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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Quantifying EGS Reservoir Complexity with an Integrated Geophysical Approach-Improved Resolution Ambient Seismic Noise Interferometry

The primary goal of the project was met, as improved resolution (from kilometers to tens/hundreds of meters and to a depth of at least 3 km) was added to the seismic interferometry-estimated parameters relative to the work reported in AltaRock Energy Inc. (2014) for the Dixie Valley Geothermal Wellfield. A procedure for initial fault identification, using seismic interferometry, was developed. The project team further improved, and applied a new, cost-effective, and non-invasive exploration method, developed and validated since 2010, during two previous experiments, at Dixie Valley and Soda Lake, Nevada. The method, initially designed for hydrothermal exploration, was adapted to EGS exploration conditions, which require deeper crustal information. Regions of elevated energy for both the 2-7 Hz and 5-15 Hz frequency bands are located above intersections of multiple faults. Thus, an unprecedented, innovative seismic field deployment and ambient seismic noise and signal processing were successfully used to estimate crustal structure and to identify fault locations at depths up to 3 km, at a cost per kilometer of survey that in some cases may be as much as ten times less than the cost of a controlled-source survey. Based on valid and useful seismic data collected and analyzed during Phase I, in Phase II the team has further quantified statistically the advantage of using seismic analysis, in combination with other geological and geophysical techniques, for EGS target identification. A power spectral density analysis of the passive-seismic empirical Green’s functions reveals significantly higher power recorded over the exploration area at the power plant, and in the drilling area, near the BB station 10201, especially on the horizontal components. In this study, the most important relationship observed during the prior report, namely the one between velocity and temperature was explored. The areas that show anomalously high predicted temperature in the geostatistics occur where multiple faults intersect. Not surprisingly, such areas span between the Dixie Valley range front fault and the basin-ward Piedmont faults, in the vicinity of numerous wells. These locations also fall in the area of high EGS favorability from the prior 2014 study. The anomalies also coincide with the high power spectral density values. The result of altering all favorability criteria for the new data reached a maximum of 1/8th of a point and were not large enough to change any favorability value reported in the 2014 study. The new data reinforce the results obtained from the previous 2014 study. The current high-resolution ambient seismic noise project provided excellent results in defining faults in the wellfield. As such the identification of known faults by the project demonstrated the utility of this method for identifying faults at an exploration stage in geothermal development. Such fault identification, while extremely useful in exploration investigation, has not added sufficient new data for the generation of significantly different DVGW favorability maps in the current project. We believe the cost savings of our passive-seismic technique for EGS, over common industry active-source survey techniques, may have a minimum of 30%, and a maximum of 90%. There are many significant non-cost benefits of passive seismic surveys. Our passive-source method provides an environmentally-friendly seismic survey with minimal to negligible impacts. The economic benefit is greater EGS project feasibility.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Detection of Anomalies in Gamma Background Radiation Data with K-Means and Self-Organizing Map Clustering Algorithms (Consortium on Nuclear Security Technologies (CONNECT) Q1 Report)

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. The challenge is that spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

PRIMED for the Future: Purposing Raw Intake for Machine Learning-Enabled Detection (Final Report)

