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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Thresholding Analysis and Feature Extraction from 3D Ground Penetrating Radar Data for Noninvasive Assessment of Peanut Yield

This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

A dynamic control approach for energy-efficient production scheduling on a single machine under time-varying electricity pricing

This paper proposes a dynamic control algorithm to enable an energy-aware single machine scheduling under the time-varying electricity pricing policy, in which price rates remain fixed day-to-day over the season. The key issue is to assign a set of jobs to available time periods where different electricity prices are assigned, while considering requested due dates of jobs so as to minimize total penalty costs for earliness and tardiness of jobs and total energy consumption costs, simultaneously. As the first contribution of this study, we develop a new mixed integer nonlinear programming (MINLP) model that aims at determining job arrival times and resulting earliness and tardiness of jobs and energy consumption costs for machine idle and normal processing. Second, an efficient heuristic approach based on continuous-time variable control models and algorithm is developed. The proposed heuristic adaptively changes job arrival times and due dates, which finally determine production sequence over the time periods of different electricity prices, machine turn-off, and machine idle with minimum energy consumption costs and just-in-time (JIT) penalty. Energy and JIT performance of the proposed approach is examined using real energy and machining parameters of a HAAS machine and compared to those of the metaheuristic approach. For relatively large size data groups, the proposed approach incurs about higher energy consumption costs on average, which are offset by up to lower JIT costs, resulting in lower total costs on average compared to the metaheuristic approach. Further, the proposed time-scaled heuristic algorithm yields extremely short computational time, which enables production managers to flexibly select proper production strategies and to implement them for different production environments.

42 ENGINEERING↗
DOE OSTI2022

Functional Traits Resolve Mechanisms Governing the Assembly and Distribution of Nitrogen-Cycling Microbial Communities in the Global Ocean

Microorganisms drive much of the marine nitrogen (N) cycle, which jointly controls the primary production in the global ocean. However, our understanding of the microbial communities driving the global ocean N cycle remains fragmented. Focusing on “who is doing what, where, and how?”, this study draws a clear picture describing the global biogeography of marine N-cycling microbial communities by utilizing the Tara Oceans shotgun metagenomes. The marine N-cycling communities are highly variable taxonomically but relatively even at the functional trait level, showing clear functional redundancy properties. The functional traits and taxonomic groups are shaped by the same set of geo-environmental factors, among which, depth is the major factor impacting marine N-cycling communities, differentiating mesopelagic from epipelagic communities. Latitudinal diversity gradients and distance-decay relationships are observed for taxonomic groups, but rarely or weakly for functional traits. The composition of functional traits is strongly deterministic as revealed by null model analysis, while a higher degree of stochasticity is observed for taxonomic composition. Integrating multiple lines of evidence, in addition to drawing a biogeographic picture of marine N-cycling communities, this study also demonstrated an essential microbial ecological theory—determinism governs the assembly of microbial communities performing essential biogeochemical processes; the environment selects functional traits rather than taxonomic groups; functional redundancy underlies stochastic taxonomic community assembly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Spanning the Core Design Space with Binary Perturbations - A Mathematical Investigation

A reactor core design engineer, even one with many years of experience or one using advanced fuel pattern optimization software, will likely fine tune the pattern before finalizing the design. Fine tuning can further minimize local enthalpy peaks, optimize reactivity to extend burnup, and add additional margin to limits on reactivity coefficients or heat flux. For reactors that have reached an equilibrium fuel cycle, the number of feeds each cycle is typically the similar to previous cycles as is their placement in the core. It is then the engineer's responsibly to efficiently select the placement of burned assemblies to ensure optimal fuel utilization, satisfy safety limits, and meet cycle energy requirements. One of the engineer's most commonly used tools in this fine tuning investigation is to swap the positions of two assemblies, most likely two previously irradiated ('burned') assemblies since their reactivity is lower than fresh fuel. Such an operation is referred to in this work as a binary perturbation or a 'swap'. If quarter core symmetry is maintained, the perturbation translates to the other three quadrants. Likewise if eighth core symmetry is maintained, the perturbation translates to other seven octants. The question is then, how much of the core design space can binary perturbations possibly span? This work seeks to quantitatively define the space spanned by m number of binary perturbations as a function of variable size through combinatorial principles and computer simulation. The results of this study are useful in understanding the in-core fuel management problem faced by both professionals who employ hand manipulations and those who attempt stochastic solutions of the problem.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

