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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Development and Implementation of a New AI-Based Tool to Support Fast Reactor Software Model Generation and Validation

This report summarizes FY26 work to develop Maggie, an artificial intelligence-based assistant designed to support software model generation and validation activities for fast reactor analysis codes. The project established a modular, code-agnostic software architecture that separates reusable agent capabilities from code-specific knowledge and tools, with initial implementation focused on the FRP-supported fast reactor safety analysis code SAS4A/SASSYS1 (SAS). A curated SAS-specific knowledge base was assembled from the code manual, training materials, historical analysis reports, and representative input files, and was integrated through retrieval-augmented generation to ground Maggie’s responses in authoritative sources. Maggie was deployed on the internal Argonne network, where it demonstrated practical user-facing capability as a chatbot for answering natural language questions about SAS and retrieving relevant technical information. Demonstration cases also showed that Maggie can generate useful snippets of SAS input for selected modeling tasks, while highlighting current limitations in reliability and consistency for more complex input generation tasks. Overall, the FY26 effort established the technical foundation for an AI-assisted capability intended to improve the efficiency, consistency, and accessibility of fast reactor software model development at Argonne and, with further improvements, to support eventual use by the broader fast reactor community, including industry users of FRP-supported analysis tools.

Thomas, Rachel [Argonne National Laboratory (ANL),↗
DOE OSTI2025

An evolving Coupled Model Intercomparison Project phase 7 (CMIP7) and Fast Track in support of future climate assessment

The Coupled Model Intercomparison Project (CMIP) coordinates community-based efforts to answer key and timely climate science questions, facilitate delivery of relevant multi-model simulations through shared infrastructure, and support national and international climate assessments. Generations of CMIP have evolved through extensive community engagement from punctuated phasing into more continuous support for the design of experimental protocols, infrastructure for data publication and access, and public delivery of climate information. We identify four fundamental research questions motivating a seventh phase of coupled model intercomparison relating to patterns of sea surface temperature change, changing weather, the water–carbon–climate nexus, and tipping points. Key CMIP7 advances include an expansion of baseline experiments, a focus on CO 2 -emissions-driven experiments, sustained support for community MIPs, periodic updating of historical forcings and diagnostics requests, and a collection of prioritized experiments, or the “Assessment Fast Track”, drawn from community MIPs to support climate research, assessment, and service goals across prediction and projection, characterization, attribution, and process understanding.

Environmental sciences↗
DOE OSTI2025

Basin-Scale Structural Features Database: Spatial Datasets to Support Carbon Storage Resource Assessments

Presentation slides on "Basin-Scale Structural Features Database: Spatial Datasets to Support Carbon Storage Resource Assessments" for CCUS 2025 Annual Meeting. The Basin-Scale Structural Features database contains a series of basin-scale spatial datasets representing structural features, including faults, fractures, folds, and earthquakes. Designed to support carbon storage feasibility and resources assessments for Carbon Capture and Storage (CCS) projects, the database leverages publicly available data resources from authoritative sources (e.g. US Geological Survey, State Geologic Surveys), and aims to help users better understand basin-scale structural features, as well as potential data gaps in areas with sparse information.

basin scale↗
DOE OSTI2026

Energy, power, and infrastructure demands from electrifying airport ground support equipment at United States airports

As the airline industry seeks to reduce costs and transition to clean energy, electric ground support equipment is emerging as a favorable option. The integration of electric ground support equipment into airport operations requires careful planning for vehicle deployment, charging infrastructure, and grid impacts. We develop a flexible, bottom-up modeling framework to assess energy and infrastructure needs across more than 300 U.S. airports. Our analysis estimates site-specific power and energy demand, equipment counts by type, charger requirements, and costs. Here we quantify the magnitude of new electrical loads created by the electrification of airport ground support equipment, finding that peak power demand at the largest airports can reach up to 20 megawatts, with annual electricity consumption approaching 51,000 megawatt-hours. We further show that behind-the-meter battery energy storage systems and solar photovoltaic systems can reduce peak load and lower total system costs by as much as 10 million dollars.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Optimization-Based Dynamic Voltage Support of Microgrids Using Energy Storage Systems

