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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Strong parallel evidence of selection during switchgrass sward establishment in hybrid and lowland ecotypes

Switchgrass sward establishment results in up to 90% seedling mortality. The degree of selection during sward establishment has not been reported using modern genetic methods. Pooled leaf samples were sequenced from replicated swards of 46 half-sib families from two breeding groups (lowland and hybrid) before and through 3 years of stand establishment. Pooled allele frequencies were then assessed using fixation indices (Fst) and an independent data set was used to predict the polygenic impact of establishment selection on two traits (heading date and winter survivorship). Last, the DNA pools were assigned survival rankings to predict the sward survival genomically estimated breeding values within the training data set. Strong and parallel selection occured in both breeding groups. Five genomic regions exceeded the significant threshold of 99.9% in >10 families, indicating consistent selection across families and breeding groups. Polygenic trait predictions determined that establishment selection was partially associated with winter survivorship but resulted in variable heading date alterations. The genomewide variation is consistent with selection for a small number of related parental lines. This study observed strong selection for a small number of hybrid and coastal ecotype individuals which are promising germplasm sources for improved sward survival. This confirms prior reports of sward selection during grassland establishment and highlights the strength of pooled DNA sequencing for survival traits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

High Ion Conductive and Selective Membrane Achieved through Dual Ion Conducting Mechanisms

Conventional ion-selective membranes, that is ion-exchange and porous membranes, are unable to perform high conductivity and selectivity simultaneously due to the contradictions between their ion selecting and conducting mechanisms. Here, in this work, a bifunctional ion-selective layer is developed via the combination of nanoporous boron nitride (PBN) and ion exchange groups from Nafion to achieve high ion conductivity through dual ion conducting mechanisms as well as high ion selectivity. A template-free method is adopted to synthesize flake-like PBN, which is further enmeshed with Nafion resin to form the bifunctional layer coated onto a porous polyetherimide membrane. The double-layer membrane exhibits excellent ion selectivity (1.49 × 10 8 mS cm –3 min), which is 22 times greater than that of the pristine porous polyetherimide membrane, with outstanding ion conductivity (64 mS cm –1 ). In a vanadium flow battery, the double-layer membrane achieves a high Coulombic efficiency of 97% and outstanding energy efficiency of 91% at 40 mA cm –2 with a stable cycling performance for over 700 cycles at 100 mA cm –2 . PBN with ion exchange groups may therefore offer a potential solution to the limitation between ion selectivity and conductivity in ion-selective membranes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Selective phosphate removal with manganese oxide composite anion exchange membranes in membrane capacitive deionization

The discharge of excessive phosphorous into water bodies can lead to serious eutrophication threatening aquatic ecosystem. Membrane capacitive deionization (MCDI) is an effective platform for deionizing aqueous streams; however, conventional MCDI is unable to selectively remove targeted ions from a liquid mixture. Here, in this work, we fabricated manganese oxide composite anion exchange membranes (AEMs) for MCDI to enhance phosphate removal selectivity from sodium chloride-sodium dihydrogen phosphate (10:1 M ratio) aqueous mixtures. We systematically investigated several critical factors, such as constant current or voltage operation, applied voltage amount, process stream pH, and manganese oxide (Mn 2 O 3 ) content in the AEM, on phosphate removal efficiency and phosphate selectivity. A trade-off was observed between phosphate removal and selectivity when increasing the cell voltage. Under the best conditions, a MCDI unit with a 20 wt% Mn 2 O 3 composite AEM and a bipolar membrane facilitated high phosphate removal efficiency of ≥ 31.8 % and a phosphate over chloride selectivity of 1.1 while showing stability for at least 30 cycles. To help understand how Mn 2 O 3 composite AEM boosts phosphate selectivity, static electronic structure calculations were performed, and they revelated that hydrogen phosphate absorption on Mn 2 O 3 composite AEM was 314 kcal/mol more exothermic than that on pristine AEM while chloride adsorption on Mn 2 O 3 composite AEM was 2.2 kcal/mol less exothermic than that on a pristine AEM. Overall, this work presents an effective strategy for selectively removing phosphate from model wastewater solutions and the mechanistic understanding that governs ion selectivity in composite ion-exchange membranes used in MCDI.