The concentration of BTEX in selected urban areas of Malaysia during the COVID-19 pandemic lockdown

Volatile organic compounds (VOCs) such as benzene, toluene, ethylbenzene and xylene (BTEX) are air pollutants that harm human health. This study aims to identify BTEX concentrations before the lockdown known as the Movement Control Order was imposed (BMCO), during the implementation of the Movement Control Order (MCO), and then during the Conditional Movement Control Order (CMCO). These orders were introduced during the COVID-19 pandemic in Malaysia. The study utilised data measured by the continuous monitoring of BTEX using online gas chromatography instruments located at three urban area stations. Here, the results showed that the BTEX concentrations reduced by between –38% and –46% during the MCO compared to the BMCO period. The reduction of human mobility during the MCO and CMCO influenced the lower BTEX concentrations recorded at a station within the Kuala Lumpur area. The results of the BTEX diagnostic ratios and principal component analysis showed that the major source of BTEX, especially during the BMCO and CMCO periods, was motor vehicle emissions. Further investigation, using correlation analysis and polar plots, showed that the BTEX concentrations were also influenced by meteorological variables such as wind speed, air temperature and relative humidity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Artificial neural network based predictions of cetane number for furanic biofuel additives

The next generation of alternative fuels is being explored through advanced chemical and biological production techniques for the purpose of finding suitable replacements for diesel and gasoline while lowering production costs and increasing process yields. Chemical conversion of biomass to fuels offers a plethora of pathways with a variety of fuel molecules, both novel and traditional, which may be targeted. In the search for new fuels, an initial, intuition-driven evaluation of fuel compounds with desired properties is required. Due to the high cost and significant production time needed to synthesize these materials at a scale sufficient for exhaustive testing, a predictive model would allow chemists to preemptively screen fuel properties of potentially desirable fuel candidates. Current work has shown that predictive models, in this case artificial neural networks (ANN's) analyzing quantitative structure property relationships (QSPR's), can predict the cetane number (CN) of a proposed fuel molecule with relatively small error. A fuel's CN is a measure of its ignition quality, typically defined using prescribed ASTM standards and a cetane testing engine. Alternatively, the analogous derived cetane number (DCN), obtained using an Ignition Quality Tester (IQT), is a direct measurement alternative to the CN that uses an empirical inverse relationship to the ignition delay found in the constant volume combustion chamber apparatus. DCN data points acquired using an IQT were utilized for model validation and expansion of the experimental database used in this study. The present report improves on an existing model by optimizing the model architecture along with the key learning variables of the ANN and by making the model more generalizable to a wider variety of fuel candidate types, specifically the class of furans and furan derivatives, by including specific molecules for the model to incorporate. The new molecules considered include tetrahydrofuran, 2-methylfuran, 2-methyltetrahydrofuran, 5,5'-(furan-2-ylmethylene)bis(2-methylfuran), 5,5'-((tetrahydrofuran-2-yl)methylene)bis(2-methyltetrahydrofuran), tris(5-methylfuran-2-yl)methane, and tris(5-methyltetrahydrofuran-2-yl)methane. Model architecture adjustments improved the overall root-mean-square error (RMSE) of the base database predictions by 5.54%. Additionally, through the targeted database expansion, it is shown that the predicted cetane number of the furan-based molecules improves on average by 49.21% (3.74 CN units) and significantly for a few of the individual molecules. This indicates that a selected subset of representative molecules can be used to extend the model's predictive accuracy to new molecular classes. The approach, bolstered by the improvements presented in this paper, enables chemists to focus on promising molecules by eliminating less favorable candidates in relation to their ignition quality.

09 BIOMASS FUELS↗
DOE OSTI2022

Non-destructive analysis of swelling in the EMPIrE fuel test

The European Mini-Plate Irradiation Experiment (EMPIrE) was designed to support the development and testing of a coated uranium-molybdenum (U-Mo) dispersion fuel for the conversion of select high-performance research reactors (HPRRs) to utilize low-enriched uranium (LEU). To aid in the development of the coated fuel form, the EMPIrE test included several plate designs and irradiated them in the Idaho National Laboratory (INL) Advanced Test Reactor (ATR) at a high meat power density (~21 kW/cm 3 ) and to high fuel particle fission densities (~6.4 × 10 21 fissions/cm 3 ). These conditions mimic the bounding conditions of the BR-2 reactor in Belgium, where a concurrent irradiation experiment was performed, and exceed those previously explored in dispersion U-Mo fuel plates. A local fuel swelling analysis, as determined through high-fidelity, post-irradiation mini-plate profilometry, was used along with statistical methods to non-destructively evaluate the overall performance and separate the effects of convoluted fabrication variables. While some effects observed with this non-destructive analysis were subtle, others had more significant, and possibly competing, effects on the fuel swelling behavior. In closing, these observations will be examined further with destructive examinations to more fully assess them as the fuel design is developed and qualified.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

