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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Tuning the activity and selectivity of electroreduction of CO 2 to synthesis gas using bimetallic catalysts

The electrochemical carbon dioxide reduction reaction to syngas with controlled CO/H 2 ratios has been studied on Pd-based bimetallic hydrides using a combination of in situ characterization and density functional theory calculations. When compared with pure Pd hydride, the bimetallic Pd hydride formation occurs at more negative potentials for Pd-Ag, Pd-Cu, and Pd-Ni. Theoretical calculations show that the choice of the second metal has a more significant effect on the adsorption strength of *H than *HOCO, with the free energies between these two key intermediates (i.e., ΔG(*H)–ΔG(*HOCO)) correlating well with the carbon dioxide reduction reaction activity and selectivity observed in the experiments, and thus can be used as a descriptor to search for other bimetallic catalysts. The results also demonstrate the possibility of alloying Pd with non-precious transition metals to promote the electrochemical conversion of CO 2 to syngas.

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

A data-driven framework for permeability prediction of natural porous rocks via microstructural characterization and pore-scale simulation

Understanding the microstructure–property relationships of porous media is of great practical significance, based on which macroscopic physical properties can be directly derived from measurable microstructural informatics. However, establishing reliable microstructure–property mappings in an explicit manner is difficult, due to the intricacy, stochasticity, and heterogeneity of porous microstructures. In this paper, a data-driven computational framework is presented to investigate the inherent microstructure–permeability linkage for natural porous rocks, where multiple techniques are integrated together, including microscopy imaging, stochastic reconstruction, microstructural characterization, pore-scale simulation, feature selection, and data-driven modeling. A large number of 3D digital rocks with a wide porosity range are acquired from microscopy imaging and stochastic reconstruction techniques. A broad variety of morphological descriptors are used to quantitatively characterize pore microstructures from different perspectives, and they compose the raw feature pool for feature selection. Here high-fidelity lattice Boltzmann simulations are conducted to resolve fluid flow passing through porous media, from which reliable permeability references are obtained. The optimal feature set that best represents permeability is identified through a performance-oriented feature selection process, upon which a cost-effective surrogate model is rapidly fitted to approximate the microstructure-permeability mapping via data-driven modeling. This surrogate model exhibits great advantages over empirical/analytical formulas in terms of prediction accuracy and generalization capacity, which can predict reliable permeability values spanning four orders of magnitude. Besides, feature selection also greatly enhances the interpretability of the data-driven prediction model, from which new insights into the mechanism of how microstructural characteristics determine intrinsic permeability are obtained.

58 GEOSCIENCES↗
DOE OSTI2021

Evaluating the benefits of kinetic Monte Carlo and microkinetic modeling for catalyst design studies in the presence of lateral interactions

Popular computational catalyst design strategies rely on the identification of reactivity descriptors, which can be used along with Brønsted–Evans–Polanyi (BEP) and scaling relations as input to a microkinetic model (MKM) to make predictions for activity or selectivity trends. The main benefit of this approach is related to the inherent dimensionality reduction of the large material space to just a few catalyst descriptors. Conversely, it is well documented that a small set of descriptors is insufficient to capture the intricacies and complexities of a real catalytic system. The inclusion of coverage effects through lateral adsorbate-adsorbate interactions can narrow the gap between simplified descriptor predictions and real systems, but mean-field MKMs cannot properly account for local coverage effects. This shortcoming of the mean-field approximation can be rectified by switching to a lattice-based kinetic Monte Carlo (kMC) method using cluster expansion representation of adsorbate–adsorbate lateral interactions. Using the prototypical CO oxidation reaction as an example, we critically evaluate the benefits of kMC over MKM in terms of trend predictions and computational cost when using only a small set of input parameters. After confirming that in the absence of lateral interactions the kMC and MKM approaches yield identical trends and mechanistic information, we observed substantial differences between the two kinetic models when lateral interactions were introduced. Here, the mean-field implementation applies coverage corrections directly to the descriptors, causing an artificial overprediction of the activity of strongly binding metals. In contrast, the cluster expansion in kMC implementation can differentiate among the highly active metals but it is very sensitive to the set of included interaction parameters. Considering that computational screening relies on a minimal set of descriptors, for which MKM makes reasonable trend predictions at a ca. three orders of magnitude lower computational cost than kMC, the MKM approach does provide a better entry point for computational catalyst design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Metal–Nitrogen–Carbon Cluster–Decorated Titanium Carbide is a Durable and Inexpensive Oxygen Reduction Reaction Electrocatalyst

