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Processing parameters and machine log data for the MCPC LDRD Agile investment is collected material samples processed. This dataset captures the selected processing parameters, machine logs captured during material processing, and descriptions of how characterization samples were extracted from processed plates of material. The collect characterization data is captured in other datasets.
This dataset is from the Department of Energy Office of Science funded project, Community Research on Urban and Climate Science (CROCUS) (https://crocus-urban.org/). Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements.Datasets are stored in the netCDF data format, and we we encourage users to make use the associated toolkits available from Unidata (https://www.unidata.ucar.edu/software/netcdf/), Project Pythia (https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html), and our “Instrument Cookbooks” (https://crocus-urban.github.io/instrument-cookbooks) for more information on how to process the metadata-rich datasets.
The U.S. Department of Energy (DOE) Oak Ridge Office of Environmental Management (EM-OR) requested that Oak Ridge Associated Universities (ORAU), working under the Oak Ridge Institute for Science and Education (ORISE) contract, provide technical and independent waste management planning support under the American Recovery and Reinvestment Act (ARRA). Specifically, DOE EM-OR requested that ORAU plan and implement a sampling and analysis campaign targeting certain URS|CH2M Oak Ridge, LLC (UCOR) surveillance and maintenance (S&M) process inventory waste. Eight populations of historical and reoccurring S&M waste at the Oak Ridge National Laboratory (ORNL) have been identified in the Waste Handling Plan for Surveillance and Maintenance Activities at the Oak Ridge National Laboratory, DOE/OR/01-2565&D2 (WHP) (DOE 2012) for evaluation and processing to determine a final pathway for disposal. Population 7 (POP 7) consists of 56 containers of aged, low-level and potentially mixed S&M waste that has been staged in various locations around ORNL. Several of these POP 7 containers primarily contain personal protective equipment (PPE) and dry active waste (DAW), but may contain other miscellaneous debris. This data sharing report addresses the Comprehensive Environmental Response, Compensation, and Liability Act of 1980 (CERCLA) specified waste in a 13-container subpopulation (including eight steel boxes, three 55-gal drums, one sealand, and one intermodal) that lacked sufficient characterization data for possible disposal at the Environmental Management Waste Management Facility (EMWMF) using the approved Waste Lot (WL) 108.1 profile.
Microbial degradation of petroleum hydrocarbons is a crucial process for the clean-up of oil-contaminated environments. Cycloclasticus spp. are well-known polycyclic aromatic hydrocarbon (PAH) degraders that possess PAH-degradation marker genes including rhd3α, rhd2α, and pahE. However, it remains unknown if the expression of these genes can serve as an indicator for active PAH degradation. Here, we determined transcript-to-gene (TtG) ratios with (reverse transcription) qPCR in cultures of Cycloclasticus pugetii strain PS-1 grown with naphthalene, phenanthrene, a mixture of these PAHs, or alternate substrates (i.e., no PAHs). Mean TtG ratios of 1.99 × 10 −2 , 1.80 × 10 −3 , and 3.20 × 10 −3 for rhd3α, rhd2α, and pahE, respectively, were measured in the presence or absence of PAHs. The TtG values suggested that marker-gene expression is independent of PAH degradation. Measurement of TtG ratios in Arctic seawater microcosms amended with water-accommodated crude oil fractions, and incubated under in situ temperature conditions (i.e., 1.5°C), only detected Cycloclasticus spp. rhd2α genes and transcripts (mean TtG ratio of 4.15 × 10 −1 ). The other marker genes—rhd3α and pahE—were not detected, suggesting that not all Cycloclasticus spp. carry these genes and a broader yet-to-be-identified repertoire of PAH-degradation genes exists. The results indicate that the expression of PAH marker genes may not correlate with PAH-degradation activity, and transcription data should be interpreted cautiously.
The results from mathematical modeling of a block of reactors of a high-temperature hydrocarbon pyrolysis line are presented. Models of tube furnaces for homogeneous pyrolysis with variously designed coils are described as ideal-displacement reactors. Application of a determinative approach to the reaction mechanism and determination of the kinetic parameters for thermal decomposition of hydrocarbons and their use for describing the processes occurring in SRT-1 and SRT-2 pyrolysis furnaces showed agreement between the calculated relationships and the experimental data.
