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At least 649 records · Page 36

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DOE OSTI2016

Macromolecular crystallography with a large format CMOS detector

Recent advances in CMOS technology have allowed the production of large surface area detectors suitable for macromolecular crystallography experiments [1]. The Molecular Biology Consortium (MBC) Beamline 4.2.2 at the Advanced Light Source in Berkeley, CA, has installed a 2952 x 2820 mm RDI CMOS-8M detector with funds from NIH grant S10OD012073. The detector has a 20nsec dead pixel time and performs well with shutterless data collection strategies. The sensor obtains sharp point response and minimal optical distortion by use of a thin fiber-optic plate between the phosphor and sensor module. Shutterless data collections produce high-quality redundant datasets that can be obtained in minutes. The fine-sliced data are suitable for processing in standard crystallographic software packages (XDS, HKL2000, D*TREK, MOSFLM). Faster collection times relative to the previous CCD detector have resulted in a record number of datasets collected in a calendar year and de novo phasing experiments have resulted in publications in both Science and Nature [2,3]. The faster collections are due to a combination of the decreased overhead requirements of shutterless collections combined with exposure times that have decreased by over a factor of 2 for images with comparable signal to noise of the NOIR-1 detector. The overall increased productivity has allowed the development of new beamline capabilities and data collection strategies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2017

Chemically stable artificial SEI for Li-ion battery electrodes

Here, the importance of coating’s chemical stability in lithium-ion batteries has been demonstrated by this study. It is well known that the mechanical properties determine the cycle life, and chemical stability or chemical degradation rate determines the calendar life. In this study, we used HfO 2 coatings prepared by atomic layer deposition as an example to show the chemical stability of the coatings for lithium ion battery electrodes.

25 ENERGY STORAGE↗

DOE OSTI2017

Shielding Analysis Capability of UNF-ST&DARDS

The Used Nuclear Fuel Storage, Transportation and Disposal Analysis Resource and Data System (UNF-ST&DARDS) is used to perform dose rate calculations for spent nuclear fuel (SNF) transportation packages based on the actual physical and nuclear characteristics (i.e., assembly design, burnup, initial enrichment, and cooling time) of the as-loaded SNF. Nuclear fuel data, transportation package model templates, and SNF canister loading map information residing within the tool facilitate automated generation of SCALE input files for radiation source term and dose rate calculations. Transportation package specific models developed for UNF-ST&DARDS dose rate analyses are described in detail. UNF-ST&DARDS dose rate analyses were performed in this paper for over 400 SNF canisters from 16 sites in their designated transportation casks. For simplicity, representative dose rate calculation results are presented as a function of time (i.e., selected calendar years between 2020 and 2100) for 73 SNF canisters in dry storage at four sites. For these canisters, the projected maximum dose rate values at 2 m from the lateral surfaces of the vehicle under normal conditions of transport (NCT) would vary between 1.9 and 11.5 mrem/h in 2020. Five SNF canisters will exceed the regulatory dose rate limit of 10 mrem/h at 2 m in 2020, and the analyzed SNF canisters will comply with regulatory dose rate limits by 2030. An analysis of the impact on the dose rate of fuel failure and reconfiguration during transportation indicated that the maximum dose rate for hypothetical accident conditions will be unaffected, and the NCT maximum dose rate at 2 m would have a maximum increase by a factor of 1.7 for a representative pressurized water reactor package and by a factor of 2.6 for a representative boiling water reactor package relative to intact fuel. Finally, analysis of the actual heat loading and the dose rate at 2 m from the lateral surface of the vehicle for the five SNF canisters exceeding the NCT regulatory dose rate limit of 10 mrem/h in 2020 showed that the dose rate could be more limiting with respect to regulatory requirements than the heat loading; i.e., the canister transportability date may be evaluated based on the transportation package’s external dose rate.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

RECURRENT NOVAE IN M31

The reported positions of 964 suspected nova eruptions in M31 recorded through the end of calendar year 2013 have been compared in order to identify recurrent nova (RN) candidates. To pass the initial screen and qualify as a RN candidate, two or more eruptions were required to be coincident within 0.′1, although this criterion was relaxed to 0.′15 for novae discovered on early photographic patrols. A total of 118 eruptions from 51 potential RN systems satisfied the screening criterion. To determine what fraction of these novae are indeed recurrent, the original plates and published images of the relevant eruptions have been carefully compared. This procedure has resulted in the elimination of 27 of the 51 progenitor candidates (61 eruptions) from further consideration as RNe, with another 8 systems (17 eruptions) deemed unlikely to be recurrent. Of the remaining 16 systems, 12 candidates (32 eruptions) were judged to be RNe, with an additional 4 systems (8 eruptions) being possibly recurrent. It is estimated that ∼4% of the nova eruptions seen in M31 over the past century are associated with RNe. A Monte Carlo analysis shows that the discovery efficiency for RNe may be as low as 10% that for novae in general, suggesting that as many as one in three nova eruptions observed in M31 arise from progenitor systems having recurrence times ≲100 yr. For plausible system parameters, it appears unlikely that RNe can provide a significant channel for the production of Type Ia supernovae.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Mechanical studies of the solid electrolyte interphase on anodes in lithium and lithium ion batteries