The COVID-19 pandemic demonstrated how a novel, elusive, and diffuse biological threat can engender uncertainty and misinformation, and it underscored the need for flexible analytical modalities agnostic to the identity of biological material. Yet even before the pandemic, recognition of the limitations of the current, list-based approach, which focuses on known pathogens and biotoxins, and of the importance of agent-agnostic biodetection was growing within the biosecurity community. In a 2018 report on “Biodefense in the Age of Synthetic Biology,” for example, the National Academy of Sciences stated that “an overreliance on the Select Agent List is a systematic weakness affecting many aspects of the United States’ current biodefense mitigation capability”. More recently, a group of biodefense researchers proposed the identification and adoption of “bioagent-agnostic signatures (BASs)” as a way of detecting and characterizing not only existing agents but also novel ones, an approach they believe will “enable a more flexible and resilient biodefense posture”. Indeed, the future of biodetection requires us to begin developing novel analytics that can identify anomalies and/or characteristics that indicate a potential threat, whether known or unknown, without looking for a specific signature that has been identified previously. To assess potential threats more rapidly, it is critical to develop agnostic artificial intelligence (AI)/machine learning (ML) systems that can be employed for real-time assessment of the nature and source of a perturbation. Such systems should be multi scale and multi-dimensional, integrating sensor data from a range of biological, chemical, and physical application spaces. Emerging deep learning (DL) models demonstrate exceptional promise for identification of discriminatory features within multi-dimensional datasets. DL models have the capacity to recognize and encode highly complex patterns in a wide range of input data modalities, including images, text, and biological/chemical/physical spectra. As such, they can execute a wide range of assessments and determinations that have traditionally required a human operator. The promise of advances in DL is apparent in the realm of human health and medicine. DL models have been validated for evaluating a variety of clinical threats to human health in a range of contexts, including infection and cancer, and they demonstrated improved performance in predicting stroke relative to human neurologists in some categories of data. Continuously evolving advances in AI/ML are expected to support more efficient evaluation of raw sequence, spectroscopy, and spectrometry data. For instance, recent advances and deployment of large language models (LLM) such as Generative Pre training Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) have already motivated application of these models for biological function prediction. As frameworks such as LLMs become larger and more complex in their representations, their capacity to serve as pre-trained models that can be fine-tuned for biological/biodetection purposes will similarly be amplified. While existing and emerging AI/ML have found broad applicability and use cases in the clinical sciences, development for environmental evaluation and biodetection has been limited. Functionalizing such capabilities for this purpose requires an understanding of the existing technical landscape and how the respective tools and algorithms are currently being employed. This landscape awareness then allows an assessment of the current practical capabilities of existing models and the anticipated requirements and development efforts that will be needed to adapt available algorithms for biodetection applications relevant to DHS. Leveraging expertise in biodetection, ML, and operational biodetection, the effort described in this report is comprised of a systematic landscape assessment (Subtask 2.1), comparative evaluation (Subtask 2.2), and formulation of a value proposition (Subtask 2.3) for the prospect of ML-enabled, agnostic biodetection from raw, or minimally-processed, datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Electrocatalytic CN Coupling: Advances in Urea Synthesis and Opportunities for Alternative Products

Urea is an essential fertilizer produced through the industrial synthesis of ammonia (NH 3 ) via the Haber–Bosch process, which contributes ≈1.2% of global annual CO 2 emissions. Electrocatalytic urea synthesis under ambient conditions via CN coupling from CO 2 and nitrogen species such as nitrate (NO 3 − ), nitrite (NO 2 − ), nitric oxide (NO), and nitrogen gas (N 2 ) has gained interest as a more sustainable route. However, challenges remain due to the unclear reaction pathways for urea formation, competing reactions, and the complexity of the resulting product matrix. This review highlights recent advances in catalyst design, urea quantification, and intermediate identification in the CN coupling reaction for electrocatalytic urea synthesis. Furthermore, this review explores future prospects for industrial CN coupling, considering potential nitrogen and carbon sources and examining alternative CN coupling products, such as amides and amines.

Chemistry↗
DOE OSTI2015

Ion chamber absorbed dose calibration coefficients, N{sub D,w}, measured at ADCLs: Distribution analysis and stability

