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DOE OSTI2022

My Virtual Cancer

Since each cancer has its own unique characteristics, each one can respond differently to the same treatments. Therefore, the creation of a digital twin (DT) of cancer can assist us in predicting the evolution of an individual's cancer through modeling each tumor's characteristics and response to treatment. Hence, we propose to take advantage of new advances in computational approaches and combine mechanistic, machine learning, and stochastic modeling approaches to create “My Virtual Cancer", a DT platform. To establish a personalized DT, we use patient-specific data for parameter estimations, sensitivity analysis, and uncertainty quantification. For each patient, we will estimate the values of parameters of their QSP model using the patient's data. We perform a multi-dimensional sensitivity analysis and uncertainty quantification on the mechanistic model to find a set of critical interactions and predict the intervals of confidence. Since this QSP model includes the data-driven mechanistic model of cells and molecules' interaction networks, one of the ultimate results of this DT would be the prediction of evolution of tumors.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Evaluation of Visual Analytics Environments: The Road to the Visual Analytics Science and Technology Challenge Evaluation Methodology

The evaluation of visual analytics environments was a topic in Illuminating the Path [Thomas 2005] as a critical aspect of moving research into practice. For a thorough understanding of the utility of the systems available, evaluation not only involves assessing the visualizations, interactions or data processing algorithms themselves, but also the complex processes that a tool is meant to support (such as exploratory data analysis and reasoning, communication through visualization, or collaborative data analysis [Lam 2012; Carpendale 2007]). Researchers and practitioners in the field have long identified many of the challenges faced when planning, conducting, and executing an evaluation of a visualization tool or system [Plaisant 2004]. Evaluation is needed to verify that algorithms and software systems work correctly and that they represent improvements over the current infrastructure. Additionally to effectively transfer new software into a working environment, it is necessary to ensure that the software has utility for the end-users and that the software can be incorporated into the end-user’s infrastructure and work practices. Evaluation test beds require datasets, tasks, metrics and evaluation methodologies. As noted in [Thomas 2005] it is difficult and expensive for any one researcher to setup an evaluation test bed so in many cases evaluation is setup for communities of researchers or for various research projects or programs. Examples of successful community evaluations can be found [Chinchor 1993; Voorhees 2007; FRGC 2012]. As visual analytics environments are intended to facilitate the work of human analysts, one aspect of evaluation needs to focus on the utility of the software to the end-user. This requires representative users, representative tasks, and metrics that measure the utility to the end-user. This is even more difficult as now one aspect of the test methodology is access to representative end-users to participate in the evaluation. In many cases the sensitive nature of data and tasks and difficult access to busy analysts puts even more of a burden on researchers to complete this type of evaluation. User-centered design goes beyond evaluation and starts with the user [Beyer 1997, Shneiderman 2009]. Having some knowledge of the type of data, tasks, and work practices helps researchers and developers know the correct paths to pursue in their work. When access to the end-users is problematic at best and impossible at worst, user-centered design becomes difficult. Researchers are unlikely to go to work on the type of problems faced by inaccessible users. Commercial vendors have difficulties evaluating and improving their products when they cannot observe real users working with their products. In well-established fields such as web site design or office software design, user-interface guidelines have been developed based on the results of empirical studies or the experience of experts. Guidelines can speed up the design process and replace some of the need for observation of actual users [heuristics review references]. In 2006 when the visual analytics community was initially getting organized, no such guidelines existed. Therefore, we were faced with the problem of developing an evaluation framework for the field of visual analytics that would provide representative situations and datasets, representative tasks and utility metrics, and finally a test methodology which would include a surrogate for representative users, increase interest in conducting research in the field, and provide sufficient feedback to the researchers so that they could improve their systems.