THE PULSATION MODE AND DISTANCE OF THE CEPHEID FF AQUILAE

The determination of pulsation mode and distance for field Cepheids is a complicated problem best resolved by a luminosity estimate. For illustration a technique based on spectroscopic luminosity discrimination is applied to the 4.47 day s-Cepheid FF Aql. Line ratios in high dispersion spectra of the variable yield values of (M{sub V} ) = -3.40 {+-} 0.02 s.e. ({+-}0.04 s.d.), average effective temperature T{sub eff} = 6195 {+-} 24 K, and intrinsic color ((B) - (V)){sub 0} = +0.506 {+-} 0.007, corresponding to a reddening of E{sub B-V} = 0.25 {+-} 0.01, or E{sub B-V}(B0) = 0.26 {+-} 0.01. The skewed light curve, intrinsic color, and luminosity of FF Aql are consistent with fundamental mode pulsation for a small-amplitude classical Cepheid on the blue side of the instability strip, not a sinusoidal pulsator. A distance of 413 {+-} 14 pc is estimated from the Cepheid's angular diameter in conjunction with a mean radius of (R) = 39.0 {+-} 0.7 R{sub Sun} inferred from its luminosity and effective temperature. The dust extinction toward FF Aql is described by a ratio of total-to-selective extinction of R{sub V} = A{sub V} /E(B - V) = 3.16 {+-} 0.34 according to the star's apparent distance modulus.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Managing Zirconium Chemistry and Phase Compatibility in Combined Process Separations for Minor Actinide Partitioning

In response to the NEUP Program Supporting Fuel Cycle R&D Separations and Waste Forms call DEFOA- 0000799, this report describes the results of an R&D project focusing on streamlining separation processes for advanced fuel cycles. An example of such a process relevant to the U.S. DOE FCR&D program would be one combining the functions of the TRUEX process for partitioning of lanthanides and minor actinides from PUREX(UREX) raffinates with that of the TALSPEAK process for separating transplutonium actinides from fission product lanthanides. A fully-developed PUREX(UREX)/TRUEX/TALSPEAK suite would generate actinides as product(s) for reuse (or transmutation) and fission products as waste. As standalone, consecutive unit-operations, TRUEX and TALSPEAK employ different extractant solutions (solvating (CMPO, octyl(phenyl)-N,Ndiisobutylcarbamoylmethylphosphine oxide) vs. cation exchanging (HDEHP, di-2(ethyl)hexylphosphoric acid) extractants), and distinct aqueous phases (2-4 M HNO 3 vs. concentrated pH 3.5 carboxylic acid buffers containing actinide selective chelating agents). The separate processes may also operate with different phase transfer kinetic constraints. Experience teaches (and it has been demonstrated at the lab scale) that, with proper control, multiple process separation systems can operate successfully. However, it is also recognized that considerable economies of scale could be achieved if multiple operations could be merged into a single process based on a combined extractant solvent. The task of accountability of nuclear materials through the process(es) also becomes more robust with fewer steps, providing that the processes can be accurately modeled. Work is underway in the U.S. and Europe on developing several new options for combined processes (TRUSPEAK, ALSEP, SANEX, GANEX, ExAm are examples). There are unique challenges associated with the operation of such processes, some relating to organic phase chemistry, others arising from the variable composition of the aqueous medium. This project targets in particular two problematic issues in designing combined process systems: managing the chemistry of challenging aqueous species (like Zr 4+ ) and optimizing the composition and properties of combined extractant organic phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

The Mechanism of Electron Injection and Acceleration in Transrelativistic Reconnection