Clusters of nitrogen- and carbon-coordinated transition metals dispersed in a carbon matrix (e. g., Fe-N-C) have emerged as an inexpensive class of electrocatalysts for the oxygen reduction reaction (ORR). In this work, it was shown that optimizing the interaction between the nitrogen-coordinated transition metal clusters embedded in a more stable and corrosion-resistant carbide matrix yielded an ORR electrocatalyst with enhanced activity and stability compared to Fe-N-C catalysts. Utilizing first-principles calculations, an electrostatics-based descriptor of catalytic activity was identified, and nitrogen-coordinated iron (FeN 4 ) clusters embedded in a TiC matrix were predicted to be an efficient platinum-group metal (PGM)-free ORR electrocatalyst. Guided by theory, selected catalyst formulations were synthesized, and it was demonstrated that the experimentally observed trends in activity fell exactly in line with the descriptor-derived theoretical predictions. The Fe-N-TiC catalyst exhibited enhanced activity (20 %) and durability (3.5-fold improvement) compared to a traditional Fe-N-C catalyst. It was posited that the electrostatics-based descriptor provides a powerful platform for the design of active and stable PGM-free electrocatalysts and heterogenous single-atom catalysts for other electrochemical reactions.

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

Stability of Pt Skin Intermetallic Core Catalysts and Adsorption Properties for the Oxygen Reduction Reaction

Density functional theory calculations were used to determine the stability of metal slabs consisting of a Pt surface monolayer and intermetallic supporting layers made from combinations of six transition metal elements (Pt, Fe, Co, Ni, Cu, and Ag), as a model system for Pt skin intermetallic core nanoparticle catalysts. The stability of the slabs is largely determined by strain at the interface of the Pt skin and the subsurface intermetallic, which was described by a lattice matching parameter (r). The surface charge on the Pt skin was found to be correlated with the average electronegativity (EN) of the intermetallic core, so this average EN was used as a descriptor for how the electronic coupling (or ligand effect) affects adsorption energies. A total of 46 slabs were investigated in terms of their stability, from which 10 stable slabs were selected for further studies of adsorbate binding (OOH*, O*, and OH*) that are intermediates in the oxygen reduction reaction (ORR). The correlation between all three adsorption energies and descriptors r and EN was found. Using a linear fit between our descriptors and the calculated adsorption energies, the overpotential for the ORR was obtained as a function of r and EN, from which a volcano plot was produced. The volcano peak was found at r = 0.96 or at EN = 2.025. Interestingly, neither r nor EN was a sufficient single reactivity descriptor as the data points were well off the general trend in both linear fits; this implies that both the strain effect and the ligand effect influence the adsorption energies, although they are partly correlated. The (r, EN) target peak parameters were used to screen over 241 intermetallic combinations of transition metal elements as active ORR activity. In conclusion, this analysis identified 11 intermetallic compounds which can support a Pt skin to have a high predicted ORR activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Microstructure prediction for Ti-22Al-25Nb in laser powder bed fusion

