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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework

Driven by major scientific advances in analytical methods, biomonitoring, and computational exposure assessment, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the computationally enabled “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences. The AEP framework offers an intuitive approach to successful organization of exposure science data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathway and adverse outcome pathways, completing the source to outcome continuum and setting the stage for more efficient integration of exposure science and toxicity testing information. Together these frameworks form and inform a decision making framework with the flexibility for risk-based, hazard-based or exposure-based decisions.

Teeguarden, Justin G.↗
DOE OSTI2026

Enabling robust, stable, and accurate nonlinear optical measurements from squeezed light generated in hot rubidium vapor

The feasibility of nonlinear optical (NLO) imaging and spectroscopy using low intensity quantum states of light including entangled photon and squeezed light sources, such as those driven by two-photon absorption (TPA), has been a topic of ongoing debate. An unambiguous identification of the appreciable quantum advantage in such quantum light applications could enable NLO imaging of biological samples without photodegradation and phototoxicity. Recently, we have constructed a two-mode squeezed light source based on four-wave mixing in 85Rb vapor, which is capable of 7.9 dB of intensity-difference squeezing (IDS), corresponding to 8.7 dB upon electronic noise correction. In this talk, we discuss the stability of our system, including implementation details on achieving and maintaining 8.7 dB of IDS for several hours, and ensuring the two spatially multimode beams are properly overlapped in a sample with minimal optical loss.

Allen, Harry [ORNL] (ORCID:0000000190253914)↗
DOE OSTI2024

Subsurface Energy Systems Mapping Inquiry Tool (MapIT)

The Subsurface Energy Systems Mapping Inquiry Tool (MapIT) is an online web mapping tool designed to help users discover available public-sourced data to facilitate data exploration for subsurface energy exploration and characterization efforts for resource identification (e.g. critical minerals, hydrocarbons, geothermal) as well as injection of geologic sequestration of carbon dioxide (e.g. enhanced oil recovery, saline storage, etc.). Modules within the tool curate data related to geology, faults, fractures, injection and confining zones, hydrologic information, groundwater, groundwater wells, geomechanical and petrophysical data, and geochemical data. User documentation on how to use the tool is also provided. Data have been collected from authoritative national, state, and local sources and made available in this tool. The data is also available as a data catalog and Esri Geodatabase at: https://edx.netl.doe.gov/dataset/mapit-database Disclaimer: There is no guarantee of completeness or appropriateness for individual user’s requirements. Use of this tool is solely at the discretion of the user. See full Federal Disclaimer for further information (https://netl.doe.gov/home/disclaimer). This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. https://www.netl.doe.gov/home/disclaimer

Carbon Sequestration↗
DOE OSTI2016

The Mu2e crystal calorimeter and improvements in the $\mu^-\mbox{N} \to e^-\mbox{N}$ search sensitivity

The Mu2e experiment will search for Charged Lepton Flavor Violation (CLFV) looking at the conversion of a muon into an electron in the field of an aluminum nucleus. About $7\cdot 10^{17}$ muons, provided by a dedicated muon beam line in construction at the Fermi National Accelarator Laboratory (Fermilab), will be stopped in 3 years in the Aluminum target. The corresponding single event sensitivity will be $2.5\cdot 10^{-17}$. The Standard Model of particle physics, even extendend to include the finite neutrino masses, predicts the ratio R μe between muon conversions and muon nuclear captures to be $\sim 10^{- 52}$. Several extensions of the Standard Model predict R μe to be in the range of $10^{-14} - 10^{-18}$. % The current best experimental limit, set by the SINDRUM II experiment is $7 \cdot 10^{-13}$ @ $90\%$ CL. The Mu2e experiment plans to improve this experimental limit by four order of magnitude to test many of the possible extensions of the Standard Model. To reach this ambitious goal, the Mu2e experiment is expected to use an intense pulsed muon beam, and rely on a detector system composed of a straw tube tracker and a calorimeter made of pure CsI crystals. The calorimeter plays a central role in the Mu2e measurement, providing particle identification capabilities that are necessary for rejecting two of the most dangerous background sources that can mimic the μ⁻N → e⁻N conversion electron: cosmic muons and $\bar{p}$ induced background. The calorimeter information allows also to improve the tracking performance. Thanks to a calorimeter-seeded track finder algorithm, it is possible to increase the track reconstruction efficiency, and make it more robust with respect to the occupancy level. Expected performances of the calorimeter have been studied in a beam test at the Beam Test Facility in Frascati (Rome, Italy). A reduced scale calorimeter prototype has been exposed to an electron beam, with energy varying from 80 to 140 MeV, for measuring the timing resolution and validate the Monte Carlo prediction. A timing resolution $\sigma_{\rm t}<200$ ps @ 100 MeV has been obtained. Combination of the background rejection performance, and the improvements in the track reconstruction, have then been used in the calculation of the expected Mu2e sensitivity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1994

The Origins of Polycyclic Aromatic Hydrocarbons: Are They Everywhere?