evaluation↗

DOE OSTI2015

Femtosecond stimulated Raman spectroscopy by six-wave mixing

Femtosecond Stimulated Raman Spectroscopy (FSRS) is motivated by the knowledge of the molecular geometry changes that accompany sub-picosecond chemical reactions. The detection of vibrational resonances throughout the entire fingerprint region of the spectrum with sub-100-fs delay precision is fairly straightforward to accomplish with the FSRS technique. Despite its utility, FSRS must contend with substantial technical challenges that stem from a large background of residual laser light and lower-order nonlinearities when all laser pulses are electronically resonant with the equilibrium system. In this work, a geometry based on five incident laser beams is used to eliminate much of this undesired background in experiments conducted on metmyoglobin. Compared to a three-beam FSRS geometry with all electronically resonant laser pulses, the five-beam approach described here offers major improvements in the data acquisition rate, sensitivity, and background suppression. The susceptibility of the five-beam geometry to experimental artifacts is investigated using control experiments and model calculations. Of particular concern are undesired cascades of third-order nonlinearities, which are known to challenge FSRS measurements carried out on electronically off-resonant systems. It is generally understood that “forbidden” steps in the desired nonlinear optical processes are the origin of the problems encountered under off-resonant conditions. In contrast, the present experiments are carried out under electronically resonant conditions, where such unfortunate selection rules do not apply. Nonetheless, control experiments based on spectroscopic line shapes, signal phases, and sample concentrations are conducted to rule out significant contributions from cascades of third-order processes. Theoretical calculations are further used to estimate the relative intensities of the direct and cascaded responses. Overall, the control experiments and model calculations presented in this work suggest promise for multidimensional resonance Raman investigations of heme proteins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Application of Machine Learning Algorithms to Identify Problematic Nuclear Data

In this work we aim to show that Machine learning algorithms are promising tools for the identification of nuclear data that contribute to increased errors in transport simulations. We demonstrate this through an application of a machine learning algorithm (Random Forest) to the Whisper/MCNP6 criticality validation library to identify nuclear data that are associated with an increase of the bias (simulated - experimental $k_{eff}$) in the calculations. Specifically, the $k_{eff}$ sensitivity profiles (w.r.t. nuclear data) of 233 U solution benchmarks are used to predict the bias and Shapley Additive Explanations (SHAP) are used to explain how the sensitivities are related to the predicted bias. The SHAP values can be interpreted as sensitivity coefficients of the machine learning model to the $k_{eff}$ sensitivities which are used to make predictions of bias. Using the SHAP values we can identify specific subsets of nuclear data which have the highest probability of influencing bias. We demonstrate the utility of this method by showing how SHAP values were used to identify an inconsistency in the 19 F inelastic scattering nuclear data. The methodology presented here is not limited to transport problems and can be applied to other simulations if there are experimental measurements to compare against, simulations of those experimental measurements, and the ability to calculate sensitivities of the model output with respect to the data inputs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements

Defoliation by herbivorous insects is a widespread forest disturbance driver, affecting global forest health and ecosystem dynamics. Additionally, compared with time- and labor-intensive field surveys, remote sensing provides the only realistic approach to mapping canopy defoliation by herbivorous insects over large spatial and temporal scales. However, the spectral and structural signatures of defoliation by insects at the individual tree level have not been well studied. Additionally, the predictive power of spectral and structural metrics for mapping canopy defoliation has seldom been compared. These critical knowledge gaps prevent us from consistently detecting and mapping canopy defoliation by herbivorous insects across multiple scales. During the peak of a gypsy moth outbreak in Long Island, New York in summer 2016, we leveraged bi-temporal airborne imaging spectroscopy (IS, i.e., hyperspectral imaging) and LiDAR measurements at 1m spatial resolution to explore the spectral and structural signatures of canopy defoliation in a mixed oak-pine forest. We determined that red edge and near-infrared spectral regions within the IS data were most sensitive to crown-scale defoliation severity. LiDAR measurements including B70 (i.e., 70th bincentile height), intensity skewness, and kurtosis were effectively able to detect structural changes caused by herbivorous insects. In addition to canopy leaf loss, increased exposure of understory and non-photosynthetic materials contributed to the detected spectral and structural signatures. Comparing the ability of individual sensors to map canopy defoliation, the LiDAR-only Ordinary Least-Square (OLS) model performed better than the IS-only model (Adj. R-squared = 0.77, RMSE = 15.37% vs. Adj. R- squared = 0.63, RMSE = 19.11%). The IS+LiDAR model improved on performance of the individual sensors (Adj. R-squared = 0.81, RMSE = 14.46%). Our study improves our understanding of spectral and structural signatures of defoliation by herbivorous insects and presents a novel approach for mapping insect defoliation at the individual tree level. Furthermore, with the current and next generation of spaceborne sensors (e.g., WorldView-3, Landsat, Sentinel-2, HyspIRI, and GEDI), higher accuracy and frequent monitoring of insect defoliation may become more feasible across a range of spatial scales, which are critical for ecological research and management of forest resources including the economic consequences of forest insect infestations (e.g., reduced growth and increased mortality), as well as for informing and testing of carbon cycle models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Integration of performance assessments and 3116 basis documents within the Savannah River Site liquid waste facilities - 15375