This work presents a physics-informed framework for predicting solidification morphology and defect susceptibility in additively manufactured Ti–22Al–25Nb across a broad processing space. The framework integrates solidification microstructure selection (SMS) analysis with a single-track defect-based printability map to establish a unified methodology linking processing parameters to both interfacial morphology and manufacturability. Thermal gradients G and solidification rates R are first computed using the Thermo-Calc Additive Manufacturing (TC-AM) module, a finite-interface-dissipation (FID) phase-field (PF) model coupled with CALPHAD method is then employed to systematically distinguish planar and dendritic regimes as functions of $G$ and $R$. By superimposing the printability map onto the morphology projections, a comprehensive process–structure framework is obtained. Across most processing conditions, the predicted microstructure is predominantly dendritic, while planar growth emerges only under selected laser power $P$ and scan speed $v$ combinations. In addition to morphology classification, the framework quantifies the dendritic area fraction and introduces a width-based morphology descriptor to characterize the spatial extent of planar/dendritic regions within the melt pool. It provides mechanistic insight into the interplay between solidification physics and defect formation, offering practical guidance for parameter selection and microstructural control in Ti–22Al–25Nb additive manufacturing (AM).

36 MATERIALS SCIENCE↗
DOE OSTI2019

Scaling Relations on Basal Plane Vacancies of Transition Metal Dichalcogenides for CO 2 Reduction

Transition metal dichalcogenides (TMDs) have shown promising electrocatalytic performance for CO 2 reduction (CO 2 R) recently. However, the development of efficient and selective catalysts remains a major challenge. Although recent studies have suggested the importance of activation energies as activity descriptors for CO 2 R beyond CO, the scaling of intermediate binding energies presents the first step in computational catalyst screening. Here, we investigate the basal vacancy on 2H and 1T/1T' phase group V, VI, and X TMDs for CO 2 reduction. We find that the change of oxophicility and carbophilicity on each group of TMDs follows different trends, which leads to different scaling relations amongst key intermediates. Our thermochemical analysis also suggests group V and VI TMDs to be either selective for hydrogen evolution reaction or prone to OH poisoning. Furthermore, the initial analysis suggests group X TMDs to be possible candidates for active and selective CO 2 reduction without suffering from OH poisoning, which motivates further theoretical kinetic studies. We furthermore find that their reaction energetics can be tuned by the density of the basal vacancies.

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

Improved Rate for the Oxygen Reduction Reaction in a Sulfuric Acid Electrolyte using a Pt(111) Surface Modified with Melamine

The feasible commercialization of alkaline, phosphoric acid and polymer electrolyte membrane fuel cells depends on the development of oxygen reduction reaction (ORR) electrocatalysts with improved activity, stability, and selectivity. The rational design of surfaces to ensure these improved ORR catalytic requirements relies on the so-called "descriptors" (e.g., the role of covalent and noncovalent interactions on platinum surface active sites for ORR). Here, we demonstrate that through the molecular adsorption of melamine onto the Pt(111) surface [Pt(111)-M ad ], the activity can be improved by a factor of 20 compared to bare Pt(111) for the ORR in a strongly adsorbing sulfuric acid solution. Additionally, the M ad moieties act as "surface-blocking bodies," selectively hindering the adsorption of (bi)sulfate anions (well-known poisoning spectator of the Pt(111) active sites) while the ORR is unhindered. This modified surface is further demonstrated to exhibit improved chemical stability relative to Pt(111) patterned with cyanide species (CN ad ), previously shown by our group to have a similar ORR activity increase compared to bare Pt(111) in a sulfuric acid electrolyte, with Pt(111)-M ad retaining a greater than ninefold higher ORR activity relative to bare Pt(111) after extensive potential cycling as compared to a greater than threefold higher activity retained on a CN ad -covered Pt(111) surface. We suggest that the higher stability of the Pt(111)-M ad interface stems from melamine's ability to form intermolecular hydrogen bonds, which effectively turns the melamine molecules into larger macromolecular entities with multiple anchoring sites and thus more difficult to remove.

