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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics

Determining a process–structure–property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in the inverse direction are essential. Problems in materials design are often considered in the context of process–property linkage by bypassing the materials structure, or in the context of structure–property linkage as in microstructure-sensitive design problems. However, there is a lack of research effort in studying materials design problems in the context of process–structure linkage, which has a great implication in reverse engineering. In this paper, given a target microstructure, we propose an active learning high-throughput microstructure calibration framework to derive a set of processing parameters, which can produce an optimal microstructure that is statistically equivalent to the target microstructure. The proposed framework is formulated as a noisy multi-objective optimization problem, where each objective function measures a deterministic or statistical difference of the same microstructure descriptor between a candidate microstructure and a target microstructure. Furthermore, to significantly reduce the physical waiting wall-time, we enable the high-throughput feature of the microstructure calibration framework by adopting an asynchronously parallel Bayesian optimization to exploit high-performance computing resources. Case studies in additive manufacturing and grain growth are used to demonstrate the applicability of the proposed framework, where kinetic Monte Carlo (kMC) simulation is used as a forward predictive model, such that for a given target microstructure, the target processing parameters that produced this microstructure are successfully recovered.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multichannel Analysis of Surface Waves Accelerated (MASWAccelerated): Software for efficient surface wave inversion using MPI and GPUs

Multichannel Analysis of Surface Waves (MASW) is a technique frequently used in geotechnical engineering and engineering geophysics to infer 1D layered models of seismic shear wave velocities in the top tens to hundreds of meters of the subsurface. We aim to accelerate MASW calculations by capitalizing on modern computer hardware available in the workstations of most engineers: multiple cores and graphics processing units (GPUs). We propose new parallel and GPU accelerated algorithms for computing 1D MASW inversion, and provide software implementations in C using Message Passing Interface (MPI) and CUDA. These algorithms take advantage of sparsity that arises in the problem, and the work balance between processes considers typical data trends. We compare our methods to an existing open source Matlab MASW tool. Our serial C implementation achieves a 2x speedup over the Matlab software, and we continue to see improvements by parallelizing the problem with MPI. Here we see nearly perfect strong and weak scaling for uniform data, and improve strong scaling for realistic data by repartitioning the problem to process mapping. By utilizing GPUs available on most modern workstations, we observe an additional 1.3x speedup over the serial C implementation on the first use of the method. We typically repeatedly evaluate theoretical dispersion curves as part of an optimization procedure, and on the GPU the kernel can be cached for faster reuse on later runs. We observe a 3.2x speedup on the cached GPU runs compared to the serial C runs. This work is the first open-source parallel or GPU-accelerated software tool for MASW imaging, and should enable geotechnical engineers to fully utilize all computer hardware at their disposal.

58 GEOSCIENCES↗
DOE OSTI2021

Solving inverse problems in stochastic models using deep neural networks and adversarial training

Inverse problems associated with stochastic models constitute a significant portion of scientific and engineering applications. In such cases the unknown quantities are distributions. The applicability of traditional methods is limited because of their demanding assumptions or prohibitive computational consumption; for example, maximum likelihood methods require closed-form density functions, and Markov Chain Monte Carlo needs a large number of simulations. We propose a new method that estimates the unknown distribution by matching the statistical properties between observed and simulated random processes. We leverage the expressive power of neural networks to approximate the unknown distribution and use a discriminative neural network for computing the statistical discrepancies between the observed and simulated random processes. Here we demonstrated numerically that the proposed methods can estimate both the model parameters and learn complicated unknown distributions.

42 ENGINEERING↗
DOE OSTI2025

Influence of sizing concentration on strength, stiffness, and porosity in textile grade carbon fiber (TCF)-Epoxy composites: Revealing inverse trends