The end of the Cold War has left a legacy of over 136 million liters (36 million gallons) of radioactive waste in the aging waste tanks at the Department of Energy's Savannah River Site (SRS). A robust program is in place to remove waste from these tanks, treat the waste to separate it into a relatively small volume of high-level waste and a large volume of low-level waste, and to actively dispose of the low-level waste on-site. This process allows the US Department of Energy (DOE) to move forward with closure of the cleaned waste tanks and associated ancillary structures. To support performance-based, risk decision making for closure and disposal activities, three individual performance assessments (PAs) have been developed - salt-stone Disposal Facility, F-Tank Farm and H-Tank Farm. In addition, three Waste Determinations (WDs) have been made by the Secretary of Energy, in consultation with the Nuclear Regulatory Commission (NRC), under the authority of Section 3116 of the Ronald W. Reagan National Defense Authorization Act for Fiscal Year 2005 (NDAA Section 3116). To support these waste determination decisions, individual '3116 Basis Documents' were developed that describe the assumptions and conditions under which the criteria outlined in NDAA Section 3116 are met. The development of each of the PAs represented a long and arduous effort that involved the development of both deterministic and probabilistic conceptual models of the facilities, the development of assumptions and associated ranges, uncertainty and sensitivity analyses, model data collection and analyses, writing of the actual performance assessment, internal and external peer reviews, and ultimately approval by DOE. Similarly, the development of the 3116 Basis Documents represents similar time and financial commitments. At the end of these efforts, after years of preparation and millions of dollars in investment, we have thousands of pages of highly technical text, tables of data and hundreds of graphs and charts all contained in a series of thick binders that fit neatly on the shelf of a bookcase. We were done - right? Actually, we were just getting started. Performance Assessments and 3116 Basis Documents are living documents that shape day-to-day and long-term decision making in the operation of a low-level waste disposal facility (e.g., salt-stone Disposal Facility) or in area closure activities (e.g., F-Tank Farm or H-Tank Farm). Although these documents do not contain specifically described requirements per se, they do utilize specific assumptions that, if changed significantly, could result in no longer being able to demonstrate that there is reasonable assurance that the required performance objectives or the criteria from NDAA Section 3116 can be met. Savannah River Remediation LLC (SRR), the contractor responsible for Liquid Waste operations at SRS, has not only developed the PAs and 3116 Basis Documents to support this work and shepherded these documents through the DOE approval process, but has also developed the processes and procedures to ensure the successful implementation and integration of these documents into day-to-day decision making within the associated facilities. This paper describes these processes, including the Un-reviewed Waste Management Question process and the PA Maintenance process, and provides specific examples of their successful implementation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Introduction to the Bioproduct Transition Dynamics Model

