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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Economizing on Precious Metals in Three-Way Catalysts: Thermally Stable and Highly Active Single-Atom Rhodium on Cera for NO Abatement under Dry and Industrially Relevant Conditions

We show for the first time that atomically dispersed Rh cations on ceria, prepared via high-temperature atom trapping synthesis, are the active species for (CO+NO) reaction. This provides a direct link with the organometallic homogeneous Rh(I) complexes capable of catalyzing dry (CO+NO) reaction but without the stability concern. In particular, thermally stable Rh cations in 0.1wt% Rh1/CeO2 achieve full NO conversion with a TOF of ~ 330 hr-1 per Rh atom at 120 °C. Under dry conditions, the main product above 100 °C is N2 with N2O being the minor product. The presence of water promotes low-temperature activity of 0.1wt% Rh1/CeO2 starting at 50 °C with full NO conversion at ~120 °C. In the case with the wet stream, however, ammonia and nitrogen are the main products with only minor N2O amounts. NH3 formation at such low temperatures is attractive because of the potential to use this as a passive SCR system. Because of the uniformity of Rh ions on the support, we are able to observe the intermediates of (CO+NO) reaction via infrared measurements on Rh cations on zeolite and ceria. Furthermore, we show that ammonia formation correlates with the WGS activity of the material and thus, rhodium hydride Rh-H species are involved in this reaction: we detect Rh(CO)Hx species on ceria using isotopic IR measurements. These findings provide new mechanistic understanding for the catalytically active species in TWC catalysis and open up a new avenue for the synthesis of novel emissions control catalysts with 100% atom economy of ultra-expensive precious metals such as Rh.

Khivantsev, Konstantin↗
DOE OSTI2021

Comparative Study on the Machine Learning-Based Prediction of Adsorption Energies for Ring and Chain Species on Metal Catalyst Surfaces

Computation of adsorption and transition state energies for a large number of surface intermediates for numerous active site models pose significant computational overhead in computational screening of catalysts. Machine learning (ML) techniques can be used to predict part of these energies. To predict the energies, ML models need to be fed appropriate metal and species descriptors. For complex surface chemistries, the structures of the intermediate species can vary greatly. In this paper, working with the hydrodeoxygenation of succinic acid on six different metal surfaces, we have studied the effect of linear and non-linear ML models used along with pen-and-paper based species descriptors and two categories of metal descriptors on two different categories of intermediate species: chain and ring. More specifically, our computations include the prediction of chain species when trained on only chain species and also when trained on both chain and ring species. Similar computations were performed for predictions of ring species. In each case, results of linear ML models were compared with kernel based non-linear models. Our results indicate that ring species data does not improve the prediction of chain species. Similarly, chain species data does not improve the prediction of ring species. The use of non-linear ML models, however, did help to minimize the prediction errors compared to the linear models. Furthermore, the study also shows that electronic or adsorption energy based metal descriptors along with bond count based species fingerprints can achieve a mean absolute error (MAE) of less than 0.2 eV for complex chain molecules when used with an appropriate machine learning model.

Adsorption↗
DOE OSTI2020

Examining Ohmic Losses in Fuel-Cell Catalyst Layers with Different Pt/C Ratios

A model of a fuel-cell cathode with distinct proton conductivities at the agglomerate and electrode scales was used to simulate polarization curves under conditions with facile oxygen transport. Here, the model was applied to electrodes containing agglomerates with different ratios of platinum to carbon in order to examine the hypothesis that intra-agglomerate proton conductivity is substantially lower than ionomer conductivity. The model predicts decreasing performance and increasing sensitivity to Pt/C ratio as intra-agglomerate conductivity declines relative to a constant through-electrode conductivity. Experiments on catalyst layers with Vulcan supports do not show the predicted trend indicating that mesoscale intra-agglomerate conductivity is not orders of magnitude lower than bulk ionomer conductivity.

08 HYDROGEN↗