A microgrid network is characterized by a high R/X ratio, making the voltage more sensitive to active power changes compared to bulk power systems, where the voltage is regulated primarily by reactive power. Due to its sensitivity, voltage control approaches for microgrids should also consider the active power input coupling, making it very different from conventional power systems. Additionally, as the energy costs associated with active and reactive powers are different and the operational conditions of microgrids connected to active distribution systems vary over time, the ideal controller to provide voltage support must be flexible enough to handle these technical and operational constraints. This paper proposes a model predictive control approach to provide dynamic voltage support using energy storage systems. This approach uses a simplified predictive model of the system to solve the model predictive control problem. By proper selection of model predictive control weighting parameters, the quality of service provided can be adjusted to achieve the desired performance. A simulation study in MATLAB/Simulink validates the proposed approach for the Cordova, Alaska microgrid. Results show that the performance of the voltage support can be adjusted depending on the choice of weight and constraints of the controller.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Influence of Mo- and Ga-Supported HZSM-5 Co-Catalyst Configuration in Microwave-Assisted Methane and Ethane Dehydroaromatization

Microwave (MW)-assisted dehydroaromatization (DHA) using an Mo-supported HZSM-5 catalyst (Mo/HZSM-5) enhances the value of natural gas resources by converting stranded or underutilized natural gas into value-added chemicals in modular microwave reactor systems. This approach offers the potential to generate economic value. However, natural gas mixtures often contain multiple components, including ethane (C2H6) and propane (C3H8), which complicate the reaction pathways. Ga-supported HZSM-5 (Ga/HZSM-5) catalysts are generally inactive toward CH4 but exhibit higher activity toward C2H6 and C3H8. Therefore, investigating the combination of Mo/HZSM-5 and Ga/HZSM-5 in various catalyst bed configurations is essential. This study explores different co-catalyst bed configurations and CH4/C2H6 feed compositions to determine the most effective way for enhanced natural gas conversion and benzene production.

cocatalyst bed configuration↗
DOE OSTI2025

Constructing Highly Durable Fuel Cell Catalysts Through Integrating Graphitic Shell‐Protected Composite Carbon Support with Gaseous Co Deposition‐Driven PtCo Intermetallics

Metal dissolution, nanoparticle agglomeration, and carbon support corrosion cause significant performance degradation of current PtCo catalysts under acidic and oxidative oxygen reduction reaction. Here, in this study, an integrated strategy is presented to design high-performance Pt 3 Co intermetallic catalysts by regulating gaseous Co deposition-driven diffusion into Pt nanoparticles supported on a composite carbon. The composite carbon is derived from ZIF-8/polyaniline, consisting of a high-surface-area (HSC) core and a protective graphitic shell (GS), which is employed to design 40 wt.% Pt/HSC@GS catalyst. The corresponding membrane electrode assemblies (MEAs) can maintain 1.09 A cm −2 at 0.7 V (20. 7% loss) after 10 000 cycles (1.0–1.5 V for carbon stability) and 1.15 A cm −2 (16.1% loss) after 150 000 cycles (0.60–0.95 V for catalyst stability). A ordered Pt 3 Co intermetallic is synthesized via a gaseous Co-deposition process, which yields a homogeneous Co-rich layer onto the Pt nanoparticles on the composite support, thereby facilitating Co diffusion into Pt crystal during subsequent ordering annealing to form the ordered intermetallic structure. This gaseous deposition leads to a thin carbon layer on PtCo nanoparticles, inhibiting particle growth during the annealing and mitigating Co dissolution under dynamic electrochemical conditions. The PtCo catalyst achieves impressive MEA performance and long-term durability (1.45 A cm −2 at 0.7 V after 120 000 cycles) under heavy-duty conditions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Supported Single‐Atom Manganese Catalysts for the Trimerization of Ethylene

Selective ethylene oligomerization via oxidative cyclization, forming metallacyclic intermediates, is typically catalyzed by molecular titanium and chromium complexes to produce butenes, hexenes, or octenes, depending on the supporting ligand framework. However, this mechanism requires significant electron density at the metal active site and is not known to be generalizable to other first-row transition metals. In this work, we computationally investigate the electronic modulation of five transition metals (Mn, Fe, Co, Ni, and Cu) supported on titania (TiO₂) through reductive lithium intercalation to promote selective oligomerization via oxidative cyclization, using density functional theory (DFT). Our findings predict that Mn/LiTiO₂ exhibits high catalytic activity due to the exergonic nature of oxidative cyclization with two ethylene molecules. Additionally, lithium titanate (LiTiO₂) supports enhance catalytic performance compared to TiO₂. Experimental validation confirms that Mn/LiTiO₂ achieves higher conversion rates and improved selectivity toward hexene (C₄:C₆ = 1:2.6). The enhanced activity is attributed to lithiation, which alters the electronic environment around Mn active sites. Mechanistic studies reveal that the formation of a seven-membered ring, a key intermediate for hexene formation, is more favorable on LiTiO₂ than TiO₂. This work provides the first evidence of Mn catalyzing selective ethylene oligomerization via oxidative cyclization in either homogeneous or heterogeneous catalysis.

