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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Attraction and Longevity of 2- and 3-Component Food Cone Lures for the Caribbean Fruit Fly, Anastrepha suspensa (Diptera: Tephritidae)

Abstract The Caribbean fruit fly, Anastrepha suspensa (Loew) (Diptera: Tephritidae), is a quarantine pest of Citrus spp. and a production pest of guava and other specialty fruits in Florida. Effective monitoring lures and traps are needed for early pest detection and timely initiation of control measures. As part of a continued effort to identify attractive synthetic lures for the Caribbean fruit fly, we conducted field tests in Homestead, Florida to compare the efficacy and longevity of commercial 2- and 3-component cone lures (2C [ammonium acetate and putrescine], 3C [ammonium acetate, putrescine, and trimethylamine]), the current standards used by regulatory agencies, versus the traditional liquid protein bait consisting of hydrolyzed torula yeast and borax as a positive control. Additional lures were also field-aged and periodically brought into the laboratory to quantify residual chemical contents. Traps baited with the torula yeast-borax mixture captured the highest mean number of A. suspensa, and traps baited with the commercial 2C lures captured more flies than the 3C lures. Traps baited with torula yeast-borax also captured the highest number of nontarget Diptera. Captures with all three treatments were significantly biased toward females. Attractiveness of the 2C lure began to drop after 6–8 wk, and the 3C lure after 5–6 wk. Overall, these data suggest that the 2C cone lure is more attractive to A. suspensa than the 3C cone lure under field conditions in south Florida, and that the 2C lures are attractive for up to 8 wk.

Entomology↗
DOE OSTI2026

Measurement of the 73 As ⁢(𝑛,𝛾) cross section at thermal neutron energies

A measurement of the thermal neutron capture cross section of 73 As (t 1/2 = 80.3 d) was performed utilizing the University of Missouri Research Reactor. Two sealed quartz tubes containing 73 As were prepared and characterized before being irradiated for ≈1 week. The resulting production of 74 As was quantified with γ-ray spectroscopy. Monitor foils of Fe, Zr, and Mo were irradiated alongside the 73 As samples to assess the thermal and resonance region neutron fluxes. In conclusion, the thermal 73 As ⁢(𝑛,𝛾) cross section was determined to be 56.1 ± 8.9 b based on the average of the two 73 As samples and represents the first neutron-induced reaction cross section measured on 73 As.

and nuclear chemistry↗
DOE OSTI2024

Drilling Down I/O Bottlenecks with Cross-layer I/O Profile Exploration

I/O performance monitoring tools such as Darshan and Recorder collect I/O-related metrics on production systems and help understand the applications' behavior. However, some gaps prevent end-users from seeing the whole picture when it comes to detecting and drilling down to the root causes of I/O performance slowdowns and where those problems originate. These gaps arise from limitations in the available metrics, their collection strategy, and the lack of translation to actionable items that could advise on optimizations. This paper highlights such gaps and proposes solutions to drill down to the source code level to pinpoint the root causes of I/O bottlenecks scientific applications face by relying on cross-layer analysis combining multiple performance metrics related to I/O software layers. We demonstrate with two real applications how metrics collected in high-level libraries (which are closer to the data models used by an application), enhanced by source-code insights and natural language translations, can help streamline the understanding of I/O behavior and provide guidance to end-users, developers, and supercomputing facilities on how to improve I/O performance. Using this cross-layer analysis and the heuristic recommendations, we attained up to 6.9× speedup from run-as-is executions.

Ather, Hammad↗
DOE OSTI2025

In situ neutron and synchrotron diffraction of La0.9Sr0.1Co1-yFeyO3 (y=0, 0.25, 0.75, 1)

Powder La0.9Sr0.1Co1-yFeyO3 (y=0, 0.25, 0.75, 1) samples were probed via in situ neutron (POWGEN, Spallation Neutron Source, Oak Ridge National Laboratory) and synchrotron (11-BM, Advanced Photon Source, Argonne National Lab) diffraction cycling between methane and air at temperatures ranging from room temperature to 850 C. Mail in neutron data (POWGEN) was collected on the samples at room temperature and 10 K. Additionally, gas production under similar reaction conditions as the in situ diffraction as monitored by Gasboard 3100 (California State University, Fullerton) is also included.

catalysis↗
DOE OSTI2023

Litterfall and Branchfall Mass Flux in Malaysia Lambir Hills GEM plots (2008-2010)

