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Linic, Suljo

Publications and source records attributed to Linic, Suljo.

9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Overcoming limitations in propanedehydrogenation by codesigning catalyst-membrane systems

Propylene production through propane dehydrogenation (PDH) is endothermic, and high temperatures required to achieve acceptable propane conversions lead to low selectivity and severe carbon-induced deactivation of conventional catalysts. We developed a catalyst-membrane system that removes the hydrogen by-product and can thus achieve propane conversions that exceed equilibrium limits. In this codesigned system, a silica/alumina (SiO 2 /Al 2 O 3 ) hollow-fiber hydrogen membrane was packed with a selective platinum-tin (Pt 1 Sn 1 /SiO 2 ) PDH catalyst on the tube side with hydrogen diffusing from the tube to the shell side. We demonstrate that the catalyst-membrane system can achieve propane conversions >140% of the nominal equilibrium conversion with a propylene selectivity >98% without deactivation of the system components. Here, we also show that by introducing oxygen on the shell side of the catalyst-membrane system, we can couple the endothermic PDH reaction on the tube side with exothermic hydrogen oxidation on the shell side. This coupling results in higher rates of hydrogen transport, leading to further enhancements in the propane conversion as well as desired thermoneutral system operation.

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

High-Performance Iridium–Molybdenum Oxide Electrocatalysts for Water Oxidation in Acid: Bayesian Optimization Discovery and Experimental Testing

Ir oxides are costly and scarce catalysts for oxygen evolution reaction (OER) in acid. There has been extensive interest in developing alternatives that are either Ir-free or require smaller amounts of Ir to drive the reactions at acceptable rates. One design strategy is to identify Ir-based mixed oxides that achieve similar performance while requiring smaller amounts of Ir. The obstacle to this strategy has been a very large phase space of the Ir-based mixed metal oxides, in terms of the metals combined with Ir and the different crystallographic structures of the mixed oxides, which prevents a thorough exploration of possible materials. In this work, we developed a workflow that uses machine-learning-aided Bayesian optimization in combination with density functional theory to make the exploration of this phase space plausible. This screening identified Mo as a promising dopant for forming acid-tolerant Ir-based oxides for the OER. We synthesized and characterized the Ir–Mo mixed oxides in the form of thin-film electrocatalysts with a known surface area. We show that these mixed oxides exhibited overpotentials ~30 mV lower than a pure Ir control while maintaining 24% lower Ir dissolution rates than the Ir control. Furthermore, these findings suggest that Mo is a promising dopant and highlight the promise of machine learning to guide the experimental exploration and optimization of catalytic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Optimizing hierarchical membrane/catalyst systems for oxidative coupling of methane using additive manufacturing

The advantage of a membrane/catalyst system in oxidative coupling of methane (OCM) compared to conventional reactive systems is that by introducing oxygen to the OCM catalytic sites through a membrane, parasitic gas phase reactions of O 2 (g), responsible for lowering product selectivity, can be avoided. The design and fabrication of membrane/catalyst systems has, however, been hampered by low volumetric chemical conversion rates, high capital cost, and difficulties in codesigning membrane and catalyst properties to optimize the performance. We solve these issues by developing a dual-layer additive manufacturing process, based on phase inversion, to design, fabricate and optimize a hollow fiber membrane/catalyst system for OCM. We demonstrate the approach though a case study using BaCe 0.8 Gd 0.2 O 3-δ (BCG) as the basis of both the catalyst and separation layers. We show that by using the manufacturing approach we can codesign the membrane thickness and catalyst surface area so that the flux of oxygen transport through the membrane and methane activation rates in the catalyst layer match each other. Here, we demonstrate that this “rate matching” is critical for maximizing the performance, with the membrane/catalyst system significantly overperforming conventional reactor designs under identical conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Mechanisms of Ethylene Epoxidation over Silver from Machine Learning-Accelerated First-Principles Modeling and Microkinetic Simulations

In this work we employed machine learning-augmented density functional theory (DFT) thermodynamic calculations to assess the stability of different AgO x structures under catalytic ethylene epoxidation reaction conditions. We found that there are multiple AgO x surface motifs that could co-exist under the relevant conditions. These included Ag surface oxides (e.g., AgO_p(4 × 4) and Ag 1.83 O) and atomic oxygen-covered Ag(111) surfaces. Furthermore, we employed DFT calculations to evaluate the energetics of different reaction mechanisms by which ethylene and oxygen can react on these surfaces. These studies revealed several energetically viable reaction pathways for ethylene epoxidation. Microkinetic modeling analyses, based on the DFT-calculated reaction pathways, showed that ethylene epoxidation can proceed on all surfaces and that multiple pathways, including those involving Langmuir–Hinshelwood and Eley–Rideal mechanisms, could be involved in selective and unselective reactions. The diversity of mechanisms that we discovered in the context of the relatively simple ethylene epoxidation reaction on Ag suggests that the richness and complexity of surface chemistry are most likely a rule rather than an exception in heterogeneous catalytic chemical transformations on metal surfaces and that the concept of a single or even a dominant mechanism and reaction intermediates might need to be revisited for many reactions.