The effect of fiber sizing (i.e., surface treatment) concentration (0 %, 1.36 %, 1.52 %, 1.94 %, and 2.13 %) on the mechanical properties (tensile, flexural, interlaminar shear strength (ILSS), and low velocity impact) of textile grade carbon fiber (TCF)-epoxy composite is examined. An inverse relationship between the strength and stiffness of the composite is observed with increased sizing concentration. The root mean square (RMS) roughness of the fiber surface increased from 17.8 nm (unsized) to 22.7 nm with 2.13 % sizing concentration. It was found that the tensile strength increased by 131 % from 221.4 ± 18.5 MPa (unsized) to 510.8 ± 28.05 MPa (for 1.36 % sizing) and further by 155 %–563.7 ± 14.95 MPa at 2.13 % sizing. On the contrary, the stiffness is initially increased by 126 % from 33.52 ± 7.80 GPa (unsized) to 75.9 ± 3.21 GPa (for 1.36 % sizing) but reduced with further increase in the sizing concentration. A single fiber pull-out test is simulated using the finite element method to validate the reverse trend in strength and stiffness. The varying sizing concentration is simulated by introducing an interface of varying thickness between fibers and matrix. Simulation results confirm that a thicker interface, corresponding to a higher sizing concentration, decreases interfacial shear stress, enhancing material strength while decreasing stiffness. The reverse trend in strength and stiffness with the sizing concentration aligns with experimental observations. In conclusion, the present study emphasizes the importance of sizing concentration for mechanical properties and provide a design criterion for customized high-strength and high-stiffness applications.

Porosity↗
DOE OSTI2024

Capillary-Enhanced Two-Phase Micro-Cooler Using Copper-Inverse-Opal Wick with Silicon Microchannel Manifold for High-Heat-Flux Cooling Application

In this work, we demonstrate a two-phase capillary-fed boiling micro-cooler that consists of a ~ 25-..mu..m-thick copper inverse opal (CIO) porous wicking structure for high-heat-flux boiling and a silicon 3D-manifold for distributed liquid delivery and vapor extraction across a 0.5 cm x 0.5 cm heated area. At low inlet water mass flow rates of 1.5 to 1.9 g(min)-1, the micro-cooler displays nearly two-phase boiling with exit vapor quality ~ 1 and a high critical heat flux (CHF) of 253 to 320 W cm-2 with low superheat of ~ 10 degrees C resulting in a thermal resistance of boiling ~ 0.025 cm2 degrees C W-1 or heat transfer coefficient of 0.4 MW m-2 degrees C-1. For higher flow rates of 5, 10, and 15 g(min)-1, the micro-cooler exhibits a hybrid single-phase and two-phase cooling regime where the contribution of the sensible heat (single-phase) cooling is linearly added to that of the two-phase cooling. For the highest flow rate of 15 g(min)-1, the CHF is increased to ~ 500 W cm-2 resulting in an overall thermal resistance of ~ 0.18 cm2 degrees C W-1. However, the two-phase heat transfer effectiveness, which estimates the utilization level of the inlet mass flow rate for two-phase boiling, is reduced to ~ 0.11. To achieve the best cooling system performances, the micro-cooler must operate entirely within the two-phase boiling regime (exit vapor quality or two-phase heat transfer effectiveness ~ 1). Ideally, the "coolant" should be delivered near its saturation temperature (~ 100 degrees C for water), which provides significant advantages for the energy-efficient operation of data centers and power electronics. We present detailed analysis with Infrared and high speed camera images at various inlet flow rates and heat fluxes to understand complex heat transfer in the micro-cooler. Furthermore, a conjugate thermofluidic simulation model, which incorporates the physics of capillary-fed boiling in a porous copper layer, agrees well with the experimental data.

capillary-enhanced boiling↗
DOE OSTI2023

Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems

We explore using neural operators, or neural network representations of nonlinear maps between function spaces, to accelerate infinite-dimensional Bayesian inverse problems (BIPs) with models governed by nonlinear parametric partial differential equations (PDEs). Neural operators have gained significant attention in recent years for their ability to approximate the parameter-to-solution maps defined by PDEs using as training data solutions of PDEs at a limited number of parameter samples. The computational cost of BIPs can be drastically reduced if the large number of PDE solves required for posterior characterization are replaced with evaluations of trained neural operators. However, reducing error in the resulting BIP solutions via reducing the approximation error of the neural operators in training can be challenging and unreliable. We provide an a priori error bound result that implies certain BIPs can be ill-conditioned to the approximation error of neural operators, thus leading to inaccessible accuracy requirements in training. To reliably deploy neural operators in BIPs, we consider a strategy for enhancing the performance of neural operators: correcting the prediction of a trained neural operator by solving a linear variational problem based on the PDE residual. We show that a trained neural operator with error correction can achieve a quadratic reduction of its approximation error, all while retaining substantial computational speedups of posterior sampling when models are governed by highly nonlinear PDEs. The strategy is applied to two numerical examples of BIPs based on a nonlinear reaction–diffusion problem and deformation of hyperelastic materials. We demonstrate that posterior representations of the two BIPs produced using trained neural operators are greatly and consistently enhanced by error correction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Simulations and analysis tools for charge-exchange (d, 2 He) reactions in inverse kinematics with the AT-TPC