Purpose: To analyze absorbed dose calibration coefficients, N{sub D,w}, measured at accredited dosimetry calibration laboratories (ADCLs) for client ionization chambers to study (i) variability among N{sub D,w} coefficients for chambers of the same type calibrated at each ADCL to investigate ion chamber volume fluctuations and chamber manufacturing tolerances; (ii) equivalency of ion chamber calibration coefficients measured at different ADCLs by intercomparing N{sub D,w} coefficients for chambers of the same type; and (iii) the long-term stability of N{sub D,w} coefficients for different chamber types by investigating repeated chamber calibrations. Methods: Large samples of N{sub D,w} coefficients for several chamber types measured over the time period between 1998 and 2014 were obtained from the three ADCLs operating in the United States. These are analyzed using various graphical and numerical statistical tests for the four chamber types with the largest samples of calibration coefficients to investigate (i) and (ii) above. Ratios of calibration coefficients for the same chamber, typically obtained two years apart, are calculated to investigate (iii) above and chambers with standard deviations of old/new ratios less than 0.3% meet stability requirements for accurate reference dosimetry recommended in dosimetry protocols. Results: It is found that N{sub D,w} coefficients for a given chamber type compared among different ADCLs may arise from differing probability distributions potentially due to slight differences in calibration procedures and/or the transfer of the primary standard. However, average N{sub D,w} coefficients from different ADCLs for given chamber types are very close with percent differences generally less than 0.2% for Farmer-type chambers and are well within reported uncertainties. Conclusions: The close agreement among calibrations performed at different ADCLs reaffirms the Calibration Laboratory Accreditation Subcommittee process of ensuring ADCL conformance with National Institute of Standards and Technology standards. This study shows that N{sub D,w} coefficients measured at different ADCLs are statistically equivalent, especially considering reasonable uncertainties. This analysis of N{sub D,w} coefficients also allows identification of chamber types that can be considered stable enough for accurate reference dosimetry.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Special Nuclear Material Mass Estimates from Neutron Singles Count Rate [Poster]

The objective of this research was to create an algorithm to provide an estimate of special nuclear material (SNM) mass using only neutron count rate data from a Radioisotope Identification Device (RIID), rather than using time-correlated data from a neutron multiplicity counter. To meet this objective neutron count rate measurements of a 252 Cf source were taken at varying distances with an ORTEC Detective X, FLIR Identifinder 2, and an ORTEC RADEAGLET-R. An algorithm was created to estimate mass of SNM utilizing the singles rate equation and the measured absolute efficiency curves.

FLIR↗
DOE OSTI2025

Cosmological Simulations of Stellar Halos with Gaia Sausage–Enceladus Analogs: Two Sausages, One Bun?

Observations of the Milky Way’s stellar halo find that it is predominantly comprised of a radially biased population of stars, dubbed the Gaia Sausage–Enceladus, or GSE. These stars are thought to be debris from dwarf galaxy accretion early in the Milky Way’s history. Though typically considered to be from a single merger, it is possible that the GSE debris has multiple sources. To investigate this possibility, we use the IllustrisTNG50 simulation to identify stellar accretion histories in 98 Milky Way analogs—the largest sample for which such an identification has been performed—and find GSE-like debris in 32, with two-merger GSEs accounting for a third of these cases. Distinguishing single-merger GSEs from two-merger GSEs is difficult in common kinematic spaces, but differences are more evident through chemical abundances and star formation histories. This is because single-merger GSEs are typically accreted more recently than the galaxies in two-merger GSEs: the median infall times (with 16th and 84th percentiles) are $5.{9}_{-2.0}^{+3.3}$ and $10.{7}_{-3.7}^{+1.2}$ Gyr ago for single- and two-merger scenarios, respectively. The systematic shifts in abundances and ages that occur as a result suggest that efforts in modeling these aspects of the stellar halo prove ever-important in understanding its assembly.

galactic archaeology↗
NASA NTRS1984

Optical observations of the BL Lacertae object PKS 2155-304 and implications regarding the X-ray absorption feature at 600-700 eV

The interpretation of the PKS 2155-304 identification as a BL Lac object whose X-ray spectrum contains an absorption trough has been hampered by the lack of a reliable redshift for the source. Spectra derived from extensive optical observations of this source imply a redshift of 0.117 + or - 0.002. With knowledge of the redshift, the range of mechanisms which can produce the X-ray trough can be reduced. Attention is given to these alternative mechanisms.