The U.S. Department of Energy, Bioenergy Technologies Office has a broad understanding of different conversion processes that produce bioproducts (chemicals derived from biomass feedstocks) and the market attributes of those processes. However, bioproducts have in many cases proven to be difficult to scale up to commercial production and bring to market, and there is currently a need for greater understanding around the possible successful scenarios for advancing the bioproducts industry. Insight into the myriad factors that impact the success of individual bioproducts and the growth of the bioproducts industry will enable bioproduct stakeholders to evaluate strategies for development and investment that could achieve the greatest impact. An understanding of these factors will also enable the identification of synergies between the bioproducts and biofuels industries that occur through shared learning and co-production. The Bioproduct Transition Dynamics (BTD) model explores questions such as the following: what are the factors that separate a successful, commercially produced bioproduct like succinic acid from one that never progresses beyond bench-scale lab research? Can these factors be influenced by stakeholders, and to what extent? The BTD model uses system dynamics to capture the impacts of investor decision-making, bioproduct techno-economics, and market factors during the early stages of bioproduct development. Key components of the BTD model include techno-economic benchmarks that inform the investor decision-making process, failures and setbacks in development and the resulting need for additional work, and the impacts of effective or ineffective management during each development stage. These factors and their interactions are tracked through bench-scale laboratory research, piloting, demoing, and the construction and operation of the first commercial-scale plant. Bioproduct development is linked to the biofuels industry through a shared-learning model, enabling the benefit to biofuels from bioproduct technology and feedstock supply chain development to be quantified. A wide variety of bioproducts can be analyzed using the BTD model, including bioproducts that have been developed through economic or policy-driven mechanisms, and those with either niche (low volume) or scalable (high volume) markets. The BTD model also has the potential to apply to technology development processes outside the bioproducts industry; background research performed for this project has shown that the early stages of technology development are similar, and impacted by similar factors, regardless of the exact industry. This presentation will cover the BTD model logic, input data, validation process, and results of a sensitivity analysis based on techno-economic data for succinic acid. The sensitivity analysis was performed to identify factors with the largest impact on the eventual success or failure of a bioproduct. Results indicate that management effectiveness, which affects how efficiently money spent is converted into technological advancements, is the single most important factor in determining whether a bioproduct reaches commercial-scale production. The capacity of the first commercial plant and the type of biomass feedstock used, as well as the presence of government support in the form of cost-sharing, were also identified as significant factors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

A System Dynamics Model of Early-Stage Transition Dynamics in the Bioproducts Industry

The U.S. Department of Energy, Bioenergy Technologies Office has a broad understanding of different conversion processes that produce bioproducts (chemicals derived from biomass feedstocks) and the techno-economic attributes of those processes. However, bioproducts have in many cases proven to be difficult to scale up to commercial production, and there is currently a need for greater understanding around the possible successful scenarios for advancing the bioproducts industry. Insight into the myriad factors that impact the success of individual bioproducts and the growth of the bioproducts industry will enable bioproduct stakeholders to evaluate strategies for development and investment that could achieve the greatest impact. An understanding of these factors will also enable the identification of synergies between the bioproducts and biofuels industries that occur through shared learning and co-production. The Bioproduct Transition Dynamics (BTD) model explores questions such as the following: what are the factors that separate a successful, commercially produced bioproduct like succinic acid from one that never progresses beyond bench-scale lab research? Can these factors be influenced by stakeholders, and to what extent? The BTD model uses system dynamics to capture the impacts of investor decision-making, bioproduct techno-economics, and end use factors during the early stages of bioproduct development. Key components of the BTD model include techno-economic benchmarks that inform the investor decision-making process, failures and setbacks in development and the resulting need for additional work, and the impacts of effective or ineffective management during each development stage. These factors and their interactions are tracked through bench-scale laboratory research, piloting, demoing, and the construction and operation of the first commercial-scale plant. Bioproduct development is linked to the biofuels industry through a shared-learning model, enabling the benefit to biofuels from bioproduct technology and feedstock supply chain development to be quantified. A wide variety of bioproducts can be analyzed using the BTD model, including bioproducts that have been developed through economic or policy-driven mechanisms, and those with either niche (low volume) or scalable (high volume) demand. The BTD model also has the potential to apply to technology development processes outside the bioproducts industry; background research performed for this project has shown that the early stages of technology development are similar, and impacted by similar factors, regardless of the exact industry. This presentation will cover the BTD model logic, input data, validation process, and results of a sensitivity analysis based on techno-economic data for succinic acid. The sensitivity analysis was performed to identify factors with the largest impact on the eventual success or failure of a bioproduct. Results indicate that management effectiveness, which affects how efficiently money spent is converted into technological advancements, is the single most important factor in determining whether a bioproduct reaches commercial-scale production. The capacity of the first commercial plant and the type of biomass feedstock used, as well as the presence of government support in the form of cost-sharing, were also identified as significant factors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Nearest neighbour distributions: New statistical measures for cosmological clustering