Electron acceleration during magnetic reconnection is thought to play a key role in time-variable high-energy emission from astrophysical systems. By means of particle-in-cell simulations of transrelativistic reconnection, we investigate electron injection and acceleration mechanisms in low-β electron–proton plasmas. We set up a diversity of density and field structures (e.g., X-points and plasmoids) by varying the guide field strength and choosing whether to trigger reconnection or let it spontaneously evolve. We show that the number of X-points and plasmoids controls the efficiency of electron acceleration, with more X-points leading to a higher efficiency. Using on-the-fly acceleration diagnostics, we also show that the nonideal electric fields associated with X-points play a critical role in the first stages of electron acceleration. As a further diagnostic, we include two populations of test particles that selectively experience only certain components of electric fields. We find that the out-of-plane component of the parallel electric field determines the hardness of the high-energy tail of the electron energy distribution. These results further our understanding of electron acceleration in this regime of magnetic reconnection and have implications for realistic models of black hole accretion flows.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

AI-based design of a nuclear reactor core

The authors developed an artificial intelligence (AI)-based algorithm for the design and optimization of a nuclear reactor core based on a flexible geometry and demonstrated a 3× improvement in the selected performance metric: temperature peaking factor. The rapid development of advanced, and specifically, additive manufacturing (3-D printing) and its introduction into advanced nuclear core design through the Transformational Challenge Reactor program have presented the opportunity to explore the arbitrary geometry design of nuclear-heated structures. The primary challenge is that the arbitrary geometry design space is vast and requires the computational evaluation of many candidate designs, and the multiphysics simulation of nuclear systems is very time-intensive. Therefore, the authors developed a machine learning-based multiphysics emulator and evaluated thousands of candidate geometries on Summit, Oak Ridge National Laboratory’s leadership class supercomputer. The results presented in this work demonstrate temperature distribution smoothing in a nuclear reactor core through the manipulation of the geometry, which is traditionally achieved in light water reactors through variable assembly loading in the axial direction and fuel shuffling during refueling in the radial direction. The conclusions discuss the future implications for nuclear systems design with arbitrary geometry and the potential for AI-based autonomous design algorithms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

COLORS OF ELLIPTICALS FROM GALEX TO SPITZER

Multi-color photometry is presented for a large sample of local ellipticals selected by morphology and isolation. The sample uses data from the Galaxy Evolution Explorer ( GALEX ), Sloan Digital Sky Survey (SDSS), Two Micron All-Sky Survey (2MASS), and Spitzer to cover the filters NUV , ugri , JHK and 3.6 μ m. Various two-color diagrams, using the half-light aperture defined in the 2MASS J filter, are very coherent from color to color, meaning that galaxies defined to be red in one color are always red in other colors. Comparison to globular cluster colors demonstrates that ellipticals are not composed of a single age, single metallicity (e.g., [Fe/H]) stellar population, but require a multi-metallicity model using a chemical enrichment scenario. Such a model is sufficient to explain two-color diagrams and the color–magnitude relations for all colors using only metallicity as a variable on a solely 12 Gyr stellar population with no evidence of stars younger than 10 Gyr. The [Fe/H] values that match galaxy colors range from −0.5 to +0.4, much higher (and older) than population characteristics deduced from Lick/IDS line-strength system studies, indicating an inconsistency between galaxy colors and line indices values for reasons unknown. The NUV colors have unusual behavior, signaling the rise and fall of the UV upturn with elliptical luminosity. Models with blue horizontal branch tracks can reproduce this behavior, indicating the UV upturn is strictly a metallicity effect.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Quantifying sensitivity in numerical weather prediction-modeled offshore wind speeds through an ensemble modeling approach

A decade of research has shown that numerical weather prediction (NWP)-modeled wind speeds can be highly sensitive to the inputs and setups within the NWP model. For wind resource characterization applications, this sensitivity is often addressed by constructing a range of setups and selecting the one that best validates against observations. However, this approach is not possible in areas that lack high-quality hub height observations, especially offshore wind areas. In such cases, techniques to quantify and disseminate confidence in NWP-modeled wind speeds in the absence of observations are needed. We address this need in the present study and propose best practices for quantifying the spread in NWP-modeled wind speeds. We implement an ensemble approach in which we consider 24 different setups to the Weather Research and Forecasting (WRF) model. We construct the ensemble by considering variations in WRF version, WRF namelist, atmospheric forcing, and sea surface temperature (SST) forcing. Our analysis finds that the standard deviation produces more consistent estimates compared to the interquartile range and tends to be the more conservative estimator for ensemble variability. We further find that model spread increases closer to the surface and on shorter time scales. In conclusion, we explore methods to attribute total ensemble variability to the different ensemble components (e.g., atmospheric forcing and SST product) and find that contributions by components also vary depending on time scale. We anticipate that the methods and results presented in this paper will provide a reasonable foundation for further research into ensemble-based wind resource data sets.