Powder bed fusion processes commonly introduce voids and defects into components. It has been shown that many of these features and their size and shape characteristics are controllable through process parameter manipulation, but this process is typically time-consuming and expensive. In spite of this, an experiment was developed to explore a wide region of laser powder bed fusion process space, and several void measurements were obtained to characterize component density percentage, pore size, and pore shape. Then, multiple models commonly used in the literature to predict porosity were examined and compared with the experimental data. Using the developed porosity model, a fatigue life prediction framework was adapted, and the predictions were compared to the previously published experimental data. Results of this investigation reveal the strengths and weaknesses of multiple literature models in predicting porosity, and they depict how porosity and resulting fatigue lives vary across process space.
Phase selection in Ti-Zr-Hf-Al high-entropy alloys was investigated by in-situ high-energy x-ray diffraction, single-crystal x-ray diffraction, and KKR-CPA electronic-structure methods that address disorder and vacancies, predicting formation enthalpy and chemical short-range order (SRO). Samples with varying Al content were synthesized, characterized, and computationally assessed to ascertain the composition-dependent phase selection, as increased Al content often acts as a stabilizer of a body-centered-cubic structure. Equiatomic TiZrHfAl was especially interesting due to its observed bcc superstructure – a variant of γ-brass with 4 vacancies per cell (not 2 as in γ-brass). We highlight how vacancy ordering mediates selection of this variant of γ-brass, which is driven by vacancy-atom SRO that dramatically suppress all atomic SRO. As vacancies are inherent in processing refractory systems, we expect that similar discoveries await in other high entropy alloys or in revisiting older experimental data.
In the study of microband formation during plastic deformation of face centered cubic metals and alloys, two theories have been proposed regarding the orientations of their boundaries: (i) they are aligned parallel to crystallographic planes associated with dislocation glide (i.e. (111) planes in FCC metals), or (ii) they are aligned in accordance with the macroscopic stress state generated during deformation. In this study, high resolution 3D electron backscatter diffraction (3D EBSD) was used to investigate the morphology and crystallographic nature of microband boundaries within a 19 × 9 × 8.6 μm volume of a deformed grain in commercial purity aluminum cold rolled to 22% reduction. It was found that microband boundaries correspond to both theories of orientation. Additionally, a single surface may contain both crystallographic and non-crystallographic alignments. Misorientations across boundaries in the regions of microband triple junctions have been identified for both boundary alignments. - Highlights: ► Reconstruction of a 3D volume of crystallographic orientations from EBSD data ► Subgrain features accurately reconstructed using specially designed strategies. ► Microband boundaries contain crystallographic and non-crystallographic alignments. ► Boundaries form by crystallographic process but rotate to non-crystallographic.
Cesium Hafnium Chloride (CHC) is a promising new scintillator for dual mode sensing of both neutrons and gamma-rays. The high chlorine content allows for both 35 Cl(n,p) 35 S and 35 Cl(n,α) 32 P reaction channels leading to reliable detection of fast neutrons. By utilizing pulse shape discrimination (PSD), these neutron interactions may be reliably separated from gamma-ray signals with a high figure-of-merit (FOM) after optimization of the PSD algorithm. Here, in this study, the PSD algorithm settings for CHC were systematically investigated using a bare 252 Cf and a lead shielded plutonium beryllium neutron source. It was found that the PSD algorithm settings affects both the FOM and the neutron detection efficiency, but a FOM as high as 4.5 for alpha particles was observed in one data processing scenario. Further, an intrinsic 5 parts per million alpha emitting contamination was observed in the sample, which we attribute to natural uranium.
Time, energy, and angle correlated neutrons are emitted in nuclear fission. New, physics-based fission event generators that capture these correlations have been incorporated into MCNP6.2 because correlations can impact nuclear non-proliferation and safeguards systems response. DRiFT is a detector response code that takes MCNP outputs and reproduces laboratory analogous detector quantities. Previous work has compared DRiFT to single detector experiment data and has discussed recent updates to DRiFT specific to correlated fission experiments. In this work, we demonstrate a validation of recent updates to DRiFT for organic scintillator response in correlated fission neutron experiments using a 252Cf source. Furthermore, outputs from MCNP with the FREYA fission event generator were post-processed with DRiFT and compared to experiment data. Simulation and experiment results are in reasonably good agreement for multiple correlated neutron quantities.