ABSTRACT The use of summary statistics beyond the two-point correlation function to analyse the non-Gaussian clustering on small scales, and thereby, increasing the sensitivity to the underlying cosmological parameters, is an active field of research in cosmology. In this paper, we explore a set of new summary statistics – the k-Nearest Neighbour Cumulative Distribution Functions (kNN-CDF). This is the empirical cumulative distribution function of distances from a set of volume-filling, Poisson distributed random points to the k-nearest data points, and is sensitive to all connected N-point correlations in the data. The kNN-CDF can be used to measure counts in cell, void probability distributions, and higher N-point correlation functions, all using the same formalism exploiting fast searches with spatial tree data structures. We demonstrate how it can be computed efficiently from various data sets – both discrete points, and the generalization for continuous fields. We use data from a large suite of N-body simulations to explore the sensitivity of this new statistic to various cosmological parameters, compared to the two-point correlation function, while using the same range of scales. We demonstrate that the use of kNN-CDF improves the constraints on the cosmological parameters by more than a factor of 2 when applied to the clustering of dark matter in the range of scales between 10 and $40\, h^{-1}\, {\rm Mpc}$. We also show that relative improvement is even greater when applied on the same scales to the clustering of haloes in the simulations at a fixed number density, both in real space, as well as in redshift space. Since the kNN-CDF are sensitive to all higher order connected correlation functions in the data, the gains over traditional two-point analyses are expected to grow as progressively smaller scales are included in the analysis of cosmological data, provided the higher order correlation functions are sensitive to cosmology on the scales of interest.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

In search of large signals at the cosmological collider

We look for oscillating signals in the primordial bispectrum from new physics heavy particles which are visibly large for next generation large scale structures (LSS) survey. We show that in ordinary inflation scenarios where a slow-rolling inflaton generates density fluctuations and with no breaking of scale invariance or spacetime symmetry, there exist no naturally large signals unless the rolling inflaton generates a parity-odd chemical potential for the heavy particles. We estimate the accessibility of this signal through observations. While current CMB data are already sensitive in the most optimistic scenario, future probes, including LSS survey and 21 cm observation, can cover interesting regions of the model space.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Constraining non-standard neutrino interactions with neutral current events at long-baseline oscillation experiments

We explore, for the first time, neutral-curren t events at long-baseline experiments to constrain vector and axial-vector neutrino non-standard interactions (NSI) with quarks. We leverage the flavor dependence of NSIs to perform an oscillation analysis in the neutral- current channel. We first introduce a framework to parametrize the effect of NSI on the cross section. Then, as an example, we analyze NOvA neutral-current data which provides significantly improved constraints on the axial-vector NSI parameters ${\varepsilon}_{\mu \mu}^A$, ${\varepsilon}_{\tau \tau}^A$ and ${\varepsilon}_{e\mu}^A$. This is highly complementary to constraints from SNO data, which, differently from long-baseline neutral current data, is not sensitive to isospin conserving NSIs ${\varepsilon}_u^A$ = ${\varepsilon}_d^A$. Additionally, we disfavor large values of the diagonal vectorial NSI ${\varepsilon}_{\mu \mu}^{VV}$ and ${\varepsilon}_{\tau \tau}^V$ which originate from the LMA-Dark solution. We also highlight the complementarity between NSI searches at oscillation experiments using charged current and neutral current channels.