Bowyer, S.↗
NASA NTRS1987

X-ray selected high redshift clusters of galaxies

Studies of the evolution of X-ray emission from clusters of galaxies have so far used distant clusters discovered optically and then observed at X-ray wavelengths (Henry et al., 1982; Henry and Lavery, 1984). A different approach to the study of cluster evolution is to use clusters selected directly by their X-ray emission, since X-ray selection is extremely successful at discovering high-redshift clusters (up to z = 0.5). The Extension of the Medium Sensitivity Survey, a study of serendipitous X-ray sources discovered with the Einstein Imaging Proportional Counter, has so far provided a sample of more than 60 clusters with relatively high redshifts. Upon completion of the identification programs the sample will include over 100 clusters. Here the data currently available are presented, and the basic properties of this new sample of clusters of galaxies are discussed. A preliminary number-counts distribution is also derived.

Gioia, Isabella M.↗
NASA NTRS1989

Plasma observations near Neptune - Initial results from Voyager 2

The plasma science experiment on Voyager 2 made observations of the plasma environment in Neptune's magnetosphere and in the surrounding solar wind. Because of the large tilt of the magnetic dipole and fortuitous timing, Voyager entered Neptune's magnetosphere through the cusp region, the first cusp observations at an outer planet. Thus the transition from the magnetosheath to the magnetosphere observed by Voyager 2 was not sharp but rather appeared as a gradual decrease in plasma density and temperature. The maximum plasma density observed in the magnetosphere is inferred to be 1.4 per cubic centimeter (the exact value depends on the composition), the smallest observed by Voyager in any magnetosphere. The plasma has at least two components; light ions (mass, 1 to 5) and heavy ions (mass, 10 to 40), but more precise species identification is not yet available. Most of the plasma is concentrated in a plasma sheet or plasma torus and near closest approach to the planet. A likely source of the heavy ions is Triton's atmosphere or ionosphere, whereas the light ions probably escape from Neptune. The large tilt of Neptune's magnetic dipole produces a dynamic magnetosphere that changes configuration every 16 hours as the planet rotates.

Belcher, J. W.↗
NASA NTRS1993

Mineralogic variability of the Kelso Dunes, Mojave Desert, California derived from Thermal Infrared Multispectral Scanner (TIMS) data

Mineral identification and mapping of alluvial material using thermal infrared (TIR) remote sensing is extremely useful for tracking sediment transport, assessing the degree of weathering and locating sediment sources. As a result of the linear relation between a mineral's percentage in a given area (image pixel) and the depth of its diagnostic spectral features, TIR spectra can be deconvolved in order to ascertain mineralogic percentages. Typical complications such as vegetation, particle size and thermal shadowing are minimized upon examination of dunes. Actively saltating dunes contain little to no vegetation, are very well sorted and lack the thermal shadows that arise from rocky terrain. The primary focus of this work was to use the Kelso Dunes as a test location for an accuracy analysis of temperature/emissivity separation and linear unmixing algorithms. Accurate determination of ground temperature and component discrimination will become key products of future ASTER data. A decorrelation stretch of the TIMS image showed clear color variations within the active dunes. Samples collected from these color units were analyzed for mineralogy, grain size, and separated into endmembers. This analysis not only revealed that the dunes contained significant mineralogic variation, but were more immature (low quartz percentage) than previously reported. Unmixing of the TIMS data using the primary mineral endmembers produced unique variations within the dunes and may indicate near, rather than far, source locales for the dunes. The Kelso Dunes lie in the eastern Mojave Desert, California, approximately 95 km west of the Colorado River. The primary dune field is contained within a topographic basin bounded by the Providence, Granite Mountains, with the active region marked by three northeast trending linear ridges. Although active, the dunes appear to lie at an opposing regional wind boundary which produces little net movement of the crests. Previous studies have estimated the dunes range from 70% to 90% quartz mainly derived from a source 40 km to the west. The dune field is assumed to have formed in a much more arid climate than present, with the age of the deposit estimated at greater than 100,000 years.