Here, particle tracking, the analysis of individual moving elements in time series of microscopic images, enables burgeoning new applications, but there is need to better resolve conformation and dynamics. Here we describe the advantages of Delaunay triangulation to extend the capabilities of particle tracking in three areas: (1) discriminating irregularly shaped objects, which allows one to track items other than point features; (2) combining time and space to better connect missing frames in trajectories; and (3) identifying shape backbone. To demonstrate the method, specific examples are given, involving analyzing the time-dependent molecular conformations of actin filaments and λ-DNA. The main limitation of this method, shared by all other clustering techniques, is the difficulty to separate objects when they are very close. This can be mitigated by inspecting locally to remove edges that are longer than their neighbors and also edges that link two objects, using methods described here, so that the combination of Delaunay triangulation with edge removal can be robustly applied to processing large data sets. As common software packages, both commercial and open source, can construct Delaunay triangulation on command, the methods described in this paper are both computationally efficient and easy to implement.
We present meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains (USA) over three consecutive water years with different amounts of snow water equivalent (SWE) accumulation: A year with above average SWE (2019), a year with average SWE (2020), and a year with below average SWE (2021). This data set is distinguished by its emphasis on paired open-forest sites in a continental snow climate. Approximately once a month during February–May, we collected data from 15 to 20 snow pits and took 8 to 19 snow depth transects. Our sampling sites were in open and adjacent forested areas at 3,100 m and in a lower elevation aspen (3,035 m) and higher elevation conifer stand (3,395 m). In total, we recorded 270 individual snow pit density and temperature profiles and over 4,000 snow depth measurements. These data are complimented by continuous meteorological measurements from two weather stations: One in the open and one in the adjacent forest. Meteorology data—including incoming shortwave and longwave radiation, outgoing shortwave radiation, relative humidity, wind speed, snow depth, and air and infrared surface temperature—were quality controlled and the forcing data were gap-filled. These data are available to download from Bonner, Smyth, et al. at three levels of processing, including a level with downscaled, adjusted precipitation based on data assimilation using observed snow depth and a process-based snow model. We demonstrate the utility of these data with a modeling experiment that explores open-forest differences and identifies opportunities for improvements in model representation.
Machine condition monitoring has been proven to reduce machine downtime and increase productivity. The state-of-the-art research uses vibration monitoring for tasks such as maintenance and tool wear prediction. A less explored aspect is how vibration monitoring might be used to monitor equipment sensitive to vibration. In a manufacturing environment, one example of where this might be needed is in monitoring the vibration of optical linear encoders used in high-precision machine tools and coordinate measuring machines. Monitoring the vibration of sensitive equipment presents a unique case for vibration monitoring because an accurate calculation of the maximum sustained vibration is needed, as opposed to extracting trends from the data. To do this, techniques for determining sustained peaks in vibration signals are needed. Here, this work fills this gap by formalizing and testing methods for determining sustained vibration amplitudes. The methods are tested on simulated signals based on experimental data. Results show that processing the signal directly with the novel Expire Timer method produces the smallest amounts of error on average under various test conditions. Additionally, this method can operate in real-time on streaming vibration data.