A stable solid electrolyte interphase (SEI) layer is key to high performing lithium ion batteries for metrics such as calendar and cycle life. The SEI must be mechanically robust to withstand large volumetric changes in anode materials such as lithium and silicon, so understanding the mechanical properties and behavior of the SEI is essential for the rational design of artificial SEI and anode form factors. The mechanical properties and mechanical failure of the SEI are challenging to study, because the SEI is thin at only ~ 10 – 200 nm thick and is air sensitive. Furthermore, the SEI changes as a function of electrode material, electrolyte and additives, temperature, potential, and formation protocols. A variety of in situ and ex situ techniques have been used to study the mechanics of the SEI on a variety of lithium ion battery anode candidates; however, there hasn't been a succinct review of the findings thus far. Because of the difficultly of isolating the true SEI and its mechanical properties, there have been a limited number of studies that can fully de-convolute the SEI from the anode it forms on. A review of past research will be helpful for culminating current knowledge and helping to inspire new innovations to better quantify and understand the mechanical behavior of the SEI. This review will summarize the different experimental and theoretical techniques used to study the mechanics of SEI on common lithium ion battery anodes and their strengths and weaknesses.

25 ENERGY STORAGE↗

DOE OSTI2021

Partitioning uncertainty in projections of Arctic sea ice

Abstract Improved knowledge of the contributing sources of uncertainty in projections of Arctic sea ice over the 21st century is essential for evaluating impacts of a changing Arctic environment. Here, we consider the role of internal variability, model structure and emissions scenario in projections of Arctic sea-ice area (SIA) by using six single model initial-condition large ensembles and a suite of models participating in Phase 5 of the Coupled Model Intercomparison Project. For projections of September Arctic SIA change, internal variability accounts for as much as 40%–60% of the total uncertainty in the next decade, while emissions scenario dominates uncertainty toward the end of the century. Model structure accounts for 60%–70% of the total uncertainty by mid-century and declines to 30% at the end of the 21st century in the summer months. For projections of wintertime Arctic SIA change, internal variability contributes as much as 50%–60% of the total uncertainty in the next decade and impacts total uncertainty at longer lead times when compared to the summertime. In winter, there exists a considerable scenario dependence of model uncertainty with relatively larger model uncertainty under strong forcing compared to weak forcing. At regional scales, the contribution of internal variability can vary widely and strongly depends on the calendar month and region. For wintertime SIA change in the Greenland-Iceland-Norwegian and Barents Seas, internal variability contributes 60%–70% to the total uncertainty over the coming decades and remains important much longer than in other regions. We further find that the relative contribution of internal variability to total uncertainty is state-dependent and increases as sea ice volume declines. These results demonstrate that internal variability is a significant source of uncertainty in projections of Arctic sea ice.

Bonan, David B. (ORCID:0000000338676009)↗

DOE OSTI2020

Using onset times from frequent seismic surveys to understand fluid flow at the Peace River Field, Canada

With our limited knowledge of the relationship between changes in the state of an aquifer or reservoir and the corresponding changes in the elastic moduli, that is the rock physics model, hampers the effective use of time-lapse seismic observations for estimating flow properties within the Earth. A central problem is the complicated dependence of the magnitude of time-lapse changes on the saturation, pressure, and temperature changes within an aquifer or reservoir. We describe an inversion methodology for reservoir characterization that uses onset times, the calendar time of the change in seismic attributes, rather than the magnitude of the changes. We find that onset times are much less sensitive than magnitudes to the rock physics model used to relate time-lapse observations to changes in saturation, temperature and fluid pressure. We apply the inversion scheme to observations from daily monitoring of enhanced oil recovery at the Peace River field in Canada. An array of 1492 buried hydrophones record seismic signals from 49 buried sources. Time-shifts for elastic waves traversing the reservoir are extracted from the daily time-lapse cubes. In our analysis 175 images of time-shifts are transformed into a single map of onset times, leading to a substantial reduction in the volume of data. These observations are used in conjunction with bottom hole pressure data to infer the initial conditions prior to the injection, and to update the reservoir permeability model. The combination of a global and local inversion scheme produces a collection of reservoir models that are best described by three clusters. The updated model leads to a nearly 70 percent reduction in seismic data misfit. The final set of solutions successfully predict the observed normalized pressure history during the soak and flow-back into the wells between 82 and 175 days into the cyclic steaming operation.