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

Effect of silver($\mathrm{I}$) ion reduction on the selectivity of olefins, paraffins, and aromatic compounds by gas chromatographic stationary phases consisting of silver($\mathrm{I}$) salts in ionic liquids

We report the olefin/paraffin selectivity offered by ionic liquid (IL) stationary phases can be enhanced through the addition of silver(I) ion, which is well-known to undergo selective complexation with unsaturated compounds. However, such stationary phases often suffer from the loss of chromatographic selectivity as silver(I) ion can be reduced to elemental silver. To maintain the separation performance of silver(I) ion/IL stationary phases, an understanding of factors and conditions that promote the reduction of silver(I) ion is needed. In this study, capillary gas chromatography columns featuring a stationary phase consisting of the 1-decyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide ([C 10 MIM + ][NTf 2 - ]) IL impregnated with [Ag + ][NTf 2 - ] were examined to investigate the effects of temperature, hydrogen content in exposure gas stream, and time of heating/exposure events on olefin selectivity. Retention factors of representative analytes, such as C 6 olefins and paraffins as well as aromatic compounds, were measured after subjecting the columns to the aforementioned conditions, followed by an evaluation of selectivity factors over time. Selectivity factors of olefins and aromatic compounds were observed to decrease significantly when the stationary phases were heated to temperatures higher than 110°C as well as being subjected to mixed gas streams containing greater than 50 mol% of hydrogen. As constant column heating temperatures were applied under exposure gas mixtures containing hydrogen and nitrogen, a gradual decrease in analyte selectivity factors was observed under prolonged periods of time. However, application of a ternary gas mixture comprised of 25/50/25 mol% hydrogen/nitrogen/methane resulted in an increase in the 3-hexyne/cis-2-hexene selectivity when measured at 120°C for 60 h, due to a smaller decrease in the retention factor of 3-hexyne compared to cis-2-hexene.

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

Assessment of the impact of reactor residence time distribution on non-equilibrium product selectivity of polypropylene pyrolysis using reactive molecular dynamics simulations

Mass residence time distribution (RTD) is considered to be an important factor controlling the product selectivity in the pyrolysis of biomass and plastic wastes along with the pyrolysis chemistry. However, due to the complex pyrolysis chemistry of biomass and plastic waste, the coupling between the reaction chemistry, RTD, and product selectivity is challenging to understand. Here, we introduce a reaction molecular dynamics-based method to examine pyrolysis chemistry and species timescales to assess the impact of RTD on product selectivity and yield. To validate this method, reactive molecular dynamics simulations were conducted for polypropylene pyrolysis and its non-equilibrium product selectivity using 6 different RTDs. We find that the RTD and the reaction chemistry control the peak non-equilibrium product concentrations. The peak monomer (C 3 H 6 ) concentration during pyrolysis can be increased by up to 25 % by using a narrow RTD in the case of polypropylene pyrolysis. We also find that product selectivity is strongly affected by the average residence time and RTD. This coupling between the reaction chemistry, RTD, and product selectivity highlights the need to understand detailed reaction chemistry to control RTD and optimize non-equilibrium product selectivity during polymer and biomass pyrolysis. The present method provides a new way to design RTD for reactors to reach maximized product selectivity of plastic waste and biomass.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Interactive Effects of Microbes and Nitrogen on Panicum virgatum Root Functional Traits and Patterns of Phenotypic Selection