During the past 15 years considerable progress in observational techniques has been achieved in the middle-infrared region (5000-500 per centimeter, 2-20 micron), the region where most diagnostic molecular vibrations occur. Spectra of many different astronomical infrared sources, some deeply embedded in dark molecular clouds and others at their edges, are now available. These spectra provide a powerful probe, not only for the identification of interstellar molecules in both the gas and solid phases, but also of the physical and chemical conditions which prevail in these two very different domains. The two lectures will focus on the evidence that polycyclic aromatic hydrocarbons (PAHs) are an important, ubiquitous and abundant interstellar species. PAHs are. extremely stable species which can range in size from a few angstroms across to several hundred angstroms (PAHs are also the building blocks of amorphous carbon particles). This identification rests on the suggestive agreement between the laboratory spectra of PAHs with a set of IR emission bands which emanate from many different sources where ultraviolet starlight impinges on a "dusty" region. The picture is that individual PAHs are first pumped into highly vibrationally excited states and relax by fluorescence at their fundamental vibrational frequencies. That PAHs are a ubiquitous interstellar component has serious ramifications in other spectral regions as well, including the strong extinction in the ultraviolet, and the classic visible diffuse interstellar bands discovered more than 50 years ago (but unexplained to this day) The first part of the course will focus on the interpretation of astronomical spectra. The second lecture will start by showing how recent laboratory data on PAHs taken under realistic interstellar conditions has con borated the PAH hypothesis and led to great insight into the conditions in the PAH containing regions. This lecture will end by reviewing the ever-increasing evidence for interstellar PAHs in meteorites and interplanetary dust particles. This in conjunction with the recent suggestion that PAHs are abundant in Jupiter's atmosphere will make the point that "PAHs are Everywhere".

Allamandola, L. J.↗
NASA NTRS1995

[No Title]

The Two Micron All-Sky Survey will result in a database of millions of newly-discovered near-infrared bright sources of all types, from stars to nebulae to distant galaxies. The full potential of thesurvey will be greatly expanded by providing optical (blue and/or visual) identifications to thesesources from existing databases of optical objects from digitized Schmidt plate archives. I willdescribe the procedures developed at IPAC to identify infrared sources in the IRAS Faint SourceSurvey using such optical archives, and how these will be applied to the 2MASS survey. I willsummarize some of the expected results and in particular will discuss a search for distant near-infrared luminous Active Galactic Nuclei in the 2MASS database.

2MASS Survey↗
DOE OSTI2022

Ice-nucleating agents in sea spray aerosol identified and quantified with a holistic multimodal freezing model

Sea spray aerosol (SSA) is a widely recognized important source of ice-nucleating particles (INPs) in the atmosphere. However, composition-specific identification, nucleation processes, and ice nucleation rates of SSA-INPs have not been well constrained. Microspectroscopic characterization of ambient and laboratory-generated SSA confirms that water-borne exudates from planktonic microorganisms composed of a mixture of proteinaceous and polysaccharidic compounds act as ice-nucleating agents (INAs). These data and data from previously published mesocosm and wave channel studies are subsequently used to further develop the stochastic freezing model (SFM) producing ice nucleation rate coefficients for SSA-INPs. The SFM simultaneously predicts immersion freezing and deposition and homogeneous ice nucleation by SSA particles under tropospheric conditions. Predicted INP concentrations agree with ambient and laboratory measurements. In addition, this holistic freezing model is independent of the source and exact composition of the SSA particles, making it well suited for implementation in cloud and climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

BFSVBF (BatFinder Smart Video BioFilter) [SWR-22-87] and Multi-class BatFinder Smart Video BioFilter Keras

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The code has gone through five rounds of development with images used to train the models. This advancement allows for automated real-time data collection, identification, and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. We will discuss the two open source and publicly available machine learning models developed within this scope of this work: 1) a binary model with a 97.5% accuracy in identifying the difference between an object and an empty scene, including wind turbine and clouds; and 2) a multiple classification model with the capability of identifying the type of object detected: bats (90% accuracy), birds (83% accuracy), insects (69% accuracy) and non-biological (99% accuracy).