Kim, Yu Lim [Argonne National Laboratory (ANL), Ar↗
DOE OSTI2026

Influence of Polymer and Support Interactions on the Electrocatalytic Properties of Heterogenized Molecular Complexes

Heterogenized molecular catalysts have been the subject of increasing interest in the context of electrochemical small molecule activation due to the possibility of active site homogeneity and tunability, which can surpass many nanostructured materials. When molecular catalysts strongly interact with carbon supports through π–π interactions, they tend to show higher selectivity and activity during heterogeneous electrochemical reactions. Indeed, ligand modifications in the secondary sphere that allow for more π–π overlap between catalyst and carbon support often show improved performance. Interactions between catalyst and polymer binder are also a possible modulus of control in these reactions, either through coordinating moieties that interact with active sites or through control over substrate or product transport. Finally, the covalent linking of molecular complexes onto carbon materials has also been shown to result in high activity and stability. Recent advances in the use of carbon supports, both through covalent and non-covalent interactions, and polymers to alter small molecule activation by heterogenized molecular catalysts are summarized in this Perspective.

Johnson, Elizabeth K. [University of Virginia, Cha↗
DOE OSTI2024

Bringing HPE Slingshot 11 support to Open MPI

The Cray HPE Slingshot 11 network is used on the new exascale systems arriving at the U.S. Department of Energy (DoE) laboratories (e.g., Frontier, Aurora, Perlmutter). As such, the support of this network is an important capability to meet the needs of exascale applications. Here, this article highlights recent work to develop supporting infrastructure to enable Open MPI to efficiently support these new platforms. A key component of this effort involves development of a new Open Fabrics Interface (OFI) provider, LinkX. We discuss the design and development of enhancements that take advantage of the new Slingshot 11 network and AMD GPUs. We include performance data from tests on the Frontier supercomputer using synthetic communication benchmarks, and the vendor provided MPI as a baseline for comparison. The tests demonstrate full functionality of Open MPI on the system and initial results show favorable performance when compared to the highly tuned vendor implementation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Tuning transition metal nanoparticles on a non-traditional support via experimental design

The ability to control metal nanoparticle size and morphology on supported catalysts is crucial for optimizing catalytic performance in targeted applications. Here, this work presents a systematic approach for tuning Ni particle and crystallite size on an unconventional, low-porosity silica fume support through select thermal treatments. The catalyst was synthesized via the deposition of nickelocene onto silica fume, resulting in well-dispersed Ni nanoparticles. A face-centered central composite design was employed to systematically assess the effects of time, temperature, and sintering gas environment on metal particle growth. The results demonstrate that the sintering gas environment is the primary factor governing particle and crystallite evolution, with temperature as the next most significant influence. Nickel nanoparticles sintered at temperatures of 650 °C and above under inert conditions exhibited substantial growth and polycrystalline structures, whereas samples treated in oxidative environments formed NiO, restricting particle mobility. Minimally oxidative (500 ppm O₂) environments facilitated rapid sintering while effectively removing residual ligands from the one-step nickelocene deposition process. Extensive structural characterization via a combination of scanning transmission electron microscopy, X-ray diffraction, hydrogen temperature programmed reduction, and small-angle X-ray scattering revealed that oxidative treatments enhanced metal-support interactions, as evidenced by increased reduction temperatures and narrower particle size distributions. These findings establish quantitative relationships between sintering parameters and Ni nanoparticle characteristics, providing a framework for rational catalyst design through controlled thermal treatments. This methodology is broadly applicable to other catalytic systems and provides a quantitative foundation for catalyst design.

CVD↗
DOE OSTI2026

Assessing metal nitrides and metal carbides as supports for thermally stable single-atom catalysts

Single-atom catalysts supported on metal oxides have been demonstrated to exhibit exceptional activity while also maintaining single-atom stability. However, alternative supports such as metal nitrides and carbides have received far less attention. Herein, we use density functional theory to systematically investigate the relative thermal stability of single-atom catalysts over a host of transition metal nitride and carbide supports. By considering the binding and dimerization energies of isolated transition metal atoms across various surface facets, we identify transition metal/support pairs that show the most promise for high-density single-atom catalysts. We find that transition metal atoms can be stabilized on both defect sites and pristine surfaces over transition metal nitrides and carbides. Furthermore, we identify promising metal/support pairings that may be suitable for achieving both stable and high-density single-atom catalysts. Furthermore, these results provide valuable insights to guide synthesis efforts towards achieving stable single-atom transition metal catalysts.