Charge-exchange (d, 2 He) reactions in inverse kinematics at intermediate energies are a very promising method to investigate the Gamow–Teller transition strength in unstable nuclei. A simulation and analysis software based on the attpcroot package was developed to study this type of reactions with the active-target time projection chamber (AT-TPC). The simulation routines provide a realistic detector response that can be used to understand and benchmark experimental data. Analysis tools and correction routines can be developed and tested from simulations in ATTPCROOT , because they are processed in the same way as the real data. In particular, we study the feasibility of using coincidences with beam-like particles to unambiguously identify the (d, 2 He) reaction channel, and to develop a kinematic fitting routine for future applications. More technically, the impact of space-charge effects in the track reconstruction, and a possible correction method are investigated in detail. Finally, this analysis and simulation package constitutes an essential part of the software development for the fast-beams program with the AT-TPC.

(d,2He)↗
DOE OSTI2020

First measurement in the Gamow window of a reaction for the γ-process in inverse kinematics: 76 Se(α,γ) 80 Kr

The p-nuclei are the few stable nuclei heavier than iron on the neutron-deficient side of the valley of stability that cannot be produced through astrophysical neutron-capture reactions. The limited experimental data on reactions through which the p-nuclei might be produced leaves the origin of their production largely unknown. This work presents the first cross section measurements of the 76 Se(α,γ) 80 Kr reaction. The rate of the time reversed reaction, 80 Kr(γ,α) 76 Se, is one of the most uncertain of possible reactions which can occur at the 80 Kr branching point on the ,-process photo-disintegration pathway. The reaction flow through 80 Kr will directly affect the final abundance of the p nuclide 78 Kr. Experimental cross sections at two astrophysically relevant energies are reported and compared to cross sections calculated using Hauser-Feshbach codes TALYS, NON-SMOKER, and SMARAGD. The success of these first (α,γ) cross section measurements performed in inverse kinematics in the energy region of the γ-process opens the door for future studies of reactions on radioactive γ-process nuclides.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Near-wall model for compressible turbulent boundary layers based on an inverse velocity transformation

In this work, a near-wall model, which couples the inverse of a recently developed compressible velocity transformation (Griffin et al., Proc. Natl Acad. Sci., vol. 118, 2021, p. 34) and an algebraic temperature–velocity relation, is developed for high-speed turbulent boundary layers. As input, the model requires the mean flow state at one wall-normal height in the inner layer of the boundary layer and at the boundary-layer edge. As output, the model can predict mean temperature and velocity profiles across the entire inner layer, as well as the wall shear stress and heat flux. The model is tested in an a priori sense using a wide database of direct numerical simulation high-Mach-number turbulent channel flows, pipe flows and boundary layers (48 cases, with edge Mach numbers in the range 0.77–11, and semi-local friction Reynolds numbers in the range 170–5700). The present model is significantly more accurate than the classical ordinary differential equation (ODE) model for all cases tested. The model is deployed as a wall model for large-eddy simulations in channel flows with bulk Mach numbers in the range 0.7–4 and friction Reynolds numbers in the range 320–1800. When compared to the classical framework, in the a posteriori sense, the present method greatly improves the predicted heat flux, wall stress, and temperature and velocity profiles, especially in cases with strong heat transfer. In addition, the present model solves one ODE instead of two, and has a computational cost and implementation complexity similar to that of the commonly used ODE model.