Premise of research. Plants in natural and agricultural systems are influenced in myriad ways by their microbial communities, particularly by providing goods and services that change plant functional traits. Microbes are considered an influential part of the environmental context that change plant trait expression, but often, microbe-mediated effects are contingent on local resources, such as nitrogen. Here, we ask how microbes and nitrogen affect belowground functional traits and patterns of phenotypic selection. Methodology. We performed a fully factorial greenhouse experiment with switchgrass (Panicum virgatum), manipulating microbial community composition and nitrogen availability. We measured plant performance and belowground functional traits and performed 16S amplicon sequencing of the root-associated microbial communities. We looked for correlations between microbial taxa and root functional traits, and we performed phenotypic selection analysis on five belowground functional traits to determine how traits affect plant relative performance across biotic and abiotic contexts. Pivotal results. All belowground plant functional traits except root tissue density were affected by adding nitrogen. We found that a microbial taxon (amplicon sequence variant [ASV]) in the genus Micromonospora correlated with shorter root lengths. We also found strong positive selection for longer roots regardless of the abiotic or biotic environment. In contrast, selection favored lower root-to-shoot ratios in high-nitrogen conditions, and selection on root tissue density was highest in treatments that had high nitrogen levels and perturbed microbial communities. Conclusions. We did not detect microbial effects on the expression of plant traits (ecological effects); however, patterns of phenotypic selection (evolutionary effects) on root tissue density differed depending on the biotic and abiotic environment. Additionally, we detected strong selection for increased root length across treatments; we also found that one ASV correlated with decreased root length, indicating potential conflict between root microbiome components and plant fitness. Future work would be to include microbial taxa in phenotypic selection analysis and to conduct manipulations of the microbes correlated with functional traits to determine causality.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Decoy selection for protein structure prediction via extreme gradient boosting and ranking

Background: Identifying one or more biologically-active/native decoys from millions of non-native decoys is one of the major challenges in computational structural biology. The extreme lack of balance in positive and negative samples (native and non-native decoys) in a decoy set makes the problem even more complicated. Consensus methods show varied success in handling the challenge of decoy selection despite some issues associated with clustering large decoy sets and decoy sets that do not show much structural similarity. Recent investigations into energy landscape-based decoy selection approaches show promises. However, lack of generalization over varied test cases remains a bottleneck for these methods. Results: We propose a novel decoy selection method, ML-Select, a machine learning framework that exploits the energy landscape associated with the structure space probed through a template-free decoy generation. The proposed method outperforms both clustering and energy ranking-based methods, all the while consistently offering better performance on varied test-cases. Moreover, ML-Select shows promising results even for the decoy sets consisting of mostly low-quality decoys. Conclusions: ML-Select is a useful method for decoy selection. This work suggests further research in finding more effective ways to adopt machine learning frameworks in achieving robust performance for decoy selection in template-free protein structure prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Application of Multi-Criteria Decision Analysis Techniques for Informing Select Agent Designation and Decision Making

The Centers for Disease Control and Prevention (CDC) Select Agent Program establishes a list of biological agents and toxins that potentially threaten public health and safety, the procedures governing the possession, utilization, and transfer of those agents, and training requirements for entities working with them. Every 2 years the Program reviews the select agent list, utilizing subject matter expert (SME) assessments to rank the agents. In this study, we explore the applicability of multi-criteria decision analysis (MCDA) techniques and logic tree analysis to support the CDC Select Agent Program biennial review process, applying the approach broadly to include non-select agents to evaluate its generality. We conducted a literature search for over 70 pathogens against 15 criteria for assessing public health and bioterrorism risk and documented the findings for archiving. The most prominent data gaps were found for aerosol stability and human infectious dose by inhalation and ingestion routes. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for accuracy, particularly for pathogens with very few known cases, or where proxy data (e.g., from animal models or similar organisms) were used to address data gaps. Analysis of results obtained from a two-dimensional plot of weighted scores for difficulty of attack (i.e., exposure and production criteria) vs. consequences of an attack (i.e., consequence and mitigation criteria) provided greater fidelity for understanding agent placement compared to a 1-to-n ranking and was used to define a region in the upper right-hand quadrant for identifying pathogens for consideration as select agents. A sensitivity analysis varied the numerical weights attributed to various properties of the pathogens to identify potential quantitative (x and y) thresholds for classifying select agents. The results indicate while there is some clustering of agent scores to suggest thresholds, there are still pathogens that score close to any threshold, suggesting that thresholding “by eye” may not be sufficient. The sensitivity analysis indicates quantitative thresholds are plausible, and there is good agreement of the analytical results with select agent designations. A second analytical approach that applied the data using a logic tree format to rule out pathogens for consideration as select agents arrived at similar conclusions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Natural Selection Footprints Among African Chicken Breeds and Village Ecotypes