17 WIND ENERGY↗
DOE OSTI2017

Predictive coarse-graining

We propose a data-driven, coarse-graining formulation in the context of equilibrium statistical mechanics. In contrast to existing techniques which are based on a fine-to-coarse map, we adopt the opposite strategy by prescribing a probabilistic coarse-to-fine map. This corresponds to a directed probabilistic model where the coarse variables play the role of latent generators of the fine scale (all-atom) data. From an information-theoretic perspective, the framework proposed provides an improvement upon the relative entropy method and is capable of quantifying the uncertainty due to the information loss that unavoidably takes place during the coarse-graining process. Furthermore, it can be readily extended to a fully Bayesian model where various sources of uncertainties are reflected in the posterior of the model parameters. The latter can be used to produce not only point estimates of fine-scale reconstructions or macroscopic observables, but more importantly, predictive posterior distributions on these quantities. Predictive posterior distributions reflect the confidence of the model as a function of the amount of data and the level of coarse-graining. The issues of model complexity and model selection are seamlessly addressed by employing a hierarchical prior that favors the discovery of sparse solutions, revealing the most prominent features in the coarse-grained model. A flexible and parallelizable Monte Carlo – Expectation–Maximization (MC-EM) scheme is proposed for carrying out inference and learning tasks. A comparative assessment of the proposed methodology is presented for a lattice spin system and the SPC/E water model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Assessing changes in US regional precipitation on multiple time scales

We estimate trends in US regional precipitation on multiple time spans and scales relevant to the detection of changes in climatic regimes. A large literature has shown that trend estimation in hydrological series may be affected by long-term persistence (LTP) and selection of sample length. We show that 2000-year proxy-based reconstructions of the Palmer Modified Drought Index for the US Southeast (SE) and Pacific Coast (PC) regions exhibit LTP and reveal post- 1900 changes to be within the range of longer-term natural fluctuations. We also use a new data base of daily precipitation records for 20 locations (10 PC and 10 SE) extending back in many cases to the 1870s. Over the 1901–2017 interval upward trends in some measures of average and extreme precipitation appear, but they are not consistently significant and in the full records back to 1872 they largely disappear. Furthermore, they also disappear or reverse in the post-1978 portion of the data set, which is inconsistent with them being responses to enhanced greenhouse gas forcing. We conclude that natural variability is likely the dominant driver of historical changes in precipitation and hence drought dynamics in the US SE and PC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Capture of volatile RuO 4 from oxidized simulated used nuclear fuel solutions

Ruthenium is a challenging fission product in used nuclear fuel (UNF) reprocessing due to its complex redox chemistry, variable speciation in nitric acid, partial extractability, and volatility. This work presents a strategy for Ru removal based on the volatilization of RuO 4 using the strong oxidant sodium bismuthate, followed by RuO 2 deposition onto various substrates. Among the materials tested, polymer-based substrates such as polyolefin wax film (Parafilm®), exhibited superior performance, achieving up to near-quantitative Ru removal from solution. After dissolution of the substrate, 99.6% of the Ru was recovered as RuO 2 . The deposition mechanism onto the polyolefin wax film involves both physisorption and chemical reaction through oxidative cleavage of olefinic bonds within the polymer. In contrast, on inorganic substrates such as Al foil, RuO 4 undergoes incomplete surface adsorption and reduction, as confirmed by FTIR and XPS analyses. This approach remains effective under conditions of simulated oxidation of UNF solutions, selectively removing Ru while leaving other fission products in solution. As a result, capture of RuO 4 offers a practical and efficient strategy for ruthenium decontamination and recovery, offering a route for integration into existing UNF reprocessing flowsheets to enhance overall process safety and performance.