Density functional theory↗
DOE OSTI2025

Plastic-waste hydrogenolysis over two-dimensional MXene-supported ruthenium catalysts with tunable interlayer spacing

The hydrogenolysis of plastics is limited by active-site inaccessibility and inefficient mass transport of bulky polymer chains. To overcome these challenges, this work developed two-dimensional MXene-supported Ru (Ru@MXene) catalysts. Lyophilization of a solution containing dispersed MXene sheets and Ru precursors enabled the confinement of Ru species within the MXene interlayers, which act as pillars to expand the interlayer spacing. Building on this, a silica-pillared MXene-supported Ru (Ru@P-MXene) with even larger interlayer spacing exhibited a reaction rate of 914.9 g C5–C35 g Ru –1 h –1 for the hydrogenolysis of low-density polyethylene (LDPE) into valuable liquid chemicals (e.g., C 5 –C 35 ). A comparison of product yields between Ru@P-MXene and Ru@MXene suggests that elongated Ru particles confined within the MXene support expose their side facets for the reaction. In conclusion, this work demonstrates a new application of MXene in thermochemical catalysis, offering a solution to the challenges of active-site accessibility, mass transport, and reaction confinement in chemical plastic upcycling.

2D materials↗
DOE OSTI2025

Palm oil deoxygenation with glycerol as a hydrogen donor for renewable fuel production using nickel-molybdenum catalysts: The effect of support

Palm oil, one of the most widely used vegetable oils, offers significant potential as a sustainable feedstock for biofuel production. This study explores the deoxygenation of palm oil using glycerol as a hydrogen donor, with nickel-molybdenum (NiMo) catalysts supported on commercial alumina (Al 2 O 3 ), and zeolite (HZSM-5) comparing with self-prepared zirconia (ZrO 2 ). The catalysts were synthesized via incipient wetness impregnation and evaluated for their performance in biofuel production. NiMo/Al 2 O 3 exhibited the lowest oxygen removal efficiency (68.5 %), while NiMo/HZSM-5 achieved a higher oxygen removal (74.3 %) but also demonstrated the highest coke formation. The type of support material influenced the resulting biofuel range, with NiMo/HZSM-5 and NiMo/ZrO 2 favoring jet fuel production, whereas NiMo/Al 2 O 3 was more suitable for diesel production. Notably, NiMo/ZrO 2 exhibited the highest performance in palm oil deoxygenation while minimizing coke formation. These findings highlight NiMo/ZrO 2 as a promising catalyst for efficient and stable biofuel production, with the support material significantly influencing product yield and fuel quality.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particles

Accurate and efficient characterization of nanoparticles (NPs), particularly regarding particle size distribution, is essential for advancing our understanding of their structure-property relationship and facilitating their design for various applications. In this study, we introduce a novel two-stage artificial intelligence (AI)-driven workflow for NP analysis that leverages prompt engineering techniques from state-of-the-art single-stage object detection and large-scale vision transformer (ViT) architectures. This methodology is applied to transmission electron microscopy (TEM) and scanning TEM (STEM) images of heterogeneous catalysts, enabling high-resolution, high-throughput analysis of particle size distributions for supported metal catalyst NPs. The model's performance in detecting and segmenting NPs is validated across diverse heterogeneous catalyst systems, including various metals (Ru, Cu, PtCo, and Pt), supports (silica (SiO 2 ), γ-alumina (γ-Al 2 O 3 ), and carbon black), and particle diameter size distributions with mean and standard deviations ranging from 1.6 ± 0.2 nm to 9.7 ± 4.6 nm. The proposed machine learning (ML) methodology achieved an average F1 overlap score of 0.91 ± 0.01 and demonstrated the ability to disentangle overlapping NPs anchored on catalytic support materials. The segmentation accuracy is further validated using the Hausdorff distance and robust Hausdorff distance metrics, with the 90th percent of the robust Hausdorff distance showing errors within 0.4 ± 0.1 nm to 1.4 ± 0.6 nm. In conclusion, our AI-assisted NP analysis workflow demonstrates robust generalization across diverse datasets and can be readily applied to similar NP segmentation tasks without requiring costly model retraining.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Design, Synthesis, and Evaluation of Noble Metal Nanoparticles and In Situ-Decorated Carbon-Supported Nanoparticle Electrocatalysts Using Hypergolic Reactions