Natural selection is likely a major factor in shaping genomic variation of the African indigenous rural chicken, driving the development of genetic footprints. Selection footprints are expected to be associated with adaptation to locally prevailing environmental stressors, which may include diverse factors as high altitude, disease resistance, poor nutrition, oxidative and heat stresses. To determine the existence of a selection footprint, 268 birds were randomly sampled from three indigenous ecotypes from East Africa (Rwanda and Uganda) and North Africa (Baladi), and two registered Egyptian breeds (Dandarawi and Fayoumi). Samples were genotyped using the chicken Affymetrix 600K Axiom R Array. A total of 494,332 SNPs were utilized in the downstream analysis after implementing quality control measures. The intra-population runs of homozygosity (ROH) that occurred in >50% of individuals of an ecotype or in >75% of a breed were studied. To identify inter-population differentiation due to genetic structure, FST was calculated for North- vs. East-African populations and Baladi and Fayoumi vs. Dandarawi for overlapping windows (500 kb with a step-size of 250 kb). The ROH and FST mapping detected several selective sweeps on different autosomes. Results reflected selection footprints of the environmental stresses, breed behavior, and management. Intra-population ROH of the Egyptian chickens showed selection footprints bearing genes for adaptation to heat, solar radiation, ion transport and immunity. The high-altitude-adapted East-African populations’ ROH showed a selection signature with genes for angiogenesis, oxygen-heme binding and transport. The neuroglobin gene (GO:0019825 and GO:0015671) was detected on a Chromosome 5 ROH of Rwanda–Uganda ecotypes. The sodium-dependent noradrenaline transporter, SLC6A2 on a Chromosome 11 ROH in Fayoumi breed may reflect its active behavior. Inter-population FST among Egyptian populations reflected genetic mechanisms for the Fayoumi resistance to Newcastle Disease Virus (NDV), while FST between Egyptian and Rwanda–Uganda populations indicated the Secreted frizzled related protein 2, SFRP2, on Chromosome 4, that contributes to melanogenic activity and most likely enhances the Dandarawi chicken adaptation to high-intensity of solar radiation in Southern Egypt. These results enhance our understanding of the natural selection forces role in shaping genomic structure for adaptation to the stressful African conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Longitudinal Antigenic Sequences and Sites from Intra-Host Evolution (LASSIE) identifies immune-selected HIV variants

Within-host genetic sequencing from samples collected over time provides a dynamic view of how viruses evade host immunity. Immune-driven mutations might stimulate neutralization breadth by selecting antibodies adapted to cycles of immune escape that generate within-subject epitope diversity. Comprehensive identification of immune-escape mutations is experimentally and computationally challenging. With current technology, many more viral sequences can readily be obtained than can be tested for binding and neutralization, making down-selection necessary. Typically, this is done manually, by picking variants that represent different time-points and branches on a phylogenetic tree. Such strategies are likely to miss many relevant mutations and combinations of mutations, and to be redundant for other mutations. Longitudinal Antigenic Sequences and Sites from Intrahost Evolution (LASSIE) uses transmitted founder loss to identify virus “hot-spots” under putative immune selection and chooses sequences that represent recurrent mutations in selected sites. LASSIE favors earliest sequences in which mutations arise. Here, with well-characterized longitudinal Env sequences, we confirmed selected sites were concentrated in antibody contacts and selected sequences represented diverse antigenic phenotypes. Finally, practical applications include rapidly identifying immune targets under selective pressure within a subject, selecting minimal sets of reagents for immunological assays that characterize evolving antibody responses, and for immunogens in polyvalent “cocktail” vaccines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Signaling from below: rodents select for deeper fruiting truffles with stronger volatile emissions

Many plant and fungal species use volatile organic compounds (VOCs) as chemical signals to convey information about the location or quality of their fruits or fruiting bodies to animal dispersers. Identifying the environmental factors and biotic interactions that shape fruit selection by animals is key to understanding the evolutionary processes that underpin chemical signaling. Using four Elaphomyces truffle species, we explored the role of fruiting depth, VOC emissions, and protein content in selection by five rodent species. We used stable isotope analysis of nitrogen (δ15N) in truffles to estimate fruiting depth, proton-transfer-reaction mass spectrometry to determine volatile emission composition, and nitrogen concentrations to calculate digestible protein of truffles. We coupled field surveys of truffle availability with truffle spore loads in rodent scat to determine selection by rodents. Despite presumably easier access to the shallow fruiting species, E. americanus (0.5-cm depth) and E. verruculosus (2.5-cm depth), most rodents selected for truffles fruiting deeper in the soil, E. macrosporus (4.1-cm depth) and E. bartlettii (5.0-cm depth). The deeper fruiting species had distinct VOC profiles and produced significantly higher quantities of odiferous compounds. Myodes gapperi (southern red-backed vole), a fungal specialist, also selected for truffles with high levels of digestible protein, E. verruculosus and E. macrosporus. Our results highlight the importance of chemical signals in truffle selection by rodents and suggest that VOCs are under strong selective pressures relative to protein rewards. Strong chemical signals likely allow detection of truffles deep within the soil and reduce foraging effort by rodents. Finally, for rodents that depend on fungi as a major food source, protein content may also be important in selecting truffles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Selecting a near-optimal design for multiple criteria with improved robustness to different user priorities