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

Metal-Coordinated Phthalocyanines as Platform Molecules for Understanding Isolated Metal Sites in the Electrochemical Reduction of CO 2

Single-atom catalysts (SACs) of non-precious transition metals (TMs) often show unique electrochemical performance, including the electrochemical carbon dioxide reduction reaction (CO 2 RR). However, the inhomogeneity in their structures makes it difficult to directly compare SACs of different TM for their CO 2 RR activity, selectivity, and reaction mechanisms. Here, the comparison of isolated TMs (Fe, Co, Ni, Cu, and Zn) is systematically investigated using a series of crystalline molecular catalysts, namely TM-coordinated phthalocyanines (TMPcs), to directly compare the intrinsic role of the TMs with identical local coordination environments on the CO 2 RR performance. The combined experimental measurements, in situ characterization, and density functional theory calculations of TM-Pc catalysts reveal a TM dependent CO 2 RR activity and selectivity, with the free energy difference of ΔG(*HOCO) - ΔG(*CO) being identified as a descriptor for predicting the CO 2 RR performance.

25 ENERGY STORAGE↗
DOE OSTI2022

Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion

Catalyst discovery is paramount to support access to energy and key chemical feedstocks in a post fossil fuel era. Exhaustive computational searches of large material design spaces using ab initio methods like density functional theory (DFT) are infeasible. We seek to explore large design spaces at relatively low computational cost by leveraging large, generalized, graph-based machine learning (ML) models, which are pretrained and therefore require no upfront data collection or training. We present Catlas, a framework that distributes and automates the generation of adsorbate-surface configurations and ML inference of DFT energies to achieve this goal. Catlas is open source, making ML assisted catalyst screenings easy and available to all. To demonstrate its efficacy, we use Catlas to explore catalyst candidates for the direct conversion of syngas to multi-carbon oxygenates. For this case study, we explore 947 stable/metastable binary, transition metal intermetallics as possible catalyst candidates. On this subset of materials, we are able to predict the adsorption energy of key descriptors, *CO and *OH, with good accuracy (0.16, 0.14 eV MAE, respectively). Using the projected selectivity towards C 2+ oxygenates from an existing microkinetic model, we identified 144 candidate materials. For 10 promising candidates, DFT calculations reveal a good correlation with our assessment using ML. Finally, among the top elemental combinations were Pt–Ti, Pd–V, Ni–Nb, and Ti–Zn, all of which appear unexplored experimentally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Scanning electron microscopy image representativeness: morphological data on nanoparticles

Summary A sample of a nanomaterial contains a distribution of nanoparticles of various shapes and/or sizes. A scanning electron microscopy image of such a sample often captures only a fragment of the morphological variety present in the sample. In order to quantitatively analyse the sample using scanning electron microscope digital images, and, in particular, to derive numerical representations of the sample morphology, image content has to be assessed. In this work, we present a framework for extracting morphological information contained in scanning electron microscopy images using computer vision algorithms, and for converting them into numerical particle descriptors. We explore the concept of image representativeness and provide a set of protocols for selecting optimal scanning electron microscopy images as well as determining the smallest representative image set for each of the morphological features. We demonstrate the practical aspects of our methodology by investigating tricalcium phosphate, Ca 3 (PO 4 ) 2 , and calcium hydroxyphosphate, Ca 5 (PO 4 ) 3 (OH), both naturally occurring minerals with a wide range of biomedical applications.