Yarbrough, John↗
DOE OSTI2021

Combined Mixed Potential Electrochemical Sensors and Artificial Neural Networks for the Quantificationand Identification of Methane in Natural Gas Emissions Monitoring

Sensors capable of quantifying methane concentration and discriminating between possible sources are needed for natural gas leak detection where multiple spatially overlapping sources including wetlands and agriculture may be present. We report on the fabrication by an additive manufacturing process of a four electrode La 0.87 Sr 0.13 CrO 3 , Indium Tin Oxide (In 2 O 3 90 wt%, SnO 2 10 wt%), Au, Pt mixed potential electrochemical sensor using yttria-stabilized zirconia (YSZ) as a solid electrolyte to natural gas detection. Artificial neural networks (ANNs) are used to automatically decode the possible source and concentration of methane. The ANNs trained on sensor data are capable of correctly discriminating between three sources of methane emissions from simulated mixtures of emissions from cattle, wetlands, or natural gas with >98% accuracy. Quantification error for methane in mixtures of CH 4 in air, CH 4 + NH3 in air, and simulated natural gas is less than 1.5% ppm when a two-temperature dataset is employed.

03 NATURAL GAS↗
DOE OSTI2023

Comparison of Machine Learning Algorithms for Natural Gas Identification with Mixed Potential Electrochemical Sensor Arrays

Mixed-potential electrochemical sensor arrays consisting of indium tin oxide (ITO), La 0.87 Sr 0.13 CrO 3 , Au, and Pt electrodes can detect the leaks from natural gas infrastructure. Algorithms are needed to correctly identify natural gas sources from background natural and anthropogenic sources such as wetlands or agriculture. We report for the first time a comparison of several machine learning methods for mixture identification in the context of natural gas emissions monitoring by mixed potential sensor arrays. Random Forest, Artificial Neural Network, and Nearest Neighbor methods successfully classified air mixtures containing only CH 4 , two types of natural gas simulants, and CH 4 +NH 3 with >98% identification accuracy. The model complexity of these methods were optimized and the degree of robustness against overfitting was determined. Finally, these methods are benchmarked on both desktop PC and single-board computer hardware to simulate their application in a portable internet-of-things sensor package. The combined results show that the random forest method is the preferred method for mixture identification with its high accuracy (>98%), robustness against overfitting with increasing model complexity, and had less than 10 ms training time and less than 0.1 ms inference time on single-board computer hardware.

03 NATURAL GAS↗
DOE OSTI2023

Identification of Galaxy Shreds in Large Photometric Catalogs Using Convolutional Neural Networks

Contamination from galaxy fragments, identified as sources, is a major issue in large photometric galaxy catalogs. In this paper, we prove that this problem can be easily addressed with computer vision techniques. We use image cutouts to train a convolutional neural network (CNN) to identify cataloged sources that are in reality just star-formation regions and/or shreds of larger galaxies. The CNN reaches an accuracy ~98% on our testing data sets. We apply this CNN to galaxy catalogs from three among the largest surveys available today: the Sloan Digital Sky Survey, the DESI Legacy Imaging Surveys, and the Panoramic Survey Telescope and Rapid Response System Survey. We find that, even when strict selection criteria are used, all catalogs still show a ~5% level of contamination from galaxy shreds. Our CNN gives a simple yet effective solution to clean galaxy catalogs from these contaminants.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1980

The optical identification of H2252-035 with a cataclysmic variable

An improved position is presented for the X-ray source H2252-033 using the Ariel V Sky Survey Instrument, and precise positioning using the Scanning Modulation Collimator on HEAO-1. The source is identified optically with a 13th magnitude emission-line object, which is probably a white dwarf binary with a well formed accretion disk, with similarities to the dwarf novae (e.g., SS Cyg) at minimum.