Ruthenium↗
DOE OSTI2017

Quantum Monte Carlo with variable spins: Fixed-phase and fixed-node approximations

We study several aspects of the recently introduced fixed-phase spinor diffusion Monte Carlo method, in particular, its relation to the fixed-node method and its potential use as a general approach for electronic structure calculations. We illustrate constructions of spinor-based wave functions with the full space-spin symmetry without assigning up or down spin labels to particular electrons, effectively “complexifying” even ordinary real-valued wave functions for Hamiltonians without spin terms. Interestingly, with proper choice of the simulation parameters and spin variables, such fixed-phase calculations enable one to reach also the fixed-node limit. The fixed-phase approximation has several desirable properties when compared to the fixed-node approximation. The fixed-phase solution provides a straightforward interpretation as the lowest bosonic state in a given effective potential generated by the many-body approximate phase, whereas nodal boundary conditions are defined through less intuitive and complicated hypersurfaces with one dimension less than the original configuration space. In addition, the divergences of the local energy and drift at real wave function nodes are smoothed out to lower dimensionality when the wave function is complexified, thus decreasing the variation of sampled quantities and eliminating artificial nodal domain issues that can occur in the fixed-node formalism. We illustrate some of these properties on calculations of selected first-row systems that recover the fixed-node results with quantitatively similar levels of the corresponding biases. At the same time, the fixed-phase approach opens new possibilities for more general trial wave functions with further opportunities for increasing accuracy in practical calculations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Phase, morphology, elemental composition, and formation mechanisms of biogenic and abiogenic Fe-Cu-sulfide nanoparticles: A comparative study on their occurrences under anoxic conditions

In this paper, we report on a systematic study on the physicochemical attributes of synthetic Fe-Cu-sulfide nanoparticles (NPs) precipitated under conditions similar to the anoxic, low temperature aqueous, sedimentary, soil and subsurface environments where these NPs have been repeatedly identified. Characterizing the basic attributes of these NPs is the first step in understanding their behaviors in various processes including in the bio-availability of essential and toxic metals, environmental remediation and resource recovery. All experiments are performed in the presence and absence of the sulfate-reducer Desulfovibrio vulgaris to elucidate biological controls on NP formation. First, the single-metal endmember NPs are determined by precipitation in solution containing either aqueous Fe(II) or Cu(II). Limited differences are observed between biogenic and abiogenic precipitates aged for up to one month; the Fe-only experiments resulted in 4-10 nm mackinawite (FeS) NPs that aggregate to form nanosheets up to ~1,000 nm in size, while the Cu-only experiments resulted in mixtures of covellite (CuS) NPs comprised of <10 nm fine nanocrystals, 20-40 x 6-9 nm nanorods and ~30 nm nanoplates. The crystal sizes of biogenic mackinawite and covellite are respectively larger and smaller than their abiogenic counterparts, indicating a mineral-specific response to biological presence. Structural defects are observable in the fine nanocrystals and nanorods of covellite in both biogenic and abiogenic experiments, indicative of intrinsic NP instability and formation mechanism via particle attachment. In contrast, covellite nanoplates are defect-free, indicating high stability and potentially rapid recrystallization following particle attachment. Next, mixed-metal sulfide NPs are precipitated at variable initial aqueous Fe-to-Cu ratios (2:1, 1:1 and 1:5). With increasing ratio of Fe-to-Cu, Fe-rich covellite, nukundamite (Cu 5.5 FeS 6.5 ), chalcopyrite (CuFeS 2 ), and Cu-rich mackinawite are formed. The Fe-rich covellite NPs are larger (100-200 nm) than covellite precipitated in the absence of Fe, indicating a role for Fe in promoting crystal growth. Chalcopyrite and nukundamite are formed through incorporation of Fe into precursor covellite NPs while retaining the original crystal morphology, as confirmed by doping a covellite suspension with aqueous Fe(II), resulting in the formation of chalcopyrite and nukundamite within days. Additionally, in the biological systems we observe the recrystallization of mackinawite to greigite (Fe 3 S 4 ) after six months of incubation in the absence of Cu, and the selective formation of chalcopyrite and nukundamite at lower initial Fe-to-Cu ratios compared to abiotic systems. These observations are consistent with NP precipitation that are influenced by the distinct (sub)micro-environments around bacterial cells compared to the bulk solution. Lastly, comparative TEM analyses indicate that the synthetic NPs are morphologically similar to NPs identified in natural environments, opening ways to studying behaviors of natural NPs using experimental approaches.

36 MATERIALS SCIENCE↗