58 GEOSCIENCES↗

DOE OSTI2019

PDB Structure Data Impacted Discovery and Development of Recently FDA‐Approved Drugs

Protein Data Bank (PDB) was established in 1971 as the 1 st open access digital data resource in biology and medicine. Today, the PDB currently houses >145,000 atomic level biomolecular structures determined by crystallography, NMR spectroscopy, and 3D electron microscopy. Managed by the Worldwide Protein Data Bank partnership (wwPDB; wwpdb.org ), PDB data are used by researchers and educators to understand the role molecules play in health and disease of humans, animals, and plants, food and energy production, and other topics of concern to global prosperity and sustainability. RCSB PDB operates the US data center for the global PDB archive, and makes PDB data available at no charge to all data consumers without limitations on usage. During calendar 2017, >679 million structure data files were downloaded from the PDB by Data Consumers working worldwide. RCSB PDB served >1 million RCSB.org users worldwide with PDB data integrated with ~40 external data resources providing rich structural views of fundamental biology, biomedicine, and energy sciences, and supported >600,000 PDB101.rcsb.org educational website users around the globe. RCSB PDB examined the impact of PDB archiving and open access availability on the discovery of 210 new molecular entities (NMEs or drugs) approved by the United States (US) Food and Drug Administration (FDA) between 2010 and 2016. Biological targets for 90% of NMEs are known. Approximately 6,000 PDB structures containing one of these drug targets and/or one approved NME were identified, providing 85% coverage of known targets and 88% coverage of 2010–2016 approved NMEs. Coverage was comparable across all therapeutic areas. Most structures were both published and freely accessible from the PDB long before the NMEs were approved. Additional analyses revealed these PDB structures were cited by more than 200,000 PubMed‐indexed publications reporting pre‐competitive research on the would‐be drug targets. PDB structures and citing publications contributed substantially to understanding of the target biology, and led to biopharmaceutical company investment in successful drug discovery/development programs. Overall, access to PDB data contribute to patent applications, drug discovery and development, publication of scientific studies, innovations that can lead to new product development and company formation, and STEM education. Support or Funding Information RCSB PDB is funded by a grant (DBI‐1338415) from the National Science Foundation , the National Institutes of Health , and the US Department of Energy . This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Zardecki, Christine↗

DOE OSTI2022

Applying ALARA Principles in the Design of New Radiological Facilities

The application of ALARA (As Low As Reasonably Achievable) principles to the design of new radiological facilities at Argonne National Laboratory provides a consistent radiation safety basis for future facility operations. The Department of Energy Rule 10 CFR 835 specifies design objectives to be adopted during the design of new facilities for controlling personal radiation exposure. One is to keep exposure levels below 20% of the applicable standards in 10 CFR 835.202. For a radiation worker 20% of the standard corresponds to 5 µSv hr -1 for a 2,000 hour work year. For a member of the public the DOE design objective corresponds to 0.2 mSv in a calendar year. For a new facility worst case radionuclides and their source strengths are chosen. Local shielding is specified to reduce exposure rates to less than 50 µSv hr -1 at 30 cm from the shielding. The current version MCNP6 of the Los Alamos radiation shielding computer program MCNP is then used to calculate the exposure rates elsewhere. Design modifications are made to meet the criteria. The calculations and resulting facility design modifications are discussed for two new radiological facilities.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2018

Search for annual and diurnal rate modulations in the LUX experiment

Various dark matter models predict annual and diurnal modulations of dark matter interaction rates in Earth-based experiments as a result of the Earth’s motion in the halo. Observation of such features can provide generic evidence for detection of dark matter interactions. This study reports a search for both annual and diurnal rate modulations in the LUX dark matter experiment using over 20 calendar months of data acquired between 2013 and 2016. This search focuses on electron recoil events at low energies, where leptophilic dark matter interactions are expected to occur and where the DAMA experiment has observed a strong rate modulation for over two decades. By using the innermost volume of the LUX detector and developing robust cuts and corrections, we obtained a stable event rate of 2.3 ± 0.2 cpd / keV ee / tonne , which is among the lowest in all dark matter experiments. No statistically significant annual modulation was observed in energy windows up to 26 keV ee . Between 2 and 6 keV ee , this analysis demonstrates the most sensitive annual modulation search up to date, with 9.2 σ tension with the DAMA/LIBRA result. We also report no observation of diurnal modulations above 0.2 cpd / keV ee / tonne amplitude between 2 and 6 keV ee .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Search for Periodic Time Variations of the Solar 8 B Neutrino Flux between 1996 and 2018 in Super-Kamiokande