This data package includes two folders: Litterfall and Branchfall. The Litterfall folder includes three files: "GEM_Lambir_Hills_Litterfall_Mass_Flux_Data_2008_to_2010.csv" includes the litterfall data, the "GEM_Lambir_Hills_Litterfall_Metadata.xlx" includes the descripion of the columns in the data file, and the PDF file "GEM_Lambir_Hills_Litterfall_Methods_Description" includes a brief description of the litterfall sampling methods. The Branchfall Folder includes four files: the "GEM_Lambir_Hills_Branchfall_Data_2009_to_2010.csv" file includes the branchfall data, the "GEM_Lambir_Hills_Branchfall_Metadata_2009_to_2010.xlsx" includes the description of the columns in the csv data file, the "GEM_Lambir_Hills_Branchfall_Methods_Description.pdf" includes a brief description of the branchfall collection methods, and the "GEM_Lambir_Hills_Branchfall_Data_Metadata_Methods_TransectSums.xlx" includes data, metadata, methods description, and a 'TransectsSums' tab where the reader can find the sum of the branchfall data in each transect Litterfall and Branchfall data were collected to quantify canopy productivity and net primary productivity allocated to branch turnover, which are components of the total net primary productivity and ecosystem carbon budget. The methods follow the Global Ecosystems Monitoring (GEM) protocols (see gem.tropicalforests.ox.ac.uk). The data collection took place from 2008 to 2010 in two 1-ha GEM plots in Lambir Hills National Park, Sarawak, Malaysia, within the Lambir 52-ha ForestGEO plots. One of the plots was located on clay soil and the other on sandy loam. The site is an old-growth moist tropical forest dominated by Dipterocarpacea. The climate is moist tropical and aseasonal. Litterfall was collected biweekly from 25 litter traps per plot. Branchfall was estimated using four 100-m transects, which were surveyed every two months.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Frequency Comb-Based Remote Methane Observation Network (Final Scientific/Technical Report)

ARPA-E investment in long-range, frequency comb-based methane detection has resulted in the first scalable, cost-effective continuous methane emissions system. At the onset of this ARPA-E MONITOR award, the dual-frequency comb spectrometer was a sprawling, expensive, laboratory-confined device which had only recently been demonstrated capable of measuring atmospheric trace gases over open-paths. Bringing the technology to the point of being able to monitor for and characterize methane emissions at oil and natural gas production facilities seemed an almost impossible task. Over the subsequent months and years, our team transitioned the Nobel-prize-winning frequency-comb-laser spectrometer technology to a field-deployed regional methane leak detection system. The spectrometer enables highly sensitive near-infrared absorption measurements of methane along kilometer-scale laser beam paths. The measurements are coupled with an atmospheric modeling and inversion framework to triangulate the location of emission sources and quantify the emission rates.

03 NATURAL GAS↗
DOE OSTI2022

Feedstock Carbon Intensity Calculator (FD-CIC): Users’ Manual and Technical Documentation

The carbon intensities (CIs) of biofuels are determined with the life cycle analysis (LCA) technique, which accounts for the energy/material uses and emissions during the complete supply chain of biofuel including feedstock production and fuel conversion stages. Regulatory agencies such as California Air Resources Board (CARB) adopts LCA to calculate biofuel CIs. The Low Carbon Fuel Standard (LCFS) program developed by CARB allows individual biofuel conversion facilities to submit their own biofuel CIs with their facility input data and incentivizes the reduction in the CI specific to that particular facility compared to a reference fuel’s CI (Liu et al 2020). Such an incentive program has driven innovations in biorefineries to reduce their greenhouse gas (GHG) emissions by linking their revenue directly to its CI score through LCFS credit trading. Besides the biofuel conversion stage, different farming practices for feedstock growth can result in significant CI variations for feedstocks, thus for biofuels. To provide evidence-based research findings, the U.S. Department of Energy’s Advanced Research Projects Agency–Energy (ARPA-E) has supported the Systems Assessment Center of the Energy Systems and Infrastructure Analysis Division at Argonne National Laboratory to examine CI variations of different farming practices to grow agricultural crops for biofuel production. Meanwhile, the ARPA-E has launched the Systems for Monitoring and Analytics for Renewable Transportation Fuels from Agricultural Resources and Management (SMARTFARM) program to develop technologies and data platforms that enable an accurate measurement of key farming parameters that can help robust accounting of the GHG benefits of sustainable, low-carbon agronomic practices at farm level.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Solid-State Mixed-Potential Electrochemical Sensors for Natural Gas Leak Detection and Quality Control (Final Technical Report)