ODZIOMEK, KATARZYNA↗
DOE OSTI2020

Descriptor-Based Analysis of Atomic Layer Deposition Mechanisms on Spinel LiMn 2 O 4 Lithium-Ion Battery Cathodes

Protective coatings have been shown to effectively suppress Mn ion dissolution from the spinel LiMn 2 O 4 lithium-ion battery cathode by stabilizing the surface against undesired side reactions with the electrolyte. In spite of extensive study, however, there remains a lack of atomic-scale understanding of how such coatings are deposited, and no molecular-level descriptor to predict trends in deposition mechanisms has been identified. We have recently shown that Al 2 O 3 coatings grown by atomic layer deposition (ALD) with alternating trimethylaluminum (TMA) and water exposures exhibit submonolayer growth because of precursor decomposition on the lithium manganate spinel (LMO) surface during early ALD pulses. In the present work, we elucidate the underlying mechanisms of this Al 2 O 3 ALD process using density functional theory (DFT) calculations and X-ray photoelectron spectroscopy (XPS) experiments, and we introduce a generalized descriptor-based framework to understand the resulting trends across a spectrum of surface structures and functionalities. We demonstrate that all decomposition products, including CH 3 -aluminum adducts and dissociated CH 3 groups, are Lewis bases and are coordinated to oxygen atoms on the LMO surface, leading to charge transfer to Lewis acidic Mn 3d states. Inert-transfer XPS supports these theoretical predictions, showing an increase in near-surface Mn 3+ content following TMA exposure and shifts in C is spectra consistent with C-O bond formation. We extend the DFT studies to various low- and high-index LMO surface facets, as a proxy for tuning the Lewis acid-base interactions between surface-bound CH 3 * and near-surface Mn ions. The thermochemistry for TMA reactions on these chemically distinct LMO surfaces demonstrates that ALD is structure-sensitive and that there is higher reactivity for TMA decomposition and Al 2 O 3 nucleation near LMO steps and defects. Motivated by the Lewis basic character of the decomposition products, we introduce the oxygen vacancy formation energy as a descriptor for decomposition energetics, and we demonstrate that all energetics are correlated to this quantity through the number of electrons that are transferred along the reaction coordinate. Finally, based on these findings, we hypothesize that improved electrochemical cycling with only 1-2 ALD cycles may be due to selective passivation of defect sites on the LMO surface that are more susceptible to Mn dissolution, and we suggest that similar descriptor-based analyses could be useful for the study of other ALD coatings on oxide substrates.

25 ENERGY STORAGE↗
DOE OSTI2023

Unifying principles for catalytic hydrotreating processes (Final Technical Report)

This project builds on the hypothesis that the hydrotreating processes for the removal of oxygen and sulfur are fundamentally similar at the atomic-scale and existing knowledge from the treatment of petroleum derived feedstock can be leveraged for the design of novel catalysts for the upgrade of bio-oil. We tested this hypothesis by comparing computed potential energy diagrams for hydrodesulfurization (HDS) of thiophene over MoS 2 with hydrodeoxygenation (HDO) of furan over MoO 3 and concluded that certain aspects, such as catalyst promotion with transition metals, are valid strategies for both reactions. On the other hand, we also noticed significant differences in the mechanism for hydrogen (H 2 ) activation, which requires sites with metallic character. While MoS 2 is known to have metallic edge states that can catalyze H 2 dissociation, this elementary step is prohibitively slow on defect-free oxides. Only in the presence of vacancies or by creating metal/oxide interfaces can efficient H 2 activation sites during HDO be formed. The need for bifunctional catalyst when it comes to efficient and selective HDO or dehydrogenation reactions was further corroborated in joint experimental and theoretical studies of the Guerbet reaction for the coupling of biomass derived oxygenates over PdCu alloys, nitrate reduction over In-promoted Pd nanoparticles, and ethylene dehydroaromatization over Ga-exchanged ZSM-5 zeolites. All of these catalytic systems have in common that catalytic sites with distinct functional requirements are needed to create a working catalyst. Detailed computational studies were carried out for HDO of m -cresol and phenol on Ru-modified TiO 2 surfaces, which allowed us to attribute catalytic activity to the metal/oxide interface. A surprising finding was that heterolytic cleavage of the H-H bond across the Ru/TiO 2 interface was critically important, despite lower barriers for homolytic H 2 activation on Ru metal. The explanation lies in the high barriers for hydrogen spillover from Ru to TiO 2 , which becomes unnecessary in the heterolytic activation pathway. Moreover, we also reported that proton transfer steps between metal and oxide sites are mediated by weakly adsorbed surface water. During attempts to develop and validate a kinetic Monte Carlo (kMC) model for HDO reactions at the Ru/TiO 2 interface, it became clear that lateral interactions are paramount to describe realistic surface chemistry and without these interactions, the reduction and hydroxylation behavior from our simulations was inconsistent with reported experiments. To assess the importance of lateral interactions in popular computational catalyst design strategies relying on the identification of reactivity descriptors, which can be used along with Brønsted–Evans–Polanyi (BEP) and scaling relations as input to a microkinetic model (MKM) to make predictions for activity or selectivity trends, we compared predicted trends with those obtained from descriptor-based kMC models. We critically evaluated the benefits of kMC over MKM in terms of trend predictions and computational cost when using only a small set of input parameters. After confirming that in the absence of lateral interactions the kMC and MKM approaches yield identical trends and mechanistic information, we observed substantial differences between the two kinetic models when lateral interactions were introduced. The mean-field implementation applies coverage corrections directly to the descriptors, causing an artificial overprediction of the activity of strongly binding metals. In contrast, the cluster expansion in kMC implementation can differentiate among the highly active metals but it is very sensitive to the set of included interaction parameters. Considering that computational screening relies on a minimal set of descriptors, for which MKM makes reasonable trend predictions at a ca. three orders of magnitude lower computational cost than kMC, we concluded that the MKM approach does provide an overall better entry point for computational catalyst design. Overall, this project has led to 11 peer-reviewed publications, and their scientific is impact is well illustrated by their combined 702 citations.

