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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Molecular Modifications of Crystalline Poly(triazine imide) for Advancing Its Structure–Property Relationships in Light-Driven Catalysis

Carbon-nitride materials represent light-absorbing structures composed of earth-abundant elements capable of being leveraged for semiconductor photocatalysis at their surfaces. This study systematically investigates the addition of molecular modifiers to the synthesis of crystalline carbon nitrides to assess their effects on the materials’ structure, optical bandgap, and photocatalytic activity for hydrogen (H 2 ) and oxygen (O 2 ) evolution under ultraviolet and visible-light irradiation. Melamine and five pyrimidine-centered analogs were employed as building blocks to modify various heteroatoms within the polymeric framework. The modified materials were characterized with attention to the differences introduced by the monomeric modifiers and their influence on the resulting structures and compositions. The findings indicate that these changes significantly broaden the visible-light absorption range, albeit with the gradual loss of the bulk crystalline structure. As the loading of modifiers increased beyond 50%, a predominantly amorphous form of carbon nitride emerged. XPS, 13 C solid-state NMR, and SEM analyses corroborated the changes, which were attributed to modifications of the elemental composition and a reduced amount of Li cations and charge-balancing Cl anions owing to fewer binding sites in the intralayer cavities. In photocatalytic measurements under an ultraviolet 390 nm LED, and aided by photodeposited nanoparticle cocatalysts, the unmodified PTI-LiCl framework demonstrated the highest H 2 evolution rate (HER; 3.44 mmol·g –1 ·h –1 ) with an apparent quantum yield of 5.4%, along with total water splitting at rates of 163 μmol of H 2 ·g –1 ·h –1 and 75.6 μmol·O 2 g –1 ·h –1 . While PTI-LiCl showed trace activity under a visible-light 440 nm LED, all modified materials exhibited enhanced reactivity with as low as 5% molecular modifiers. The photocatalytic rates peaked at a 15% modification level when using 2,4,6-triaminopyrimidine, with rates of 33 μmol·g –1 ·h –1 for HER, along with 19.7 μmol of H 2 ·g –1 ·h –1 and 8.7 μmol of O 2 ·g –1 ·h –1 for total water splitting. Density functional theory calculations were used to probe electronic structure changes resulting from the modifications. Furthermore, these results elucidate the structural, optical, and electronic changes arising from the five selected molecular modifiers and their impact on the semiconductors’ photocatalytic properties.

Electrical conductivity↗
DOE OSTI2020

Atomically Dispersed Metals on Well-Defined Supports including Zeolites and Metal–Organic Frameworks: Structure, Bonding, Reactivity, and Catalysis

When metals in supported catalysts are atomically dispersed, they are usually cationic and bonded chemically to supports. Investigations of noble metals in this class are growing rapidly, leading to discoveries of catalysts with new properties. Characterization of these materials is challenging because the metal atoms reside on surfaces that are typically nonuniform in composition and structure. We posit that understanding of structures and catalytic properties of these materials is emerging most strongly from investigations of structurally uniform catalysts (metal atoms dispersed on crystalline supports) which can be characterized incisively with atomic-resolution electron microscopy, X-ray absorption spectroscopy, and infrared spectroscopy, bolstered by density functional theory. We assess the literature of such catalysts supported on zeotype materials, metal–organic frameworks, and covalent organic frameworks. Finally, assessing characterization, reactivity, and catalytic performance of catalysts for oxidation, hydrogenation, the water–gas shift reaction, and others, we consider metal–support interactions and ligand effects for various metal–support combinations, evaluating the degree of structural uniformity of exemplary catalysts and summarizing structure–reactivity and structure–catalytic property relationships.

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

Sustainable H 2 -Rich Syngas Production via Microwave-Assisted vs Conventional Catalysis of Pinewood

Catalytic gasification of biomass is a promising method for producing hydrogen-rich syngas, which is a valuable resource for cleanenergy applications. In this study, microwave-assisted biomass gasification was compared with conventional thermally driven biomass gasification using pinewood as the biomass without the use of external gasifying agents (such as air, steam, and CO 2 ), under non-catalytic and catalytic conditions. The catalysts consisted of either an iron or a nickel catalyst, and the pinewood used as biomass contained 42% oxygen. This comparative analysis explores the differences in reaction chemistry, product yields, and the role of key reactions such as the water gas shift (WGS) reaction, Boudouard reaction, etc. The gas-phase and liquid-phase products were analyzed using online gas chromatography, and the fresh and spent catalysts were analyzed using X-ray diffraction (XRD) techniques. It was found that microwave-assisted gasification offers advantages in terms of enhanced reaction efficiency, catalyst stability, and hydrogen yield. For the microwave-assisted reaction, the gas yield reached 87%, the char yield was 12.1%, and the tar yield was less than 1% (0.793%) at 550 °C. In contrast, thermal-assisted gasification using the same catalyst produced a gas yield of 85.796%, char yield of 11.438%, and higher tar yield of 2.8% at 900 °C. The higher microwave-assisted performance was attributed to faster heating and better control over reaction conditions, higher reaction rates, and more favorable conditions for hydrogen production.