Mitigation of methane emissions are a critical factor to limiting the impact of the natural gas industry on global climate change. Throughout the period of 2020-2024, the University of New Mexico and its commercialization partner and subcontractor, SensorComm Technologies, Inc. (SCT), have worked together to develop a low-cost Artificial Intelligence (AI)-driven Internet of Things (IoT)-based multi-gas sensor platform for methane emissions detection. In the final year of the project, we extended this work to include hydrogen detection in support of a transition to a hydrogen economy where hydrogen could be transported through existing natural gas infrastructure. Mixed potential electrochemical sensors were first prototyped by ceramic additive manufacturing and then transitioned to conventional ceramic manufacturing tape casting and screen-printing technologies in preparation for mass production. Demonstrated limits of detection of 5 ppm of methane in natural gas and 1 ppm of hydrogen were measured. These limits of detection are among the lowest of solid-state electrochemical sensors that have been reported in the literature or available in the industry. Machine learning algorithms were developed to identify natural gas mixtures with > 98% accuracy level and quantify methane concentrations at 97% accuracy. The presence of hydrogen could also be identified, and its concentration quantified at these accuracy levels. These algorithms were optimized for running on portable computing hardware which enabled > 1 Hz processing rates. A portable packaged IoT system was integrated with the electrochemical sensor in collaboration with SCT. The package consists of readout electronics with < 1 mV resolution, sensor temperature control, and data transmission over cellular wireless and/or Wi-Fi networks. Field testing was performed in two rounds at Colorado State University’s Methane Emissions Technology Evaluation Center (CSU METEC). The first round of testing demonstrated successful measurements of methane from an underground natural gas leak of 20 standard liters per minute (SLPM), which agreed with previously published literature using more sophisticated and expensive analytical equipment. The second round of testing showed that an above ground leak of 2 SLPM of hydrogen could be detected at 32 ft. This project has resulted in six published peer reviewed journal articles, over ten presentations at professional conferences, and one full patent application filed in 2023. Future work on this project includes increased sensitivity, higher production yields, and applications in the hydrogen safety and flare emissions monitoring spaces.

03 NATURAL GAS↗
DOE OSTI2024

The use of a benign fast-growing cyanobacterial species to control microcystin synthesis from Microcystis aeruginosa

Introduction Microcystis aeruginosa(M. aeruginosa), one of the most abundant blue-green algae in aquatic environments, produces microcystin by causing harmful algal blooms (HABs). This study investigated the combined effects of nutrients and competition among cyanobacterial subpopulations on the synthesis of microcystin-LR. Methods Under varying nitrogen and phosphorus concentrations, cyanobacterial coculture, and the presence of algicidal DCMU, the growth was monitored by optical density analysis or microscopic counting, and the microcystin production was analyzed using high-performance liquid chromatography-UV. Furthermore, growth and toxin production were predicted using a kinetic model. Results and discussion First, coculture with the fast-growing cyanobacteriumSynechococcus elongatusUTEX 2973 (S. elongatus) reducedM. aeruginosabiomass and microcystin production at 30°C. Under high nitrogen and low phosphorus conditions,S. elongatuswas most effective, limitingM. aeruginosagrowth and toxin synthesis by up to 94.7% and 92.4%, respectively. Second, this biological strategy became less effective at 23°C, whereS. elongatusgrew more slowly. Third, the photosynthesis inhibitor DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea) inhibitedM. aeruginosagrowth (at 0.1 mg/L) and microcystin production (at 0.02 mg/L). DCMU was also effective in controlling microcystin production inS. elongatus–M. aeruginosacocultures. Based on the experimental results, a multi-substrate, multi-species kinetic model was built to describe coculture growth and population interactions. Conclusion Microcystin from representative toxin-producingM. aeruginosacan be controlled by coculturing fast-growing benign cyanobacteria, which can be made even more efficient if appropriate algicide is applied. This study improved the understanding of the biological control of microcystin production under complex environmental conditions.

Microbiology↗
DOE OSTI2024

Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits

The primary objective of the study was development of a machine learning (ML)-based workflow for fracture hit (“frac hit”) detection and monitoring using shale oil-field data such as drilling surveys, production history (oil and produced water), pressure, and fracking start time and duration records. The ML method takes advantage of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks to identify the frac hits due to hydraulic communication between the fracking child well(s) and the producing parent well(s) within the same pad (intra-pad interaction) and/or on different pads (inter-pad interaction). It utilizes time series of pressure and production data from within a pad and from adjacent pads. The workflow can capture time variable features of frac hits when the model architecture is deep and wide enough, with enough trainable parameters for deep learning and feature extraction, as demonstrated in this paper by using training and testing subsets of the field data from selected neighboring pads with over a couple of hundred wells. The study was focused on frac-hit interaction among paired wells and demonstrated that the ML model, once trained, can predict the frac-hit probability.