42 ENGINEERING↗
DOE OSTI2025

Effects of the Inverse Trans-Influence in a Berkelium(III) Phosphine Oxide Complex

An example of a 249 Bk 3+ phosphine oxide complex has been prepared to examine and quantify the effects of the inverse trans-influence (ITI) on a late actinide complex. This has been accomplished through a comparison of cis and trans Bk-ligand bonds in the meridional berkelium(III) complex, BkBr 3 (OPCy 3 ) 3 (OPCy 3 = tricyclohexylphosphine oxide). A detailed bond metric analysis was completed that includes the shortest published distance for a Bk 3+ −O bond, attributed to the smaller coordination number of Bk 3+ of six in mer-BkBr 3 (OPCy 3 ) 3 . ITI calculations of the trans Bk 3+ −O bond provide an ITI value (98.5(2)%) comparable to that of mer-AmBr 3 (OPCy 3 ) 3 (98.2(2)%) and indicate a small 5f orbital contribution to bonding. This effect is discussed in the context of the isomorphous lanthanide(III) series where the ITI is calculated to be higher for mer-BkBr 3 (OPCy 3 ) 3 than in most Ln 3+ analogues.

Actinides↗
DOE OSTI2025

RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design

Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural constraints. This work presents RLMolLM, a reinforcement learning framework combining Proximal Policy Optimization (PPO) with genetic algorithms to address these limitations. Our approach optimizes multiple user-specified properties including quantitative estimates of drug-likeness (QED), synthetic accessibility (SA), and ADMET (absorption, distribution, metabolism, excretion, and toxicity) endpoints without requiring complete model retraining, while maintaining capability for scaffold-constrained generation where specific substructures must be preserved. We outperform state-of-the-art methods for molecular optimization, achieving best QED scores across GDB13, Moses, and Zinc datasets with up to 31% improvement over previous methods while maintaining excellent validity, uniqueness, and novelty metrics. For simultaneous multi-property optimization, our framework achieves substantial improvements in ADMET properties including 4.5-fold reduction in hERG toxicity and enhanced Caco-2 permeability compared to Moses dataset. Under structural constraints, the framework significantly improves molecular validity while preserving scaffolds and effectively optimizing properties. In conclusion, this versatile solution advances pharmaceutical and materials molecular design through effective integration of reinforcement learning and genetic algorithms with multi-property optimization and scaffold preservation.

Genetic algorithms↗
DOE OSTI2024

Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment

Methodologies for training machine learning potentials (MLPs) with quantum-mechanical simulation data have recently seen tremendous progress. Experimental data have a very different character than simulated data, and most MLP training procedures cannot be easily adapted to incorporate both types of data into the training process. Here, we investigate a training procedure based on iterative Boltzmann inversion that produces a pair potential correction to an existing MLP using equilibrium radial distribution function data. By applying these corrections to an MLP for pure aluminum based on density functional theory, we observe that the resulting model largely addresses previous overstructuring in the melt phase. Interestingly, the corrected MLP also exhibits improved performance in predicting experimental diffusion constants, which are not included in the training procedure. The presented method does not require autodifferentiating through a molecular dynamics solver and does not make assumptions about the MLP architecture. Our results suggest a practical framework for incorporating experimental data into machine learning models to improve the accuracy of molecular dynamics simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Efficient Mixed-Precision Matrix Factorization of the Inverse Overlap Matrix in Electronic Structure Calculations with AI-Hardware and GPUs

In recent years, a new kind of accelerated hardware has gained popularity in the artificial intelligence (AI) community which enables extremely high-performance tensor contractions in reduced precision for deep neural network calculations. In this article, we exploit Nvidia Tensor cores, a prototypical example of such AI-hardware, to develop a mixed precision approach for computing a dense matrix factorization of the inverse overlap matrix in electronic structure theory, S –1 . This factorization of S –1 , written as ZZT = S –1 , is used to transform the general matrix eigenvalue problem into a standard matrix eigenvalue problem. Here we present a mixed precision iterative refinement algorithm where Z is given recursively using matrix–matrix multiplications and can be computed with high performance on Tensor cores. To understand the performance and accuracy of Tensor cores, comparisons are made to GPU-only implementations in single and double precision. Additionally, we propose a nonparametric stopping criteria which is robust in the face of lower precision floating point operations. The algorithm is particularly useful when we have a good initial guess to Z, for example, from previous time steps in quantum-mechanical molecular dynamics simulations or from a previous iteration in a geometry optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Preparation and Structural Characterization of ZrO 2 /CuO x /Cu(111) Inverse Model Catalysts