Ramsey, Michael S.↗
NASA NTRS1995

A serach for 'failed clusters' of galaxies

We describe a search for a new type of object - large clouds of hot gas with no visible galaxies - which we call failed clusters of galaxies. We calculate the expected X-ray luminosity, temperature, and angular diameter of such objects as a function of total cloud mass and convert the results to expected X-ray fluxes from failed clusters at different redshifts. Using the Einstein Imaging Proportional Counter (IPC) database, we establish a strategy to search for candidate failed clusters. From this initial screening of 1435 IPC fields, 17 candidates are selected for more detailed analysis, which indicates that 10 of these are very probably extended X-ray sources. Optical follow-up on the 10 prime candidates finds eight clusters of galaxies (including six reproted for the first time in this paper), one stellar identification, and one without an obvious optical counterpart (the candidate with the weakest evidence for X-ray extent). Investigation of several candidates with less evidence for X-ray extent yields two additional new clusters of galaxies. A conservative comparison of our results with the Einstein Extended Medium Sensitivity Survey demonstrates that failed clusters are a relatively unimportant contributor to the mass density of the universe. Our inability to find failed clusters is consistent with the hierarchical clustering scenario for the formation of galaxies and clusters.

Tucker, W. H.↗
NASA NTRS2008

The Presence of Methane in the Atmosphere of an Extrasolar Planet

Molecules present in the atmospheres of extrasolar planets are expected to influence strongly the balance of atmospheric radiation, to trace dynamical and chemical processes, and to indicate the presence of disequilibrium effects. As molecules have the potential to reveal atmospheric conditions and chemistry, searching for them is a high priority. The rotational-vibrational transition bands of water, carbon monoxide and methane are anticipated to be the primary sources of non-continuum opacity in hot-Jupiter planets. As these bands can overlap in wavelength, and the corresponding signatures from them are weak, decisive identification requires precision infrared spectroscopy. Here we report a near-infrared transmission spectrum of the planet HD 189733b that shows the presence of methane. Additionally, a resolved water vapour band at 1.9 (micro)m confirms the recent claim4 of water in this object. On thermochemical grounds, carbon monoxide is expected to be abundant in the upper atmosphere of hot-Jupiter planets, but is not identifiable here; therefore the detection of methane rather than carbon monoxide in such a hot planet could signal the presence of a horizontal chemical gradient away from the permanent dayside, or it may imply an ill-understood photochemical mechanism that leads to an enhancement of methane.

atmospheric radiation↗
DOE OSTI2025

The ICEBERG Test Stand for DUNE Cold Electronics Development

ICEBERG is a liquid argon time projection chamber at Fermilab for the purpose of testing detector components and software for the Deep Underground Neutrino Experiment (DUNE). The detector features a 1.15m x 1m anode plane following the specifications of the DUNE horizontal drift far detector and a newly installed X-ARAPUCA photodetector. The status of ICEBERG is reported along with analysis of noise, pulser, and cosmic ray data from the ninth run beginning May 2024 with the goal of advising the DUNE collaboration on the optimal wire readout electronics configuration. In addition, development of an absolute energy scale calibration method is currently underway using known sources such as cosmic ray muon Michel electrons at the ~10 MeV scale and $^{39}$Ar decay electrons at the ~100keV scale. Research into AI-based identification of such events at the data acquisition level is introduced.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2008

Root Source Analysis/ValuStream[Trade Mark] - A Methodology for Identifying and Managing Risks

Root Source Analysis (RoSA) is a systems engineering methodology that has been developed at NASA over the past five years. It is designed to reduce costs, schedule, and technical risks by systematically examining critical assumptions and the state of the knowledge needed to bring to fruition the products that satisfy mission-driven requirements, as defined for each element of the Work (or Product) Breakdown Structure (WBS or PBS). This methodology is sometimes referred to as the ValuStream method, as inherent in the process is the linking and prioritizing of uncertainties arising from knowledge shortfalls directly to the customer's mission driven requirements. RoSA and ValuStream are synonymous terms. RoSA is not simply an alternate or improved method for identifying risks. It represents a paradigm shift. The emphasis is placed on identifying very specific knowledge shortfalls and assumptions that are the root sources of the risk (the why), rather than on assessing the WBS product(s) themselves (the what). In so doing RoSA looks forward to anticipate, identify, and prioritize knowledge shortfalls and assumptions that are likely to create significant uncertainties/ risks (as compared to Root Cause Analysis, which is most often used to look back to discover what was not known, or was assumed, that caused the failure). Experience indicates that RoSA, with its primary focus on assumptions and the state of the underlying knowledge needed to define, design, build, verify, and operate the products, can identify critical risks that historically have been missed by the usual approaches (i.e., design review process and classical risk identification methods). Further, the methodology answers four critical questions for decision makers and risk managers: 1. What s been included? 2. What's been left out? 3. How has it been validated? 4. Has the real source of the uncertainty/ risk been identified, i.e., is the perceived problem the real problem? Users of the RoSA methodology have characterized it as a true bottoms up risk assessment.