58 GEOSCIENCES↗
DOE OSTI2023

Predicting Short-Term Deformation in the Central Valley Using Machine Learning

Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. This is why it is of utmost importance to routinely monitor and assess the surface deformation occurring. Two main goals that this paper attempts to accomplish are deformation characterization and deformation prediction. The first goal is realized through the use of Principal Component Analysis (PCA) applied to a series of Interferomtric Synthetic Aperture Radar (InSAR) images that produces eigenimages displaying the key characteristics of the subsidence. Water storage changes are also directly analyzed by the use of data from the Gravity Recovery and Climate Experiment (GRACE) twin satellites and the Global Land Data Assimilation System (GLDAS). The second goal is accomplished by building a Long Short-Term Memory (LSTM) model to predict short-term deformation after developing an InSAR time series using LiCSBAS, an open-source InSAR time series package. The model is applied to the city of Madera and produces better results than a baseline averaging model and a one dimensional convolutional neural network (CNN) based on a mean squared error metric showing the effectiveness of machine learning in deformation prediction as well as the potential for incorporation in hazard mitigation models. The model results can directly aid policy makers in determining the appropriate rate of groundwater withdrawal while maintaining the safety and well-being of the population as well as the aquifers’ integrity.

58 GEOSCIENCES↗
DOE OSTI2020

P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C 3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C 3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R 2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R 2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R 2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr –1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Passive Acoustic Monitoring Provides Insights into Avian Use of Energycane Cropping Systems in Southern Florida

Birds are important indicators of ecosystem health and provide a range of benefits to society. It is important, therefore, to understand the impacts of agricultural land use changes on bird populations. The cultivation of energycane (EC)—a sugarcane hybrid—for biofuel production represents one form of agricultural land use change in southern Florida. We used passive acoustic monitoring (PAM) to examine bird community use of experimental EC fields and other agricultural land uses at two study sites in southern Florida. We deployed 16 acoustic recorders in different study plots and used the automatic species identifier BirdNET to identify 40 focal bird species. We found seasonal differences in daily avian species diversity and richness between EC experimental plots and reference agricultural fields (corn fields, orchards, pastureland), and between time periods (pre-planting, post-planting). Daily avian species diversity and richness were lower in the EC experimental plots during Fall and Winter months when plants reached maximum height (>400 cm in some areas). Despite seasonal differences in daily measures of species diversity and richness, we found no differences in cumulative species richness, suggesting that there may be little overall (season-long) effects of EC production. These findings could provide insight to avian seasonal habitat preferences and underscore the potential limitations of PAM in areas experiencing dynamic vegetation changes. More research is needed to better understand if utilization of EC cropping systems results in positive or negative effects on avian populations (e.g., foraging habitat quality, predator–prey dynamics, nest success).

BirdNET↗
DOE OSTI2020

Direct SERDS sensing of molecular biomarkers in plants under field conditions

Molecular biomarkers such as microRNAs (miRNAs) play important roles in regulating various developmental processes in plants. Understanding these pathways will help bioengineer designing organisms for efficient biomass accumulation. Current methods for RNA analysis require sample extraction and multi-step sample analysis, hindering work in field studies. Recent work in the incorporation of nanomaterials for plant bioengineering research is leading the way of an agri-tech revolution. As an example, surface-enhanced Raman scattering (SERS)–based sensors can be used to monitor RNA in vivo. However, the use of SERS in the field has been limited due to issues with observing Raman signal over complex background. To this end, shifted-excitation Raman difference spectroscopy (SERDS) offers an effective solution to extract the SERS signal from high background based on a physical approach. In this manuscript, we report the first application of SERDS on SERS sensors. We investigated this technique on SERS sensor developed for the detection of a microRNA biomarker, miR858. We tested the technique on in vitro samples and validated the technique by detecting the presence of exogenous miR858 in plants directly under ambient light in a growth chamber. The possibility of moving the detection of nucleic acid targets outside the constraints of laboratory setting enables numerous important bioengineering applications. Finally, such applications can revolutionize biofuel production and agri-tech through the use of nanotechnology-based monitoring of plant growth, plant health, and exposure to pollution and pathogens.