CO 2 hydrogenation to methanol is regarded as a promising reaction to catalytically convert a major greenhouse gas (CO 2 ) into a value-added product (methanol). In the current study, scanning tunneling microscopy (STM) and X-ray photoelectron spectroscopy (XPS) were applied to investigate the growth mode of low coverages (<0.2 ML) of ZrO 2 in an inverse ZrO 2 /CuOx/Cu(111) system, which has the potential to achieve a high selectivity for a direct CO 2 to methanol transformation. It was found that the morphology of ZrO 2 was strongly affected by the preparation method. The ZrO 2 /CuO x /Cu(111) model catalyst prepared by the oxidation at 600 K of Zr pre-deposited on Cu(111) exhibited substantial mixing of ZrO 2 and CuO x . In contrast, the direct deposition of Zr under an O 2 ambient over CuO x /Cu(111) at 600 K produced small ZrO 2 islands (10-12 nm in size) with a two-dimensional structure (i.e. only one layer of ZrO 2 ). XPS studies indicate that both preparation methods lead to ZrO 2 /CuO x /Cu(111) surfaces. The model catalyst prepared by the direct deposition of Zr in O 2 was annealed up to 700 K in ultra-high vacuum. Both STM and XPS results suggest no apparent change in ZrO 2 , while CuO x was reduced at such annealing conditions. The island size of 10-12 nm observed for ZrO 2 on Cu(111) is much smaller than island sizes seen for CeO 2 (30-50 nm) and ZnO (300-500 nm) on the same substrate, opening the possibility for unique chemical properties

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Reactivity of a Zirconia–Copper Inverse Catalyst for CO 2 Hydrogenation

Copper–zirconia catalysts have been shown to be effective for methanol synthesis via CO 2 hydrogenation, yet the active phases and reaction mechanism remain uncertain. Here, an inverse model catalyst ZrO 2 /CuO 2 /Cu(111) was prepared by mass-selected ion deposition and tested for CO 2 hydrogenation under near-ambient pressure (AP) reaction conditions by using X-ray photoelectron spectroscopy (NAP-XPS) and infrared reflection–absorption spectroscopy (NAP-IRAS). The spatial resolution afforded by the small entrance cone of the AP-XPS spectrometer was used to resolve regions of the surface with and without Zr deposition. Carbon 1s core level spectra of the ZrO 2 /Cu 2 O/Cu(111) regions of the surface under 500 mTorr of CO 2 + H 2 (1:3 ratio) show evidence for reaction intermediates including carbonate (CO 3 *), formate (HCOO*), and H x CO* species, with methoxy having the highest surface concentration at 500–600 K. These intermediates are confirmed by IRAS vibrational spectra. In regions of the surface without Zr, the Cu 2 O/Cu(111) is reduced to metallic Cu, and the surface intermediates are different and are present at much lower concentrations. The observed surface intermediates and their temperature dependence suggest a mechanism in which CO 2 is adsorbed on zirconia as carbonate (CO 3 *) and then converted to HCOO* and H x CO* hydrogenated intermediates that ultimately lead to methoxy (CH 3 O*), the final surface-bound precursor for methanol. Overall, the results clearly demonstrate the promotional effects of small ZrO 2 particles for enhancing the reactivity of Cu surfaces for CO 2 hydrogenation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Inverse Design of a Graphene-Based Quantum Transducer via Neuroevolution

