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Löffler, Frank

Publications and source records attributed to Löffler, Frank.

Closing transdisciplinary collaboration gaps of food-energy-water nexus research

The nexus of food, energy, and water (FEW) systems is key to ensuring global sustainability in the face of climate change, population growth, and urbanization. To address FEW resources inequity among different regions and countries, transdisciplinary research networking becomes increasingly important for tackling this intractable, complex grand challenge. In contrast to interdisciplinary or multidisciplinary research, transdisciplinary research emphasizes the interactions of scientific, cognitive, and social factors from a top-down view. Intellectual and strategic integrations entail weaving scientific, socioeconomic, and political perspectives together into a new convergence model and fostering a shared vision and comparable assessment, though these goals might not be realistically achievable in the short term. This article summarizes major barriers to transdisciplinary research on FEW nexus grand challenges and possible solutions to be implemented at multiple levels of distinct social systems. Implementation of the solutions relies on not only top-down incentives of governments but also bottom-up initiatives of academic communities and individual researchers. The relevance of shared interests and visions between the research communities and the public is emphasized.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Impact of microbial iron oxide reduction on the transport of diffusible tracers and non-diffusible nanoparticles in soils

In subsurface bioremediation, electron donor addition promotes microbial Fe(III)-oxide mineral reduction that could change soil pore structure, release colloids, and alter soil surface properties. These processes in turn may impact bioremediation rates and the ultimate fate of contaminants. Columns packed with water-stable, Fe-oxide-rich soil aggregates were infused with acetate-containing artificial groundwater and operated for 20 d or 60 d inside an anoxic chamber. Soluble Fe(II) and soil colloids were detected in the effluent within one week after initiation of the acetate addition, demonstrating Fe(III)-bioreduction and colloid formation. Diffusible Br-, less diffusible 2,6-difluorobenzoate (DFBA), and non-diffusible silica-shelled silver nanoparticles (SSSNP) were used as tracers in transport experiments before and after the bioreduction. The transport of Br- was not influenced by the bioreduction. DFBA showed earlier breakthrough and less tailing after the bioreduction, suggesting alterations in flow paths and soil surface chemistry during the 20-d bioreduction treatment. Similarly, the bioreduction increased the transport of SSSNP very significantly, with mass recovery increasing from 1.7% to 25.1%. Unexpectedly, the SSSNP was completely retained in the columns when the acetate injection was extended from 20 to 60 d, while the mass recovery of DFBA decreased from 89.1% to 84.1% and Br - showed no change. The large change in the transport of SSSNP was attributed to soil aggregate breakdown and colloid release (causing mechanical straining of SSSNP) and the exposure of iron oxide surfaces previously unavailable within aggregate interiors (facilitating attachment of SSSNP). Finally, these results suggest a time-dependent fashion of microbial effect on the transport of diffusivity-varying tracers.

54 ENVIRONMENTAL SCIENCES↗

Sign Learning Kink-based (SiLK) Quantum Monte Carlo for molecular systems

The Sign Learning Kink (SiLK) based Quantum Monte Carlo (QMC) method is used to calculate the ab initio ground state energies for multiple geometries of the H{sub 2}O, N{sub 2}, and F{sub 2} molecules. The method is based on Feynman’s path integral formulation of quantum mechanics and has two stages. The first stage is called the learning stage and reduces the well-known QMC minus sign problem by optimizing the linear combinations of Slater determinants which are used in the second stage, a conventional QMC simulation. The method is tested using different vector spaces and compared to the results of other quantum chemical methods and to exact diagonalization. Our findings demonstrate that the SiLK method is accurate and reduces or eliminates the minus sign problem.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Metaproteomics Identifies the Protein Machinery Involved in Metal and Radionuclide Reduction in Subsurface Microbiomes and Elucidates Mechanisms and U(VI) Reduction Immobilization