Griffiths, R. E.↗
NASA NTRS1994

Identification of a collapsing protostar

The globular molecular cloud B335 contains a single, deeply embedded, far-infrared source. Our recent observations of H2CO and CS lines toward this source provide direct kinematic evidence for collapse. Both the intensity and detailed shape of the line profiles match those expected from inside-out collapse inside a radius of 0.036 pc. The collapse began about 1.5 X 10(exp 5) years ago, similar to the onset of the outflow. The mass accretion rate is about 10 times the outflow rate, and about 0.4 solar mass should have now accumulated in the star and disk. Because B335 rotates only very slowly, any disk would still be very small (about 3 AU). The accretion luminosity should be adequate to power the observed luminosity. Consequently, we believe that B335 is indeed a collapsing protostar.

Evans, Neal J., II↗
NASA NTRS1994

Identification of a Collapsing Protostar

The globular molecular cloud B335 contains a single, deeply embedded far-infrared source. Our recent observations of H2CO and CS lines toward this source provide direct kinematic evidence for collapse. Both the intensity and detailed shape of the line profiles match those expected from inside-out collapse inside a radius of 0.036 pc. The collapse began about 1.5 x 10(exp 5) years ago, similar to the onset of the outflow. The mass accretion rate is about 10 times the outflow rate, and about 0.4 solar mass should have now accumulated in the star and disk. Because B335 rotates only, any disk would still be small (about 3 AU). The accretion luminosity should be adequate to power the observed luminosity. Consequently, we believe that B335 is indeed a collapsing protostar.

Evans, Neal J., II↗
DOE OSTI2021

Automatic Traffic Queue-End Identification using Location-Based Waze User Reports

Traffic queues, especially queues caused by non-recurrent events such as incidents, are unexpected to high-speed drivers approaching the end of queue (EOQ) and become safety concerns. Though the topic has been extensively studied, the identification of EOQ has been limited by the spatial-temporal resolution of traditional data sources. This study explores the potential of location-based crowdsourced data, specifically Waze user reports. It presents a dynamic clustering algorithm that can group the location-based reports in real time and identify the spatial-temporal extent of congestion as well as the EOQ. The algorithm is a spatial-temporal extension of the density-based spatial clustering of applications with noise (DBSCAN) algorithm for real-time streaming data with an adaptive threshold selection procedure. Here, the proposed method was tested with 34 traffic congestion cases in the Knoxville, Tennessee area of the United States. It is demonstrated that the algorithm can effectively detect spatial-temporal extent of congestion based on Waze report clusters and identify EOQ in real-time. The Waze report-based detection are compared to the detection based on roadside sensor data. The results are promising: The EOQ identification time of Waze is similar to the EOQ detection time of traffic sensor data, with only 1.1 min difference on average. In addition, Waze generates 1.9 EOQ detection points every mile, compared to 1.8 detection points generated by traffic sensor data, suggesting the two data sources are comparable in respect of reporting frequency. The results indicate that Waze is a valuable complementary source for EOQ detection where no traffic sensors are installed.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Identification of Novel Microcystins Using High-Resolution MS and MS n with Python Code

Cyanotoxins called microcystins (MCs) are highly toxic and can be present in drinking water sources. Determining the structure of MCs is paramount because of its effect on toxicity. Though over 300 MC congeners have been discovered, many remain unidentified. In this work, a method is described for the putative identification of MCs using liquid chromatography (LC) coupled with high-resolution (HR) Orbitrap mass spectrometry (MS) and a new bottom-up sequencing strategy. Maumee River water samples were collected during a harmful algal bloom and analyzed by LC–MS with simultaneous HRMS and MS/MS. Unidentified ions with characteristic MC fragments (135 and 213 m/z) were recognized as possible novel MC congeners. An innovative workflow was developed for the putative identification of these ions. Python code was written to generate the potential structures of unidentified MCs and to assign ions after the fragmentation for structural confirmation. The workflow enabled the putative identification of eight previously reported MCs for which standards are not available and two newly discovered congeners, MC-HarR and MC-E(OMe)R.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1987

Infrared source cross-index, first edition

The Infrared Source Cross-Index is a listing of correlated infrared source names (and positions) for astronomical objects observed at 1-1000 microns. The source names have been obtained from the database of the first edition of the Catalog of Infrared Observations (CIO: NASA RP 1118), covering observations published through l982. Additional identifications were located by correlating these names with identifications contained in other machine-readable astronomical catalogs in the NASA National Space Science Data Center (NSSDC). There are some 80,000 different source names in the Cross-Index, corresponding to over 27,000 unique infrared sources.

Schmitz, Marion↗