In a decision-making process, relying on only one objective can often lead to oversimplified decisions that ignore important considerations. Incorporating multiple, and likely competing, objectives is critical for balancing trade-offs on different aspects of performance. When multiple objectives are considered, it is often hard to make a precise decision on how to weight the different objectives when combining their performance for ranking and selecting designs. We show that there are situations when selecting a design with near-optimality for a broad range of weight combinations of the criteria is a better test selection strategy compared with choosing a design that is strictly optimal under very restricted conditions. Here, we propose a new design selection strategy that identifies several top-ranked solutions across broad weight combinations using layered Pareto fronts and then selects the final design that offers the best robustness to different user priorities. This method involves identifying multiple leading solutions based on the primary objectives and comparing the alternatives using secondary objectives to make the final decision. We focus on the selection of screening designs because they are widely used both in industrial research, development, and operational testing. The method is illustrated with an example of selecting a single design from a catalog of designs of a fixed size. However, the method can be adapted to more general designed experiment selection problems that involve searching through a large design space.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Principles of Ion Selection, Alignment, and Focusing in Tandem Ion Mobility Implemented Using Structures for Lossless Ion Manipulations (SLIM)

Tandem ion mobility (IM) enables the characterization of subpopulations of ions from larger ensembles, including differences that cannot be resolved in a single dimension of IM. Tandem IM consists of at least two IM regions that are each separated by an ion selection region. In many implementations of tandem IM, ions eluting from a dimension of separation are filtered and immediately transferred to the subsequent dimension of separation (selection-only experiments). We recently reported a mode of operation in which ions eluting from a dimension are trapped prior to the subsequent dimension (selection-trapping experiments), which was implemented on an instrument constructed using the structures for lossless ion manipulations (SLIM) architecture. Here, we use a combination of experiments and trajectory simulations to characterize aspects of the selection, trapping, and separation processes underlying these modes of operation. For example, the actual temporal profile of filtered ions can be very similar to the width of the waveforms used for selection, but depending on experimental parameters, can differ by up to ± 500 μs. Experiments and simulations indicate that ions in selection-trapping experiments can be spatially focused between dimensions, which removes the broadening that occurred during the preceding dimension. During focusing, individual ions are thermalized, which aligns and establishes common initial conditions for the subsequent dimension. Therefore, selection-trapping experiments appear to offer significant advantages relative to selection-only experiments, which we anticipate will become more pronounced in future experiments that make use of longer IM separations, additional dimensions of analysis, and the outcomes of this study. .

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Azo(xy) vs Aniline Selectivity in Catalytic Nitroarene Reduction by Intermetallics: Experiments and Simulations