09 BIOMASS FUELS↗
DOE OSTI2023

Using Current Data to Detect Hardware Faults at Solar Plants

Monitoring amperage data is an effective means of detecting faults in PV plant data. Utilizing amperage data collected at the combiner box gives plant operators an up-to-date list of faulted equipment, allowing them to coordinate maintenance needs at much shorter intervals than previously. Shorter maintenance intervals will increase PV plant production levels, narrowing the gap between expected and actual PV plant performance. The amperage monitoring method performs at a high level, with a string-outage related fault detection True Positive Rate of 46% and False Positive Rate of 8%.

14 SOLAR ENERGY↗
DOE OSTI2020

Fission Gas Monitoring System Efficiency Calibration Summary Report

The Fission Gas Monitoring System was designed to work in conjunction with the Fuel Accident Condition Simulator Furnace and is used to measure fission gas releases during post-irradiation fuel heating tests. A high-purity helium sweep gas is swept past the heated fuel sample in the furnace and routed to a fission gas monitoring system, which cryrogenically traps the krypton (Kr) and xenon (Xe) fission gases. As the radioactive species of the trapped gas decays, high-purity germanium detectors monitor the amount of fission gas in each trap. The primary fission product present in this test is 85Kr, which has a 10.76-year half-life. The presence of 85Kr indicates particle defects and iodine release. There is also the potential for re-irradiating the fuel compacts in the Neutron Radiography Reactor in the Hot Fuel Examination Facility basement. With re-irradiation, it is possible to have xenon fission gas present, specifically 133Xe, which has a 5.243-day half-life. Re-irradiation also produces 131I, which is important for design-basis accidents and regulatory licensing.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Neutron Dosimetry for the GE Hitachi 16-10393 Irradiation in ATR

PNNL project 74242 involves the analysis of neutron fluence monitors and melt wires irradiated in the Advanced Test Reactor (ATR) at Idaho National Laboratory in accordance with MPO 00236287 and Statement of Work (SOW) No. 17370, Rev. 0, PNNL Analysis of NSUF Flux and Melt Wire Capsules. This report is for the GE Hitachi 16-10393 irradiation which was conducted in positions B11 of the ATR. Three other irradiations included in the SOW will be reported separately. The neutron fluence monitors were prepared by PNNL and loaded into the GE Hitachi assemblies prior to irradiation. Following irradiation, the capsules were returned to PNNL for analysis. The neutron dosimetry capsules were opened, the flux wires were removed for gamma analysis. The measured activities were used to determine the activation rates for various activation products. Following suitable corrections, the measured activation rates were used to adjust calculated neutron spectra at each fluence monitor location. The adjusted neutron spectra were then used to determine displacement per atom (dpa) and gas production for irradiated materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Optimizing Rate of Penetration and Tripping Decision-Making using Real-Time Bit Wear Monitoring While Drilling Geothermal Wells

Understanding bit wear while drilling is critical to minimizing non-productive time (NPT) and optimizing rate of penetration (ROP). Lengthening drilling runs with damaged bits does not only lower the ROP, but also elevates the risk of inducing severe bit damage, which could potentially lead to time-consuming fishing operations. When drillers believe the bit has worn off substantially, the bit is tripped out to be replaced. On geothermal wells, tripping can take up to 20% of the overall well construction time, and this is generally acknowledged as an opportunity for improvement. Ideally, a bit run should be terminated before the bit is damaged beyond repair. At the same time, premature bit pulls are to be avoided as well. This study aims to leverage bit and tooth wear metrics that can be obtained in real time to characterize bit condition in order to optimize ROP and determine the optimal time to pull the bit. Two metrics were explored in this study: a bit wear metric that incorporated depth-of-cut, and a tooth wear metric developed by Bourgoyne & Young characterizing the state of bit teeth dull. Both metrics were computed using recorded data from 12¼ inches roller cone insert bit runs in five geothermal wells targeting a granodiorite formation in the western United States. Together with the actual dull grades, determined after the bits were pulled to surface, the metric trends were interpreted to characterize the downhole bit condition and identify the point at which the bit should have optimally been tripped out. The insights from studying the actual dull grades and how they relate to the two metrics were used to establish a reliable bit pull criterion. The bit wear metric trend correctly showed a noticeable departure from baseline for bits experiencing major dulling behavior. Additionally, the tooth wear model predicted the cutter dull within two dull grades for most runs, with better performance in predicting the inner teeth dull. Moreover, the combination of the bit wear and tooth wear metrics was effective in revealing the cause of the bit performance impairment. Proactive tracking of these two metrics in real-time can facilitate geothermal drilling ROP optimization and better-informed tripping decision-making, thereby avoiding wasted time and cost.

Ashari, Rahmat↗