biomass↗
DOE OSTI2021

Aerobic Partial Oxidation of Alkanes Using Photodriven Iron Catalysis

Photodriven oxidations of alkanes in trifluoroacetic acid using commercial and synthesized Fe(III) sources as catalyst precursors and dioxygen (O 2 ) as the terminal oxidant are reported. The reactions produce alkyl esters and occur at ambient temperature in the presence of air, and catalytic turnover is observed for the oxidation of methane in a pure O 2 atmosphere. Under optimized conditions, approximately 17% conversion of methane to methyl trifluoroacetate at more than 50% selectivity is observed. Furthermore, it is demonstrated that methyl trifluoroacetate is stable under catalytic conditions, and thus overoxidized products are not formed through secondary oxidation of methyl trifluoroacetate.

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

Regioselective Hydrosilylation Catalysis with Supported Well-Defined Pt(0) Complexes: Effects of Surface Anions and Phosphenium Ligands

Achieving stable, selective single-atom catalysts is challenging because localsurface-site structures are difficult to control. Surface organometallic chemistry and organic−inorganic hybrid materials offer partial solutions, but applications to supporting zero-valent metals are limited. We demonstrate that well-defined, ionically bound N-heterocyclic phosphenium ([NHP] + ) ligands can be generated on silylium-functionalized sulfated zirconia ([ i Pr 3 Si][SZO]). These surface-bound [NHP][SZO] ligands coordinate Pt(0) centers, forming [(NHP)Pt(0)L][SZO] precatalysts that are highly active for alkyne hydrosilylation. Systematic studies reveal that sterically bulky aromatic ligands enhance regioselectivity, achieving performances comparable to molecular Pt catalysts. Further, more-coordinating anions support more-regioselective precatalysts; therefore, SZO supports more-selective species than weaker-coordinating Al(OC(CF 3 ) 3 ) 3 - functionalized silica, a trend confirmed by molecular analogues. These results demonstrate that both the ligand and support control catalytic behavior and enable solid-state structure− activity relationships.

Catalysts↗
DOE OSTI2024

Invariant Molecular Representations for Heterogeneous Catalysis

Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used. This study introduces a novel machine learning (ML) method to learn adsorption energies from multiple DFT functionals by using invariant molecular representations (IMRs). To do this, we first extract molecular fingerprints for the reaction intermediates and later use a Siamese-neural-network-based training strategy to learn invariant molecular representations or the IMR across all available functionals. Our Siamese network-based representations demonstrate superior performance in predicting adsorption energies compared with other molecular representations. Notably, when considering mean absolute values of adsorption energies as 0.43 eV (PBE-D3), 0.46 eV (BEEF-vdW), 0.81 eV (RPBE), and 0.37 eV (scan+rVV10), our IMR method has achieved the lowest mean absolute errors (MAEs) of 0.18 0.10, 0.16, and 0.18 eV, respectively. These results emphasize the superior predictive capacity of our Siamese network-based representations. The empirical findings in this study illuminate the efficacy, robustness, and dependability of our proposed ML paradigm in predicting adsorption energies, specifically for propane dehydrogenation on a platinum catalyst surface.

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

A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications

Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the number of reaction intermediates can be very large, and the cost of calculating the adsorption energies by DFT for all surface intermediates even for one active site model can become prohibitive. In this paper, we have identified appropriate descriptors and machine learning models that can be used to predict a significant part of these adsorption energies given data on the rest of them. Furthermore, our investigations also included the case when the species data used to train the predictive model are of different size relative to the species the model tries to predict—this is an extrapolation in the data space which is typically difficult with regular machine learning models. Due to the relative size of the available data sets, we have attempted to extrapolate from the larger species to the smaller ones in the current work. Here, we have developed a neural network based predictive model that combines an established additive atomic contribution based model with the concepts of a convolutional neural network that, when extrapolating, achieves a statistically significant improvement over the previous models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