Results for “Ruthenium oxide” — Page 8

DOE OSTI2026

In Situ Studies of Ru-CeO 2 –TiO 2 Catalysts for Selective CO 2 Hydrogenation to Methane: Importance of Metal ↔ Oxide–Oxide Interactions

Here, this work investigates Ru-CeO 2 -TiO 2 catalysts for the CO 2 methanation reaction and compares their performance with previously studied Ru-CeO 2 systems. Despite the lower Ru loading, the TiO 2 -containing catalysts exhibit significantly higher activity. To understand this behavior, in situ X-ray absorption spectroscopy (XAS) was carried out at the Ru K-edge and Ce L 3 -edge. Unlike Ru-CeO 2 , which displays reversible redox behavior of Ru, the Ru-CeO 2 -TiO 2 catalysts show irreversible Ru reduction and a substantially higher fraction of Ce 3+ species under all tested conditions (H 2 , CO 2 , H 2 /CO 2 ). The stabilization of metallic Ru during methanation, together with the enhanced formation of Ce 3+ promoted by TiO 2 through interfacial electronic transfer, accounts for their superior performance. Complementary in situ DRIFTS measurements reveal the formation and rapid consumption of bidentate carbonates and formates. These species act as a key intermediate in methane formation. Overall, these findings highlight the crucial role of the mixed CeO 2 -TiO 2 oxide in tuning the surface chemistry of the catalysts by stabilizing metallic Ru, enhancing ceria reducibility, and promoting efficient reaction pathways for CO 2 methanation. The manipulation of metal↔oxide-oxide interactions can be a very useful tool when dealing with the valorization of CO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Defense Waste Processing Facility Nitric-Glycolic Flowsheet Chemical Process Cell Chemistry: Part 2

The conversions of nitrite to nitrate, the destruction of glycolate, and the conversion of glycolate to formate and oxalate were modeled for the Nitric-Glycolic flowsheet using data from Chemical Process Cell (CPC) simulant runs conducted by Savannah River National Laboratory (SRNL) from 2011 to 2016. The goal of this work was to develop empirical correlation models to predict these values from measurable variables from the chemical process so that these quantities could be predicted a-priori from the sludge or simulant composition and measurable processing variables. The need for these predictions arises from the need to predict the REDuction/OXidation (REDOX) state of the glass from the Defense Waste Processing Facility (DWPF) melter. This report summarizes the work on these correlations based on the aforementioned data. Previous work on these correlations was documented in a technical report covering data from 2011-2015. This current report supersedes this previous report. Further refinement of the models as additional data are collected is recommended. The glass REDOX depends on the concentrations of nitrate and manganese (oxidants), and of glycolate, formate, oxalate, carbon, and antifoam (reductants) in the melter feed. The waste sludge contains nitrite, nitrate, manganese (Mn), and oxalate. Virtually all of the nitrite is converted to nitrate or NO+NO 2 +N 2 O gases in the CPC. The portion of the nitrite converted to nitrate increases the amount of nitrate in the sludge. The amount of glycolate in the final melter feed depends on the amount of the glycolic acid feed that is destroyed. Similarly, the amounts of formate and oxalate formed during the decomposition of glycolic acid are required. The material balance on carbon was found to not close in most cases. Generally, there was less carbon at the end of testing compared to the inputs. The most uncertain product variable was glycolate, so material balances were performed where the glycolate concentration was adjusted, usually upward, to close the balance. Correlation versus the original, as-measured, data was generally poor, but correlation against the material balance adjusted values was greatly improved. It was also shown that the correlation of the measured REDOX versus the predicted REDOX was much better when the material balance adjusted glycolate values were used. Three data series were primarily used during the regressions of the data; these series were 1) Sludge Batch 9 NG flowsheet simulant runs NG51-62 (SB9-NG); 2) Scaled Runs + Bounding Hydrogen Runs (SR+BH); and 3) Runs GN43-50 and 57 (43-50,57). The glycolate destruction was found to correlate with acid stoichiometry (AS), percent reducing acid (PRA), and for some data series, headspace to simulant volume ratio (HSV), mercury (Hg), and nitrate. Although glycolate destruction for pairs of data series (e.g., [SB9-NG] and [SR+BH]) were found to depend on HSV, the combination of all three data series was not found to have significant dependence on this variable. The best model for glycolate destruction depended on AS, nitrate, and Hg. This model predicted the product glycolate compositions of the data to within 92-106%. The conversion of glycolate to formate was high when noble metals and Hg were not present, with values up to 100%. When noble metals and Hg were present, this conversion ranged from zero to 7%, and was dependent on AS. Lower AS gave higher conversions to formate. The conversion to oxalate was found to depend on the AS and the initial concentration of nitrite. An alternative fit versus AS and the form of ruthenium (Ru) used is a possible alternative. This fit was somewhat less statistically significant. This second model predicts that more oxalate is formed when Ru-nitrosyl nitrate is used rather than Ru chloride. The conversion of glycolate to oxalate ranged from zero to 6%. The conversion of nitrite to nitrate depended primarily on AS and PRA, with HSV and Hg being significant when these variables were varied. For multiple series of data, nitrite was also needed to SRNL-STI-2017-00172 5HYLVLRQ viL distinguish between data series, and the effect of HSV became insignificant. The best model for nitrite to nitrate conversion depended on AS, PRA, nitrite, and Hg. The 95% confidence intervals on the predicted values of glycolate destruction, glycolate to oxalate conversion, and nitrite to nitrate conversion were used to determine the uncertainty in the predicted REDOX when starting with only the composition of the sludge, AS, and PRA. Using the 95% confidences on an individual value (that is the confidence in getting a particular value for one single test as opposed to what the mean would be for multiple tests), the uncertainty in the predicted REDOX was calculated. The uncertainty in the actual product composition glycolate, oxalate, formate, and nitrate concentrations translated to an uncertainty in the REDOX value of ±0.1,which is approximately the uncertainty claimed in the REDOX model itself.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

CO Reduction to Ethylene and Cyclopropane via a Trappable Ruthenium Methylidene

Ruthenium complexes based on cis-[Ru(bpy) 2 (CO) 2 ] 2+ (bpy is 2,2′-bipyridine) can reduce CO 2 and CO to C 1 products including methanol, but access to products containing C–C bonds has been elusive. A reaction pathway to convert CO into multicarbon products ethylene and cyclopropane is presented here, along with mechanistic studies elucidating the key intermediates in C–C bond formation. The ruthenium hydroxymethyl complex [Ru(bpy′) 2 (CO)(CH 2 OH)] + (bpy′ = 5,5′-dimethyl-2,2′-bipyridine) undergoes protonolysis to generate the ethylene complex [Ru(bpy′) 2 (CO)(C 2 H 4 )] 2+ even at −80 °C, with free ethylene released upon warming to room temperature. Experimental evidence implicates a highly electrophilic methylidene complex [Ru(bpy′) 2 (CO)(CH 2 )] 2+ as the key intermediate. The methylidene was successfully trapped with nitriles and pyridine, forming adducts (ylide complexes) that each have a unique reactivity profile. With an appropriate nitrile, the adduct can be characterized at low temperature before warming generates ethylene. A more stable pyridine adduct [Ru(bpy′) 2 (CO)(CH 2 pyridine)] 2+ was crystallographically characterized. Even ethylene itself is sufficiently nucleophilic to react with the electrophilic methylidene, revealing a route from CO to the C 3 hydrocarbon cyclopropane. Furthermore, the methods for controlling the reactivity of hydroxymethyl and methylidene complexes toward C–C bond formation can inform the development of CO and CO 2 reduction catalysts.

Carbene Compounds↗