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

Elucidating the Roles of Local and Nonlocal Rate Enhancement Mechanisms in Plasmonic Catalysis

Plasmonic metal nanoparticles (e.g., Ag, Au, and Cu) constitute a class of materials that interact with light via the excitation of localized surface plasmon resonance (LSPR). Numerous studies have reported substantial enhancements in the rates of chemical reactions on illuminated plasmonic nanoparticle catalysts compared to corresponding systems in the absence of illumination. There are two mechanisms that have been proposed to explain the LSPR-induced chemical reactivity. One mechanism assumes a local plasmon-induced hot charge-carrier-mediated activation of the reactants, while the other assumes an LSPR-induced equilibrium heating of the catalyst, which leads to energy transfer to and chemical reaction of the adsorbed reactants. Here, in this contribution, we developed a setup amenable to accurate in situ catalyst temperature and kinetic reaction rate measurements. We employed this setup to study the LSPR-induced rate enhancement in a case study of the CO oxidation reaction on plasmonic, monometallic Ag nanoparticle catalysts supported on α-Al 2 O 3 . We explored various Ag loadings and clustering levels. Our data show that the equilibrium heating of the catalyst cannot fully explain the illumination-induced plasmonic rate enhancements. This is the case even for high loading and clustering of Ag nanoparticles, where the equilibrium heating significantly increases. Based on the analysis, we propose that local effects, related to the plasmon-induced activation of adsorbates (reactants) via electronic excitation of the reactant or photothermal heating of the reactants that is highly localized to the individual nanoparticles, play a critical role in driving LSPR-induced chemical reactions.

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

Optimizing molecular light absorption in the strong coupling regime for solar energy harvesting

The strong coupling of optical absorbers (e.g., molecules or semiconductors) to confined photonic modes fundamentally alters the physical properties of the coupled system via the formation of hybrid light-matter states. One potential application of strong light-matter coupling relies on exploiting it to localize light-induced charge excitation processes to small volumes of material. Applications that would benefit from this localization include thin-film photovoltaics, photodetection, photocatalysis, and others, where the overall performance depends on the ability of a material to amplify light absorption (i.e., the formation of electron-hole pairs) within specific locations in space. This contribution investigates how strong light-matter coupling affects light absorption rates in molecular absorbers coupled to photonic nanostructures. Here, our results show that the molecular light absorption efficiencies are highest in configurations where the strongly coupled molecules interact directly with the incoming photon flux. We also identify a nonlinear dependence in the molecular absorption as a function of concentration, unique to the strongly coupled systems. Based on these results, we propose design principles for engineering nanostructured systems that allow for high efficiencies of charge carrier localization into strongly coupled absorbers.

14 SOLAR ENERGY↗
DOE OSTI2022

Interpretable machine learning for knowledge generation in heterogeneous catalysis

Most applications of machine learning in heterogeneous catalysis thus far have used black-box models to predict computable physical properties (descriptors), such as adsorption or formation energies, that can be related to catalytic performance (that is, activity or stability). Here, extracting meaningful physical insights from these black-box models has proved challenging, as the internal logic of these black-box models is not readily interpretable due to their high degree of complexity. Interpretable machine learning methods that merge the predictive capacity of black-box models with the physical interpretability of physics-based models offer an alternative to black-box models. In this Perspective, we discuss the various interpretable machine learning methods available to catalysis researchers, highlight the potential of interpretable machine learning to accelerate hypothesis formation and knowledge generation, and outline critical challenges and opportunities for interpretable machine learning in heterogeneous catalysis.

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

Characterizing the Geometry and Quantifying the Impact of Nanoscopic Electrocatalyst/Semiconductor Interfaces under Solar Water Splitting Conditions

The materials that are receiving the most attention in photoelectrochemical water splitting are metallic nanoparticle electrocatalysts (np-EC) attached to the surface of a semiconductor (SC) light absorber. In these multicomponent systems, the interface between the semiconductor and electrocatalysts critically affects performance. However, the np-EC/SC interface remains poorly understood as it is complex on atomic scales, dynamic under reaction conditions, and inaccessible to direct experimental probes. This contribution sheds light on how the electrocatalyst/semiconductor interface evolves under reaction conditions by investigating the behavior of nickel electrocatalysts (as nanoparticles and films) deposited on silicon semiconductors. Rigorous electrochemical experiments, interfacial atomistic characterization, and computational modeling are combined to demonstrate critical links between the atomistic features of the interface and the overall performance. It is shown that electrolyte-induced atomistic changes to the interface lead to (1) modulation of the charge carrier fluxes and a dramatic decrease in the electron/hole recombination rates and (2) a change in the barrier height of the interface. Additionally, the critical roles of nonidealities and electrocatalyst coverage due to interfacial geometry are explored. Each of these factors must be considered to optimize the design of metal/semiconductor interfaces which are broadly applicable to photoelectrocatalysis and photovoltaic research.

36 MATERIALS SCIENCE↗