08 HYDROGEN↗
DOE OSTI2020

Acidity of surface-infiltrated binary oxides as a sensitive descriptor of oxygen exchange kinetics in mixed conducting oxides

Improving the kinetics of O 2 reduction on oxide surfaces is critical in many energy and fuel conversion technologies. Here we show that the acidity scale for binary oxides is a powerful descriptor for tuning and predicting oxygen surface exchange kinetics on mixed conducting oxides. By infiltrating a selection of binary oxides from strongly basic (Li 2 O) to strongly acidic (SiO 2 ) onto the surface of Pr 0.1 Ce 0.9 O 2-δ samples, it was possible to vary the chemical surface exchange coefficient k chem by 6 orders of magnitude, with basic oxides such as Li 2 O increasing k chem by nearly 1,000 times, with surface concentrations as low as 50 ppm impacting k chem . Strikingly, although the pre-exponential of k chem scales linearly with the acidity of the infiltrated binary oxide, there is nearly no change in the activation energy. Here, the origin of these dramatic changes is proposed to arise from the systematic increase in electron concentration at the Pr 0.1 Ce 0.9 O 2-δ surface with the decreasing acidity of the infiltrated binary oxide.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Epitaxial Design of Complex Nickelates as Electrocatalysts for the Oxygen Evolution Reaction

Abstract The oxygen evolution reaction (OER) is a crucial process in electrochemical water splitting, a promising technology to renewably yield hydrogen gas from water. Designing and developing earth‐abundant, efficient, and stable OER electrocatalysts to replace the most widely used but scarce RuO 2 and IrO 2 are thus of critical interest. Recently, ABO 3 ‐structured perovskite oxides, especially rare‐earth nickelates, are extensively studied for their potential use as OER electrocatalysts. In particular, the epitaxial synthesis of complex oxide thin films allows flexible and precise control over the materials so that their structure–stability–property relationships can be established. Using nickelate thin films as model systems, this review illustrates how epitaxial design allows researchers to test different hypotheses and proposed descriptors, as well as formulate new design principles. Following a brief introduction to the background of OER mechanisms, proposed activity descriptors, and synthesis methods, various epitaxial design strategies are surveyed including strain tuning, composition control, surface termination/orientation selection, defect engineering, and interface design. These have led to precise control over the atomic structures and electronic properties of nickelates which in turn determine their electrochemical performance. Finally, the remaining challenges and perspectives toward a deeper understanding and use of complex oxides as OER catalysts are discussed.