Here, we report the first synthesis of metal nanoparticles and supported metal nanoparticles on carbon by using hypergolic reactions. Specifically, we report the synthesis of noble metal nanoparticles (Pt, Ag, and Au) using sodium hydride (NaH) as both an ignition trigger and a reducing agent for the corresponding metal salt precursors. In addition, we report the one-step, in situ synthesis of Pt nanoparticles supported on carbon by adding sucrose as the carbon source. The hypergolically synthesized nanoparticles display elliptical morphology and are more crystalline compared with those conventionally synthesized in solution using sodium borohydride (NaBH 4 ). When tested as electrocatalysts, the hypergolic Pt nanoparticles exhibit more than 2 times higher specific electrochemical active surface area (ECSA) and a higher half-wave potential (E 1/2 ) of 0.94 V vs the reversible hydrogen electrode (RHE) compared to the conventionally synthesized ones. In addition, the electrocatalyst based on the in situ synthesized carbon that was decorated with the Pt nanoparticles synthesized hypergolically outperforms an analogous, state of the art, commercial PtC system. For example, the former shows an attractive E 1/2 (0.94 V) compared with 0.9 V for the commercial PtC. Accelerated durability tests (ADT) in an alkaline environment add another advantage. After 10 000 cycles, the hypergolically synthesized system shows a smaller reduction of E 1/2 and less degradation compared to the commercial PtC (10 mV compared to ∼30 mV). The work described here represents the first reported synthesis using hypergolic reactions of metal nanoparticles as well as supported metal nanoparticles. The properties of the resulting electrocatalysts demonstrate the versatility and promise of the new approach in materials synthesis and open new avenues for further investigation as electrocatalysts.

Chalmpes, Nikolaos↗
DOE OSTI2025

Fundamental Interactions of Bimetallic Cu x Pd y ( x + y = 4) Clusters Supported on the α-WC(0001) Surface and Their Performance for CO 2 Adsorption and Dissociation

The tungsten carbide α-WC(0001) surface, an active system for the activation of H 2 and important hydrogenation processes involving unsaturated hydrocarbons, can serve as a support of bimetallic clusters to produce materials with unique catalytic properties, opening routes for a wide range of technical applications. In particular, Cu x Pd y clusters are of particular interest because they combine metals with different properties. A stochastic method was applied to obtain the geometry of Cu x Pd y (x + y = 4) bare clusters, evaluating thousands of possibilities to obtain stable structures, yielding one isomer for Cu 4 , Cu 2 Pd 2 , Cu 1 Pd 3 , and Pd 4 and two isomers for Cu 3 Pd 1 . These clusters were supported on C and W terminations of the tungsten carbide (0001) surface, exploring all of the binding possibilities. The adsorption energies on the C and W terminations are in the ranges from −2.51 to −3.02 eV and from −2.26 to −3.30 eV, respectively. The strongest and weakest binding was seen for monometallic Cu 4 and Pd 4 clusters on both C and W terminations, while the Cu-Pd bimetallics have intermediate adsorption energies but lack a clear trend in terms of composition. The location of Cu x Pd y clusters over the (0001) surface induces a decrease in the work function relative to the pristine surface, while the cluster-surface Bader charge transfer and variations in the partial density of states point to changes in the electronic structure of the carbide atoms upon binding of the metallic clusters. The d-band center of the Cu x Pd y deposited on WC(0001) indicates an intermediate reactivity among Cu(111) and Pd(111) surfaces, modulating the reactivity with small numbers of Cu and Pd atoms, i.e., atom economy in catalyst design. The likelihood of existence of the most stable Cu x Pd y (x + y = 4) clusters in the temperature range of 298-400 K is 100%. The composite Cu x Pd y /α-WC(0001) (x + y = 4), is a nontrivial system since 22 isomers are needed to completely describe its structural properties. Among the isomers, seven structures are necessary to represent Cu 3 Pd 1 /α-WC(0001), five for Pd 4 /α-WC(0001), two for Cu 4 /α-WC(0001), and four for Cu 2 Pd 2 /α-WC(0001) and Cu 1 Pd 3 /α-WC(0001). The large number of cluster isomers supported on the tungsten carbide surface opens the door for several applications in the heterogeneous catalysis of the Cu x Pd y /α-WC(0001) composite, with the possibility of modulating the geometric, electronic, and chemical properties according to a desired application. Test studies for the adsorption of CO 2 indicate that the Cu x Pd y /α-WC(0001) composites are highly active for the adsorption and decomposition of the molecule, with bimetallic and admetal-carbide interactions playing a key role in the binding performance. In conclusion, this high activity indicates that these systems should be useful as catalysts for the conversion of CO 2 to oxygenates or light alkanes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