The overall goal for this funded project was to develop and exploit environmental metaproteomics tools to identify biomarkers for monitoring microbial activity affecting U speciation at U-contaminated sites, correlate metaproteomics profiles with geochemical parameters and U(VI) reduction activity (or lack thereof), elucidate mechanisms contributing to U(VI) reduction, and provide remediation project managers with additional information to make science-based site management decisions for achieving cleanup goals more efficiently. Although significant progress has been made in elucidating the microbiology contribution to metal and radionuclide reduction, the cellular components, pathway(s), and mechanisms involved in U trans-formation remain poorly understood. Recent advances in (meta)proteomics technology enable detailed studies of complex samples, including environmental samples, which differ between sites and even show considerable variability within the same site (e.g., the Oak Ridge IFRC site). Additionally, site-specific geochemical conditions affect microbial activity and function, suggesting generalized assessment and interpretations may not suffice. This research effort integrated current understanding of the microbiology and biochemistry of U(VI) reduction and capitalize on advances in proteomics technology made over the past few years. Field-related analyses used Oak Ridge IFRC field ground water samples from locations where slow-release substrate biostimulation has been implemented to accelerate in situ U(VI) reduction rates. Our overarching hypothesis was that the metabolic signature in environmental samples, as deciphered by the metaproteome measurements, would show a relationship with U(VI) reduction activity. Since metaproteomic and metagenomic characterizations were computationally challenging and time-consuming, we used a tiered approach that combines database mining, controlled laboratory studies, U(VI) reduction activity measurements, phylogenetic analyses, and gene expression studies to support the metaproteomics characterizations. Growth experiments of target microorganisms (Anaeromyxobacter, Shewanella, Geobacter) revealed tremendous respiratory versatility, as evidenced by the ability to utilize a range of electron donors (e.g. acetate, hydrogen, pyruvate, lactate, succinate, formate) and electron acceptors (e.g. nitrate, fumarate, halogenated phenols, ferric iron, nitrous oxide, etc.). In particular, the dissimilatory metabolic reduction of metals, including radionuclides, by target microorganisms spurred interest for in situ bioremediation of contaminated soils and sediments. Distinct c-type cytochrome expression patterns were observed in target microorganisms grown with the different electron acceptors. For each target microorganism, the core proteome covered almost all metabolic pathways represented by their corresponding pan-proteomes. Unique proteins were detected for each target microorganism, and their expression and possible functionalities were linked to specific growth conditions through proteomics measurements. Optimization of the proteomic tools included in-depth comprehensive metagenomic and metaproteomic analyses on a limited number of samples. The optimized metaproteomic analyses were then applied to Oak Ridge IFRC field samples from the slow-release substrate biostimulation. Metaproteomic analysis and pathway mapping results demonstrated the distinct effects of metal and non-metal growth conditions on the proteome expression. With these metaproteomic tools, we identified which previously hypothetical metabolic pathways were active during the analyzed time points of the slow release substrate biostimulation. Thus, we demonstrated the utility of these tools for site assessment, efficient implementation of bioremediation and long-term monitoring. This research of detailed protein analysis linked with metal reduction activity did (1) show that c-type cytochrome isoforms, previously associated with radionuclide reduction activity, are suitable biomarkers, (2) identify new biomarker targets for site assessment and bioremediation monitoring, and (3) provide new information about specific proteins and mechanisms involved in U(VI) reduction and immobilization. This expanded metagenomic and metaproteomic toolbox contributed to implementing science-driven site management with broad benefits to the DOE mission.

54 ENVIRONMENTAL SCIENCES↗

A survey of high level frameworks in block-structured adaptive mesh refinement packages

Over the last decade block-structured adaptive mesh refinement (SAMR) has found increasing use in large, publicly available codes and frameworks. SAMR frameworks have evolved along different paths. Some have stayed focused on specific domain areas, others have pursued a more general functionality, providing the building blocks for a larger variety of applications. In this survey paper we examine a representative set of SAMR packages and SAMR-based codes that have been in existence for half a decade or more, have a reasonably sized and active user base outside of their home institutions, and are publicly available. The set consists of a mix of SAMR packages and application codes that cover a broad range of scientific domains. We look at their high-level frameworks, their design trade-offs and their approach to dealing with the advent of radical changes in hardware architecture. Finally, the codes included in this survey are BoxLib, Cactus, Chombo, Enzo, FLASH, and Uintah.

97 MATHEMATICS AND COMPUTING↗

Chlorine Isotope Effects from Isotope Ratio Mass Spectrometry Suggest Intramolecular C-Cl Bond Competition in Trichloroethene (TCE) Reductive Dehalogenation

Chlorinated ethenes are prevalent groundwater contaminants. To better constrain (bio)chemical reaction mechanisms of reductive dechlorination, the position-specificity of reductive trichloroethene (TCE) dehalogenation was investigated. Selective biotransformation reactions (i) of tetrachloroethene (PCE) to TCE in cultures of Desulfitobacterium sp. strain Viet1; and (ii) of TCE to cis-1,2-dichloroethene (cis-DCE) in cultures of Geobacter lovleyi strain SZ were investigated. Compound-average carbon isotope effects were -19.0‰ ± 0.9‰ (PCE) and -12.2‰ ± 1.0‰ (TCE) (95% confidence intervals). Using instrumental advances in chlorine isotope analysis by continuous flow isotope ratio mass spectrometry, compound-average chorine isotope effects were measured for PCE (-5.0‰ ± 0.1‰) and TCE (-3.6‰ ± 0.2‰). In addition, position-specific kinetic chlorine isotope effects were determined from fits of reactant and product isotope ratios. In PCE biodegradation, primary chlorine isotope effects were substantially larger (by -16.3‰ ± 1.4‰ (standard error)) than secondary. In TCE biodegradation, in contrast, the product cis-DCE reflected an average isotope effect of -2.4‰ ± 0.3‰ and the product chloride an isotope effect of -6.5‰ ± 2.5‰, in the original positions of TCE from which the products were formed (95% confidence intervals). A greater difference would be expected for a position-specific reaction (chloride would exclusively reflect a primary isotope effect). These results therefore suggest that both vicinal chlorine substituents of TCE were reactive (intramolecular competition). This finding puts new constraints on mechanistic scenarios and favours either nucleophilic addition by Co(I) or single electron transfer as reductive dehalogenation mechanisms.

organohalide respiration↗

From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation

Starting from a high-level problem description in terms of partial differential equations using abstract tensor notation, the Chemora framework discretizes, optimizes, and generates complete high performance codes for a wide range of compute architectures. Chemora extends the capabilities of Cactus, facilitating the usage of large-scale CPU/GPU systems in an efficient manner for complex applications, without low-level code tuning. Chemora achieves parallelism through MPI and multi-threading, combining OpenMP and CUDA. Optimizations include high-level code transformations, efficient loop traversal strategies, dynamically selected data and instruction cache usage strategies, and JIT compilation of GPU code tailored to the problem characteristics. The discretization is based on higher-order finite differences on multi-block domains. Chemora's capabilities are demonstrated by simulations of black hole collisions. This problem provides an acid test of the framework, as the Einstein equations contain hundreds of variables and thousands of terms.

Blazewicz, Marek↗