oxidation reaction; UOR: urea oxidation reaction; HEI: high-entropy intermetallic. This paper reviews recent developments in the field of atomistic simulations of high-entropy electrocatalysts, one of emerging state-of-the-art catalytic materials. [ABSTRACT FROM AUTHOR]

Electrochemical conversion of nitrate to ammonia offers an efficient approach to reducing nitrate pollutants and a potential technology for low-temperature and low-pressure ammonia synthesis. However, the process is limited by multiple competing reactions and NO3− adsorption on cathode surfaces. Here, we report a Fe/Cu diatomic catalyst on holey nitrogen-doped graphene which exhibits high catalytic activities and selectivity for ammonia production. The catalyst enables a maximum ammonia Faradaic efficiency of 92.51% (−0.3 V(RHE)) and a high NH3 yield rate of 1.08 mmol h−1 mg−1 (at − 0.5 V(RHE)). Computational and theoretical analysis reveals that a relatively strong interaction between NO3− and Fe/Cu promotes the adsorption and discharge of NO3− anions. Nitrogen-oxygen bonds are also shown to be weakened due to the existence of hetero-atomic dual sites which lowers the overall reaction barriers. The dual-site and hetero-atom strategy in this work provides a flexible design for further catalyst development and expands the electrocatalytic techniques for nitrate reduction and ammonia synthesis. Nitrate electroreduction to ammonia can decrease pollutants and produce high-value ammonia. Here, the authors design a Fe/Cu diatomic catalyst on nitrogen-doped graphene, which exhibits high catalytic activities of and selectivity for ammonia. [ABSTRACT FROM AUTHOR]

Highlights: The state-of-the-art development in electrochemical H2O2 production via the two-electron oxygen reduction reaction is reviewed with emphasis on material science, reaction mechanisms, and fundamental factors that govern the reaction route. General principles and strategies for catalyst design are summarized to understand the inherent relationships between the catalyst properties and electrocatalytic performances. Perspectives and challenges are presented to get insights into the large-scale manufacturing of H2O2 via the electrochemical routes. An environmentally benign, sustainable, and cost-effective supply of H2O2 as a rapidly expanding consumption raw material is highly desired for chemical industries, medical treatment, and household disinfection. The electrocatalytic production route via electrochemical oxygen reduction reaction (ORR) offers a sustainable avenue for the on-site production of H2O2 from O2 and H2O. The most crucial and innovative part of such technology lies in the availability of suitable electrocatalysts that promote two-electron (2e–) ORR. In recent years, tremendous progress has been achieved in designing efficient, robust, and cost-effective catalyst materials, including noble metals and their alloys, metal-free carbon-based materials, single-atom catalysts, and molecular catalysts. Meanwhile, innovative cell designs have significantly advanced electrochemical applications at the industrial level. This review summarizes fundamental basics and recent advances in H2O2 production via 2e–-ORR, including catalyst design, mechanistic explorations, theoretical computations, experimental evaluations, and electrochemical cell designs. Perspectives on addressing remaining challenges are also presented with an emphasis on the large-scale synthesis of H2O2 via the electrochemical route. [ABSTRACT FROM AUTHOR]

Uncooled microbolometer arrays incorporating vertically aligned carbon nanotube absorbers are increasingly being adopted in satellite instrumentation for monitoring the Earth's radiation budget. A key requirement for such microbolometers is a thermistor having high-temperature coefficient of resistance (TCR), low 1/f noise while surviving high-temperature carbon nanotube growth at 800 °C. In the present work, Pt thin-film thermistors are fabricated on SiNx/SiO2/Si substrates using DC magnetron sputtering. To achieve enhanced adhesion of the Pt thin film, an interlayer of AlOx is deposited on the substrate via reactive high-power impulse magnetron sputtering. To maximize the positive TCR, the Pt/AlOx is subjected to different annealing conditions by varying temperature, time, and gaseous environment. With an increase in the annealing temperature and duration, Pt/AlOx thin film exhibits an improvement in TCR. Microstructural and morphological investigations suggest that improvement in TCR is related to the recrystallization of Pt and the resulting increase in grain size. A relatively high TCR of 0.308%/°C (TCR of bulk Pt = 0.359%/°C) was obtained at an operational range of 20–50 °C when Pt was annealed at 800 °C for 1 h and 3 h in air and Ar, respectively. Deposition of Pt without an AlOx interlayer resulted in thin film blistering and delamination when annealed at 800 °C for 1 h in air. A Pt/AlOx thermistor with a TCR of 0.308%/°C, annealed at 800 °C for 3 h in Ar has the potential for use in microbolometers that must undergo high-temperature growth of vertically aligned carbon nanotube absorbers. [ABSTRACT FROM AUTHOR]

The development of cost‐effective and highly efficient electrocatalysts to accelerate distinct electrochemical reactions is essential to help the industry to achieve a low‐carbon footprint. Single‐atom alloys (SAAs) with the characteristics of unique electronic structures, well‐defined active sites, and maximum atom utilization demonstrate promising potential to replace traditional noble metal catalysts. SAAs are expected to tailor the adsorption properties of reaction species, thus promoting electrocatalytic behaviors. Herein, representative synthetic strategies including wet chemistry, galvanic replacement, dealloying, and atomic layer deposition are introduced, followed by a summary of applications of SAAs in hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, carbon dioxide reduction reaction, and ethanol electro‐oxidation to provide an in‐depth understanding of the structure–activity relationship. Moreover, the challenges and perspectives in this emerging field of SAAs are discussed. [ABSTRACT FROM AUTHOR]

The manganese-promoted cobalt supported on gamma alumina was prepared following the incipient wetness impregnation method and employed as a catalyst for carbon dioxide reforming of methane. The addition of manganese has changed the physicochemical properties of the original material. The Co3O4 crystallite size was decreased from 12.14 to 8.66 nm, suggesting a higher metal dispersion. The activation of the catalyst precursor was more effective with less energy required. Notably, the carbon dioxide adsorption over the catalyst surface was greatly enhanced in the presence of 1 wt% manganese. Taken all together, the manganese-promoted catalyst showed an outperformance with higher activity and stability in comparison with the pristine. At 973 K, 1 atm, CH4/CO2 ratio of 1, the addition of 1 wt% Mn into 5Co/Al2O3 led to notable increases in CH4 (15.2%) and CO2 (17.68%) conversions. Also, the stability of Mn-promoted was much higher compared to pristine catalyst with only 0.3 wt% of undesirable coke formed. Meanwhile, it was 4.94 wt% in the case of 5Co/Al2O3 at the same operating condition. Furthermore, the role of carbon dioxide adsorption in carbon dioxide reforming of methane was investigated. At a proper combination of carbon dioxide activation and methane dissociation, the catalytic activity can be significantly enhanced with much lower activation energies for the feedstock gases. Nevertheless, when the adsorption of carbon dioxide was dominant, catalytic activity was much lower and the deactivation occurred rapidly. [ABSTRACT FROM AUTHOR]