Purpose: We have implemented a commercially available Radiation Exposure Monitoring System (REMS) to enhance the processes of radiation dose data collection, analysis and alerting developed over the past decade at our sites of practice. REMS allows for consolidation of multiple radiation dose information sources and quicker alerting than previously developed processes. Methods: Thirty-nine x-ray producing imaging modalities were interfaced with the REMS: thirteen computed tomography scanners, sixteen angiography/interventional systems, nine digital radiography systems and one mammography system. A number of methodologies were used to provide dose data to the REMS: Modality Performed Procedure Step (MPPS) messages, DICOM Radiation Dose Structured Reports (RDSR), and DICOM header information. Once interfaced, the dosimetry information from each device underwent validation (first 15–20 exams) before release for viewing by end-users: physicians, medical physicists, technologists and administrators. Results: Before REMS, our diagnostic physics group pulled dosimetry data from seven disparate databases throughout the radiology, radiation oncology, cardiology, electrophysiology, anesthesiology/pain management and vascular surgery departments at two major medical centers and four associated outpatient clinics. With the REMS implementation, we now have one authoritative source of dose information for alerting, longitudinal analysis, dashboard/graphics generation and benchmarking. REMS provides immediate automatic dose alerts utilizing thresholds calculated through daily statistical analysis. This has streamlined our Closing the Loop process for estimated skin exposures in excess of our institutional specific substantial radiation dose level which relied on technologist notification of the diagnostic physics group and daily report from the radiology information system (RIS). REMS also automatically calculates the CT size-specific dose estimate (SSDE) as well as provides two-dimensional angulation dose maps for angiography/interventional procedures. Conclusion: REMS implementation has streamlined and consolidated the dosimetry data collection and analysis process at our institutions while eliminating manual entry error and providing immediate alerting and access to dosimetry data to both physicists and physicians. Brent Stewart has funded research through GE Healthcare.
Results of investigation on spectral-energy thresholds, rates, and mass flow rates of laser ablation of structural materials (Cu, Ti, Zr, Nb, Mo) of high-energy photonics are presented. The data were obtained by means of an experimental diagnostic module developed for study of multifactor interaction processes between UV-near IR femtosecond laser pulse radiation and condensed media in vacuum.
Set of process and control variables corresponding to the cryogenic moderator system at the SNS. The data corresponds to 54 measurement signals (pressure, temperature, flows, valve position, etc.) spanning from 2021/10/20 to 2021/10/21.
Stoichiometric models of metabolism, such as flux balance analysis (FBA), are classically applied to predicting steady state rates - or fluxes - of metabolic reactions in genome-scale metabolic networks. Here we revisit the central assumption of FBA, i.e. that intracellular metabolites are at steady state, and show that deviations from flux balance (i.e. flux imbalances) are informative of some features of in vivo metabolite concentrations. Mathematically, the sensitivity of FBA to these flux imbalances is captured by a native feature of linear optimization, the dual problem, and its corresponding variables, known as shadow prices. First, using recently published data on chemostat growth of Saccharomyces cerevisae under different nutrient limitations, we show that shadow prices anticorrelate with experimentally measured degrees of growth limitation of intracellular metabolites. We next hypothesize that metabolites which are limiting for growth (and thus have very negative shadow price) cannot vary dramatically in an uncontrolled way, and must respond rapidly to perturbations. Using a collection of published datasets monitoring the time-dependent metabolomic response of Escherichia coli to carbon and nitrogen perturbations, we test this hypothesis and find that metabolites with negative shadow price indeed show lower temporal variation following a perturbation than metabolites with zero shadow price. Finally, we illustrate the broader applicability of flux imbalance analysis to other constraint-based methods. In particular, we explore the biological significance of shadow prices in a constraint-based method for integrating gene expression data with a stoichiometric model. In this case, shadow prices point to metabolites that should rise or drop in concentration in order to increase consistency between flux predictions and gene expression data. In general, these results suggest that the sensitivity of metabolic optima to violations of the steady state constraints carries biologically significant information on the processes that control intracellular metabolites in the cell.
The sulfate-reducing bacteria (SRB) are soil bacteria that share two common characteristics, strict anaerobiosis and the ability to respire sulfate. The metabolic activities of these bacteria play significant roles in the global sulfur cycle, anaerobic degradation of biomass, biological metal corrosion in the environment and, recently, degradation of toxic compounds. The accumulation of evidence suggests these bacteria are also key to the production of the neurotoxin methylmercury in environmental settings. We propose to use our experience with the development of genetics in sulfate-reducing bacteria of the genus Desulfovibrio to create mutations that will eliminate the methylation of mercury, thereby identifying the genes essential for this process. This information may allow the environmental monitoring of the mercury methylation potential to learn the location and quantity of the production this toxin. From these data, more accurate predictive models of mercury cycling can be generated.