neutrino interactions↗

DOE OSTI2020

The second Higgs at the lifetime frontier

We assess the current coverage and the future discovery potential of LHC searches for heavy Higgs bosons decaying into long-lived particles (LLPs), focusing primarily on the production of pairs of LLPs with hadronic final states. These signatures are generic in dark sectors where a heavy scalar decays into pairs of lighter states which subsequently mix with the Standard Model Higgs. We show that a handful of existing analyses provide broad coverage of LLP decay lengths ranging from millimeters to tens of meters, and explore the complementarity between searches for displaced and prompt final states in several simplified models. For both heavy singlet and heavy doublet scalars, LLP searches typically provide the leading sensitivity in current data and exhibit the strongest discovery potential in future LHC runs. We further translate the impact of these searches into the parameter space of various Twin Higgs models, demonstrating that LLP searches are a promising avenue for discovering a Twin Higgs with displaced decays. Finally, we propose a variety of additional search channels that would improve coverage of the second Higgs at the lifetime frontier.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Exotic field theories for (hybrid) fracton phases from imposing constraints in foliated field theory

Fracton phases of matter are gapped phases of matter that, by dint of their sensitivity to UV data, demand non-standard quantum field theories to describe them in the IR. Two such approaches are foliated quantum theory and exotic field theory. In this paper, we explicitly construct a map from one to the other and work out several examples. In particular, we recover the equivalence between the foliated and exotic fractonic BF theories recently demonstrated at the level of operator correspondence. We also demonstrate the equivalence of toric code layers and the anisotropic model with lineons and planons to the foliated BF theory with one and two foliations, respectively. Finally, we derive new exotic field theories that provide simple descriptions of hybrid fracton phases from foliated field theries known to do so. Our results both provide new examples of exotic field theories and pave the way toward their systematic construction from foliated field theories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

Effects of the magnetic field and zinc isotopes on the colony forming ability and elemental composition of E. coli bacterial cells

The combined effects of external weak magnetic fields and nuclear spins of the magnetic isotope {sup 67}Zn on vital functions of E. coli bacterial cells were proved experimentally. Metabolism of the major elements in E. coli depends on the intensity of the external static magnetic field and zinc isotopes contained in the nutrient medium. The combined effects of the external static magnetic field and zinc magnetic isotope {sup 67}Zn are observed in a change in the number of colony-forming units of E. coli bacteria in a range of 25–35 mT and in the content of Na, Ca and Mg in a range of 65–80 mT. The obtained data confirm magnetic sensitivity of intracellular enzymatic processes to the static magnetic field and magnetic moments of atomic nuclei.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Evaluation of Strain–Life Fatigue Curve Estimation Methods and Their Application to a Direct-Quenched High-Strength Steel

Methods to estimate the strain–life curve, which were divided into three categories: simple approximations, artificial neural network-based approaches and continuum damage mechanics models, were examined, and their accuracy was assessed in strain–life evaluation of a direct-quenched high-strength steel. All the prediction methods claim to be able to perform low-cycle fatigue analysis using available or easily obtainable material properties, thus eliminating the need for costly and time-consuming fatigue tests. Simple approximations were able to estimate the strain–life curve with satisfactory accuracy using only monotonic properties. The tested neural network-based model, although yielding acceptable results for the material in question, was found to be overly sensitive to the data sets used for training and showed an inconsistency in estimation of the fatigue life and fatigue properties. The studied continuum damage-based model was able to produce a curve detecting early stages of crack initiation. This model requires more experimental data for calibration than approaches using simple approximations. As a result of the different theories underlying the analyzed methods, the different approaches have different strengths and weaknesses. However, it was found that the group of parametric equations categorized as simple approximations are the easiest for practical use, with their applicability having already been verified for a broad range of materials.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Biogenic sulfide control by nitrate and (per)chlorate – A monitoring and modeling investigation