Here we introduce an inverse design framework based on artificial neural networks, genetic algorithms, and tight-binding calculations, capable to optimize the very large configuration space of nanoelectronic devices. Our non-linear optimization procedure operates on trial Hamiltonians through superoperators controlling growth policies of regions of distinct doping. We demonstrate that our algorithm optimizes the doping of graphene-based three-terminal devices for valleytronics applications, monotonously converging to synthesizable devices with high merit functions in a few thousand evaluations (out of similar or equal to ≃ 2 3800 possible configurations). The best-performing device allowed for a terminal-specific separation of valley currents with similar or equal to ≃ 96% (≃ 94%) K(K') valley purity. Importantly, the devices found through our non-linear optimization procedure have both a higher merit function and higher robustness to defects than the ones obtained through geometry optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Structural, Electrical, and Electromechanical Properties of Inverse Hybrid Perovskites from First-Principles: The Case of (CH 3 NH 3 ) 3 OI

Inverse-hybrid perovskites (IHPs) with large polarization have recently been predicted from first-principles computations. Here, we use one representative from the IHP class of materials, (CH 3 NH 3 ) 3 OI (MA3OI), to propose a route to the first-principles prediction of structural and electrical properties, such as polarization, polarization reversibility, and the associated coercive field for hybrid organic–inorganic perovskites. The route relies on the construction of the polarization reversal path that models experimental measurements. Such a path was found to play an important role in the ground-state search as well as in the identification of competing structural variations. The latter is believed to be the origin of the structural disorder that is characteristic of hybrid organic–inorganic perovskites. The application of such an approach to MA3OI leads to the prediction of several structural variations that are expected to result in a structurally disordered phase above 766 K and of the polar ground state with a polarization of 25.3 μC/cm 2 that is reversible with the application of an electric field. The upper estimate for the coercive field associated with homogeneous polarization reversal is 6.9 GV/m. The piezoelectric constants of MA3OI are predicted to be an order of magnitude smaller in comparison with a prototypical inorganic ferroelectric PbTiO 3 ; however, the low symmetry of the MA3OI structure yields finite values for all components of the piezoelectric tensor. The polarization in MA3OI is tunable by the epitaxial strain (11.5% change under 5% epitaxial strain), although less so as compared with PbTiO 3 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Understanding the Surface Structure and Catalytic Activity of SnO x /Au(111) Inverse Catalysts for CO 2 and H 2 Activation

Carbon dioxide hydrogenation is a promising approach for the reduction of greenhouse gas pollution via the production of fuels and high-value chemicals utilizing C1 chemistry. In this process, the activation of nonpolar molecules, CO 2 and H 2 , at mild conditions is challenging. In this study, we report a well-defined inverse SnO x /Au(111) catalyst that shows the ability to activate both CO 2 and H 2 at room temperature. Scanning tunneling microscopy (STM) and ambient pressure X-ray photoemission spectroscopy (AP-XPS) are combined to understand the surface structure, growth mode, chemical state, and activity of SnO x /Au(111) surfaces. Nanostructures of SnO x at the sub-monolayer level were prepared by depositing Sn on Au(111) followed by O 2 oxidation. For the as-prepared SnO x /Au(111), two-dimensionally formed SnO x thin films on a Au(111) substrate were observed with STM of two different moieties, discernible based on their height: clusters (~0.4 Å) and nanoparticles (NPs, 1–2.5 Å), which are assigned to Sn–Au alloys and SnO x , respectively, in corroboration with XPS analysis. Furthermore, SnO x /Au(111) was annealed under UHV to test its thermal stability. Upon annealing at 400–600 K, a disappearance of SnO x NPs and reappearance of highly dispersed Sn clusters were clearly noticeable from the STM and XPS results, identifying the thermal decomposition of SnO x and subsequent formation of Sn–Au alloys on the surface due to the recombination of Sn clusters with Au. We investigated the reactivity of the SnO x /Au(111) surfaces toward CH 4 , CO 2 , and H 2 . The SnO x /Au(111) surfaces have excellent CO 2 and H 2 activation abilities even at room temperature with negligible reactivity for methane activation. Our AP-XPS results show that H 2 can be activated on the SnO x NPs by the reduction to Sn. For CO 2 , the activation and further dissociation are identified by a reoxidation of Sn with newly formed Sn–O bonds and the formation of surface carbon. Therefore, we propose that SnO x is a potential catalyst or additive to achieve CO 2 hydrogenation under mild conditions.

36 MATERIALS SCIENCE↗