Brown, Richard Lee↗
DOE OSTI2024

Multiple aspects maintenance ontology-based intelligent maintenance optimization framework for safety-critical systems

Abstract Maintenance optimization is a process for improving the efficiency of maintenance strategies and activities, considering various aspects of the target system and components, such as the probabilities of system failures and the cost of repair and replacement of a failed component. The improvement of maintenance optimization algorithms generally requires information from various data sources. For example, it may require the system risk information derived from risk analysis tools or the residual lifetime of a component from fault prognosis tools. The requirements of data acquisition (DAQ) and aggregation pose new challenges for maintenance management systems (MMSs) that implement and use these maintenance optimization algorithms. This paper proposes a multiple aspects maintenance ontology-based framework to facilitate DAQ from MMSs, online monitoring systems, fault detection and discrimination tools, risk assessment tools, decision-making tools, and component identification tools, and accelerate the implementation and verification of contemporary maintenance optimization models and algorithms. The proposed framework consists of a multi-aspect maintenance ontology with critical information for maintenance optimization and application interfaces for collecting information from various data sources, such as fault prognosis tools, online monitoring tools, risk assessment tools, and decision-making algorithms. In addition, this paper proposes a heuristic method for integrating concepts and properties from other existing ontologies into the proposed framework when the existing ontology is not fully compatible with the ontology under construction. Finally, the paper verifies the proposed ontology framework using a feedwater system designed for nuclear power plants with valves and filters as the components under maintenance.

Diao, Xiaoxu (ORCID:0000000346726352)↗
DOE OSTI2018

Chapter 3: Energy Systems

The focus of SOCCR-2 is on the scientific understanding of U.S. and North American carbon cycle stocks and fluxes, in the context of and interactions with global scale budgets and climate change impacts in managed and unmanaged systems. Carbon stocks and fluxes in soils, water (including oceans), vegetation, aquatic-terrestrial interfaces (coastal/ estuaries/ wetlands), human settlements, agriculture, forestry etc. are included. This chapter assesses the contribution of the North American energy system to the global carbon cycle, including the identification of pathways to greater energy efficiency with lower emissions. The system - defined by energy-related activities in Canada, Mexico, and the United States - includes primary energy sources; the infrastructure to extract, transport, convert, transmit, distribute, and use these resources; and the socioeconomic and political structures and dynamics associated with these processes (Romero-Lankao et al., 2014).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1993

A search for the rotational transitions of H2D+ at 1370 GHz and H3O+ at 985 GHz

A search was made for the 1370 GHz lowest rotational transition of the molecular ion H2D+ in NGC 2264, W3, and the IRc2 region of M42. No emission lines were seen, but an absorption feature was detected toward IRc2. The column density and fractional abundance were calculated using a tentative identification of the line as the transition of para H2D+. The LSR velocity and the measured line width are consistent with the dynamical parameters of the hot core source. Physical parameters deduced from the data differ from those derived from millimeter-wave observations of the hot core condensation. It is suggested that significant amounts of low-density gas are associated with this region and that the material is cold enough for enhanced deuterium fractionation to occur. A search was also made for the 985 GHz transition of ortho H3O+ in W3 and IRc2 with negative results.

Boreiko, R. T.↗