Biosouring is commonly encountered during secondary oil recovery when seawater or another high sulfate water source is utilized for flooding; as a result, effective souring control is of great interest to the oil industry. Here we describe a laboratory study to evaluate the relative effectiveness of souring interventions through the injection of nitrate, chlorate and perchlorate, collectively (per)chlorate, solutions and whether in-situ galvanic potential measurements can be used for convenient and quantitative tracking of sulfide dynamics. Nitrate has typically been the chemical of choice for souring treatments, while the efficacy of (per)chlorate as a new candidate inhibitor has only been explored recently. (Per)chlorate is known to inhibit oil reservoir souring via mechanisms such as toxicity, bio-competitive exclusion and sulfur redox cycling. Two sets of experiments under different matrix and inoculation conditions were conducted to evaluate treatment efficiency under variable baseline physical and biogeochemical conditions. Our data demonstrated the sensitivity of the galvanic potential signals to sulfide concentrations where the sulfide-galvanic potential correlation is similar to the theoretical predictions based on the Nernst equation, demonstrating the feasibility of using galvanic potential as a quick and economical method for quantifying in situ sulfide concentrations for tracking reservoir souring processes and subsequent intervention effectiveness. Our results show that all three chemicals were effective at suppressing sulfidogenesis. A reactive transport model for perchlorate treatment was developed to simulate the reaction processes and explore the interactions between the underlying competing mechanisms of this inhibitor. A baseline simulation captured the temporal patterns of the effluent chemical species. Subsequent simulations in which individual inhibition mechanisms were systematically removed elucidated the relative role that each inhibition mechanism played at the different phases of the experiment. The simulation results complement the experimental findings. Our study supports the potential advantages of souring control with (per)chlorate treatments, and the application of galvanic signal as an economic, in-situ monitoring approach for tracking souring dynamics and treatment efficacy.

02 PETROLEUM↗

DOE OSTI2016

Limits on axion–photon coupling or on local axion density: Dependence on models of the Milky Way’s dark halo

The μV-scale axion is a compelling cold dark matter candidate. The Axion Dark Matter eXperiment (ADMX) searches for axions by stimulating the decay of galactic dark matter halo axions into detectable microwave photons by their conversion in a resonant cavity permeated by a strong, static magnetic field. The signal depends on properties of the Milky Way’s dark matter halo; the choice of halo model has significant implications for the sensitivity of direct detection searches, e.g., ADMX. This work explores the sensitivity of the data taken by ADMX from 2008 to 2010 to various dark matter halo models. New models for the phase-space distribution of local axions are considered; the analysis demonstrates that certain assumptions about the dark matter halo improve limits on axion–photon coupling. In addition, new ADMX data covering 860–892 MHz are included in the analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2013

Crystal and magnetic structure of (1−x)BiFeO{sub 3}–xSrTiO{sub 3} (x=0.2, 0.3, 0.4 and 0.8)

The effect of doping SrTiO{sub 3} into BiFeO{sub 3} on the magnetic and crystal structure has been explored using powder neutron diffraction for (1−x)BiFeO{sub 3}–xSrTiO{sub 3} where x=0.2, 0.3, 0.4, 0.6 and 0.8. While the data are not sensitive to the cycloidal component of the magnetic ordering, the evolution of the collinear antiferromagnetic moment of the G-type antiferromagnetic component with T and x has been explored, as have structural parameters. It is found that for x≤0.4 pure phase samples form in the R3c cell, and for x=0.8 a non-magnetic Pm3{sup ¯}m phase is obtained. The x=0.6 sample gives a mixed phase. Through the R3c phase the magnetic structure does not change appreciably apart from the reduction of magnetic moment magnitude with the increasing T and/or x. - Graphical abstract: A simple phase diagram for (1−x)BiFeO{sub 3}–xSrTiO{sub 3} , where hexagons indicate the rhombohedral R3c phase and boxes the cubic phase. Filled symbols indicate magnetic ordering. The black arrow indicates T{sub N} for BiFeO{sub 3}. Display Omitted - Highlights: • We have established part of the phase diagram for doping SrTiO{sub 3} into BiFeO{sub 3}. • (1−x)BiFeO{sub 3}–xSrTiO{sub 3} is isostructural with BiFeO{sub 3} up to x<0.4, and cubic for x>0.85. • We have examined the evolution of ordered magnetic moment with T and x.

36 MATERIALS SCIENCE↗