Intermetallic nanoparticles are promising catalysts in hydrogenation and fuel cell technologies. Much is known about the ability of intermetallic nanoparticles to selectively reduce nitro vs alkene, alcohol, or halide functional groups; less is known about their selectivity toward aniline vs azo or azoxy condensation products that result from the reduction of a nitro group alone. Because azo(xy)arenes bear promise as dyes, chemical stabilizers, and building blocks to functional materials but can be difficult to isolate, developing high surface area nanoparticle catalysts that display azo(xy) selectivity is desirable. To address this question, we studied a family of nanocrystalline group 10 metal (Pd, Pt)- and group 14 metal (Ge, Sn, Pb)-containing intermetallics-Pd 2 Ge, Pd 2 Sn, Pd 3 Sn 2 , Pd 3 Pb, and PtSn-in the catalytic reduction of nitroarenes. In contrast to monometallic Au, Pt, and Pd nanoparticles and "random" PdxSn1 – x nanoalloys, which are selective for aniline, nanoparticles of atomically precise intermetallic Pd 2 Ge, Pd 2 Sn, Pd 3 Sn 2 , and PtSn prefer an indirect condensation pathway and have a high selectivity for the azo(xy) products. The only exception is Pd 3 Pb, the most active among the intermetallic nanoparticles studied here, which is instead selective for aniline. Employing a novel application of molecular dynamics-based on machine learned potentials within a DeePMD framework-to heterogeneous catalysis, we are able to identify key reaction species on the different types of catalysts employed, furthering our understanding of the unique selectivity of these materials. By demonstrating how intermetallic nanoparticles can be as active yet more selective than other more traditional catalysts, this work provides new physical insights and opens new opportunities in the use of these materials in other important chemical transformations and applications.

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

Methanol Partial Oxidation on Cu(111) and PtCu(111) Single-Atom Alloy Surfaces: Effect of Surface Oxygen Coverage on Selectivity

The selective oxidation of methanol to formaldehyde on Cu surfaces is an important and well-studied reaction. However, a systematic analysis of product selectivity as a function of oxygen coverage on Cu(111) and Cu-based single-atom alloys (SAAs) has not been previously reported. In this work, we present a comprehensive investigation of deuterated methanol (CD 3 OH) partial oxidation on Cu(111) and 1% PtCu(111) SAA surfaces as a function of preadsorbed oxygen coverage. Temperature-programmed desorption (TPD) and X-ray photoelectron spectroscopy (XPS) reveal that isolated Pt atoms inhibit the initial surface oxidation of Cu(111) under low oxygen exposures. Despite this difference in oxidation kinetics, the product selectivity remains largely unaffected: on both Cu(111) and PtCu(111), formaldehyde (CD 2 O) is the predominant partial oxidation product over a broad range of oxygen coverages. The selectivity toward formaldehyde peaks at intermediate oxygen coverages (∼0.3 monolayers, ML), indicating the existence of an optimal oxygen loading for partial oxidation. Notably, the similar product selectivities on Cu(111) and PtCu(111) over a range of surface oxygen coverage indicate that Pt single atoms do not significantly alter the reaction pathway or shift the optimal oxygen coverage for formaldehyde formation. Control experiments confirm that Cu(111) is unreactive toward methanol in the absence of oxygen, while PtCu(111) surfaces produce a small amount of formaldehyde even when oxygen is not preadsorbed, indicating that isolated Pt atoms facilitate O−H activation at below 150 K, leading to H 2 desorption, followed by C−D activation at higher temperatures (∼350 K). Density functional theory (DFT)-based calculations show that Pt atoms increase the O 2 dissociation barrier relative to Cu(111), consistent with the observed inhibition of oxidation at low exposures. Overall, this work provides the first detailed selectivity map for methanol oxidation on oxidized Cu(111) and PtCu(111) SAA surfaces. By linking classical mechanistic insights such as methoxy- and formate-mediated pathways with single-atom alloy catalyst design, this work demonstrates that while Pt substitution modulates the oxidation kinetics and oxygen binding, the overall selectivity toward formaldehyde is governed primarily by oxygen coverage. These findings underscore the potential of isolated dopants to tune surface oxidation behavior without compromising the intrinsic partial oxidation selectivity of copper-based catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Selective Electrocatalytic Reduction of CO 2 into CO at Small, Thiol-Capped Au/Cu Nanoparticles