Choi, Min‐Ju↗
DOE OSTI2025

Functional Design of Peptide Materials Based on Supramolecular Cohesion

Peptide materials offer a broad platform to design biomimetic soft matter, and filamentous networks that emulate those in extracellular matrices and the cytoskeleton are among the important targets. Given the vast sequence space, a combination of computational approaches and readily accessible experimental techniques is required to design peptide materials efficiently. Here, we report here on a strategy that utilizes this combination to predict supramolecular cohesion within filaments of peptide amphiphiles, a property recently linked to supramolecular dynamics and consequently bioactivity. Using established coarse-grained simulations on 10,000 randomly generated peptide sequences, we identified 3500 likely to self-assemble in water into nanoscale filaments. Atomistic simulations of small clusters were used to further analyze this subset of sequences and identify mathematical descriptors that are predictive of intermolecular cohesion, which was the main purpose of this work. We arbitrarily selected a small cohort of these sequences for chemical synthesis and verified their fiber morphology. With further characterization, we were able to link the latent heat associated with fiber to micelle transitions, an indicator of cohesion and potential supramolecular dynamicity within the filaments, to calculated hydrogen bond densities in the simulation clusters. Based on validation from in situ synchrotron X-ray scattering and differential scanning calorimetry, we conclude that the phase transitions can be easily observed by very simple polarized light microscopy experiments. We are encouraged by the methodology explored here as a relatively low-cost and fast way to design potential functions of peptide materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

From Structured Solvents to Hybrid Materials (SS2HM) for Chemically Selective Capture and Electromagnetic Release of CO 2 : Mechanisms, Stability and Interfaces (Final Report)

The goal of this research program was to develop high capacity sorbents amenable for alternative regeneration approaches for direct air capture (DAC) of CO 2 . In particular, the research aimed to develop an understanding of CO 2 binding mechanism, thermal and oxidative stability, and regeneration energetics of functionalized ionic liquids (ILs), deep eutectic solvents (DESs), and porous materials. ILs and DESs are high-dielectric solvents with structural tunability that permits the rational-design for energy-efficient regeneration approaches based on electromagnetic (EM) field and moisture-swing. By further incorporating these solvents into polymeric capsules and other structural supports, multi-scale interfaces for targeted CO 2 and energy transfers were achieved. Aspects related to CO 2 capacity, selectivity, stability, dielectric properties, and binding energies were examined through experimental and computational design to identify molecular descriptors to inform future design of structured solvents and hybrid materials for DAC. Enclosed final report details the key findings, science advancements, and workforce development efforts from this project.

36 MATERIALS SCIENCE↗
DOE OSTI2016

A systematic study on Pt based, subnanometer-sized alloy cluster catalysts for alkane dehydrogenation: effects of intermetallic interaction

Platinum-based bimetallic nanoparticles are analyzed by the application of density functional theory to a series of tetrahedral Pt 3 X cluster models, with element X taken from the P-block, preferably group 14, or from the D-block around group 10. Almost identical cluster geometries allow a systematic investigation of electronic effects induced by different elements X. Choosing the propane-to-propene conversion as the desired dehydrogenation reaction, we provide estimates for the activity and selectivity of the various catalysts based on transition state theory. No significant Brønsted–Evans–Polanyi-relation could be found for the given reaction. A new descriptor, derived from an energy decomposition analysis, captures the effect of element X on the rate-determining step of the first hydrogen abstraction. Higher activities than obtained for pure Pt 4 clusters are predicted for Pt alloys containing Ir, Sn, Ge and Si, with Pt 3 Ir showing particularly high selectivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