The electrochemical CO 2 reduction reaction (CO 2 RR) is a promising approach for converting fossil fuel emissions into environmentally sustainable chemicals and fuels. The ability to control the surface structure of CO 2 RR nanocatalysts provides an opportunity to tune product selectivity. Bimetallic gold–copper catalysts have been identified as emerging electrocatalyst candidates, but Cu incorporation typically lowers product selectivity compared with pure Au. Here we show sustained CO selectivity and activity up to 49% Cu content in small (<2 nm), thiol-capped Au/Cu nanoparticles (NPs). Bimetallic NPs containing 49% Cu selectivity converted CO 2 into CO with 100 ± 6% CO Faradaic efficiency and average mass activity of ~500 mA/mg during a 12 h electrolysis experiment at –0.8 V vs RHE. Au/Cu NPs synthesized without thiol ligands selectively produced H 2 , whereas larger (>10 nm), thermally dethiolated Au/Cu NPs produced a wider product distribution including H 2 , CO, and C 2 H 4 . Density functional theory (DFT) modeling of CO 2 RR and H 2 evolution at realistic, thiol-capped Au/Cu NP structures indicated that copper–thiol surface structures sustained CO selectivity by stabilizing key *CO intermediates while making *H binding less favorable. Calculations also predicted that removing a significant fraction of the thiol ligands would increase *CO binding strength such that desorption of CO product molecules could become the most thermodynamically challenging step. This result, coupled with increased *H stability on dethiolated nanoclusters, points to decreased CO 2 RR selectivity for small, ligand-free catalysts, which is in line with experimental observations from our group and others. Here, our results demonstrate that thiol-ligand surface structures can sustain the CO selectivity of bimetallic Au/Cu NPs and reduce precious metal requirements for CO 2 RR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Synthesis of Colloidal Pd/Au Dilute Alloy Nanocrystals and Their Potential for Selective Catalytic Oxidations

Selective oxidations are crucial for the creation of valuable chemical building blocks but often require expensive and unstable stoichiometric oxidants such as hydroperoxides and peracids. To date, many catalysts that contain a single type of active site have not been able to attain the desired level of selectivity for partially oxidized products over total combustion. However, catalysts containing multiple types of active sites have proven to be successful for selective reactions. One category of such catalysts is bimetallic alloys, in which catalytic activity and selectivity can be tuned by modifying the surface composition. Traditional catalyst synthesis methods using impregnation struggle to create catalysts with sufficient control over surface chemistry to accurately tune the ensemble size of the desired active sites. Here in this paper, we describe the synthesis of colloidal nanocrystals of dilute alloys of palladium and gold. We show that when supported on titania (TiO 2 ), tuning the composition of the Pd/Au nanocrystal surface provides a synergistic effect in the selective oxidation of 2-propanol to acetone in the presence of H 2 and O 2 . In particular, we show that certain Pd/Au surface ratios exhibit activity and selectivity far superior to Pd or Au individually. Through precise structural characterization we demonstrate that isolated atoms of Pd exist in the most active catalysts. The synergy between isolated Pd atoms and Au allows for the formation of reactive oxidizing species, likely hydroperoxide groups, responsible for selective oxidation while limiting oxygen dissociation and, thus, complete combustion. This work opens the way to more efficient utilization of scarce noble metals and new options for catalyzed selective oxidations.

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

Validation of emission-line galaxies target selection algorithms for the Dark Energy Spectroscopic Instrument using the MMT Binospec

ABSTRACT The forthcoming Dark Energy Spectroscopic Instrument (DESI) experiment plans to measure the effects of dark energy on the expansion of the Universe and create a 3D map of the Universe using galaxies up to z ∼ 1.6 and QSOs up to z ∼ 3.5. In order to create this map, DESI will obtain spectroscopic redshifts of over 30 million objects; among them, a majority are [O ii] emitting star-forming galaxies known as emission-line galaxies (ELGs). These ELG targets will be pre-selected by drawing a selection region on the g − r versus r − z colour–colour plot, where high-redshift ELGs form a separate locus from the lower redshift ELGs and interlopers. In this paper, we study the efficiency of three ELG target selection algorithms – the Final Design Report (FDR) cut based on the DEEP2 photometry, Number Density Modelling (NDM) and Random Forest – to determine how the combination of these three algorithms can be best used to yield a simple selection boundary that will be best suited to meet DESI’s science goals. To do this, we selected 17 small patches in the DESI footprint where we run the three target selection algorithms to pre-select ELGs based on their photometry. We observed the pre-selected ELGs using the MMT Binospec, which is similar in functionality to the DESI instrument, to obtain their spectroscopic redshifts and fluxes of 1054 ELGs. By analysing the redshift and fluxing distribution of these galaxies, we find that although NDM performed the best, simple changes in the FDR definition would also yield sufficient performance.

79 ASTRONOMY AND ASTROPHYSICS↗