We report a search for time variations of the solar 8 B neutrino flux using 5804 live days of Super-Kamiokande data collected between May 31, 1996, and May 30, 2018. Super-Kamiokande measured the precise time of each solar neutrino interaction over 22 calendar years to search for solar neutrino flux modulations with unprecedented precision. Periodic modulations are searched for in a dataset comprising five-day interval solar neutrino flux measurements with a maximum likelihood method. We also applied the Lomb-Scargle method to this dataset to compare it with previous reports. The only significant modulation found is due to the elliptic orbit of the Earth around the Sun. The observed modulation is consistent with astronomical data: we measured an eccentricity of (1.53 ± 0.35)%, and a perihelion shift of (−1.5 ± 13.5) days.

astroparticle detectors↗

DOE OSTI2021

Economic Comparison Between Battery and Supercapacitor for Hourly Dispatching Wave Energy Converter Power

This paper demonstrates a dispatching scheme of slider-crank wave energy converter (WEC) power generation using two different kinds of energy storage components, namely, Lithium-ion (Li-ion) battery and Supercapacitors (SC). The performance of the two energy storage components has been compared in order to develop the most economical energy storage system for WEC hourly dispatching scheme. The cost optimization of the energy storage system considering both cycling and calendar aging expenses is made based on its usage of depth of discharge. In this study, extensive simulation is conducted in MATLAB/Simulink platform, and it is found that SC is a better solution than Li-ion battery in terms of economic assessment for hourly dispatching WEC power.

50 EE - Wind and Water Power Program - Water (EE-4↗

DOE OSTI2021

Ensemble Learning, Prediction and Li-Ion Cell Charging Cycle Divergence

In recent years, the pervasive use of lithium ion (Li-ion) batteries in applications such as cell phones, laptop computers, electric vehicles, and grid energy storage systems has prompted the development of specialized battery management systems (BMS). The primary goal of a BMS is to maintain a reliable and safe battery power source while maximizing the calendar life and performance of the cells. To maintain safe operation, a BMS should be programmed to minimize degradation and prevent damage to a Li-ion cell, which can lead to thermal runaway. Cell damage can occur over time if a BMS is not properly configured to avoid overcharging and discharging. To prevent cell damage, efficient and accurate cell charging cycle characteristics algorithms must be employed. In this paper, computationally efficient and accurate ensemble learning algorithms capable of detecting Li-ion cell charging irregularities are described. Additionally, it is shown using machine and deep learning that it is possible to accurately and efficiently detect when a cell has experienced thermal and electrical stress due to cell overcharging by measuring charging cycle divergence.

25 ENERGY STORAGE↗

DOE OSTI2015

SU-E-J-73: Extension of a Clinical OIS/EMR/R&V System to Deliver Safe and Efficient Adaptive Plan-Of-The-Day Treatments Using a Fully Customizable Plan-Library-Based Workflow

Purpose: To extend a clinical Record and Verify (R&V) system to enable a safe and fast workflow for Plan-of-the-Day (PotD) adaptive treatments based on patient-specific plan libraries. Methods: Plan libraries for PotD adaptive treatments contain for each patient several pre-treatment generated treatment plans. They may be generated for various patient anatomies or CTV-PTV margins. For each fraction, a Cone Beam CT scan is acquired to support the selection of the plan that best fits the patient’s anatomy-of-the-day. To date, there are no commercial R&V systems that support PotD delivery strategies. Consequently, the clinical workflow requires many manual interventions. Moreover, multiple scheduled plans have a high risk of excessive dose delivery. In this work we extended a commercial R&V system (MOSAIQ) to support PotD workflows using IQ-scripting. The PotD workflow was designed after extensive risk analysis of the manual procedure, and all identified risks were incorporated as logical checks. Results: All manual PotD activities were automated. The workflow first identifies if the patient is scheduled for PotD, then performs safety checks, and continues to treatment plan selection only if no issues were found. The user selects the plan to deliver from a list of candidate plans. After plan selection, the workflow makes the treatment fields of the selected plan available for delivery by adding them to the treatment calendar. Finally, control is returned to the R&V system to commence treatment. Additional logic was added to incorporate off-line changes such as updating the plan library. After extensive testing including treatment fraction interrupts and plan-library updates during the treatment course, the workflow is running successfully in a clinical pilot, in which 35 patients have been treated since October 2014. Conclusion: We have extended a commercial R&V system for improved safety and efficiency in library-based adaptive strategies enabling a wide-spread implementation of those strategies. This work was in part funded by a research grant of Elekta AB, Stockholm, Sweden.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-P-01: Changing Your Oncology Information System: A Detailed Process and Lessons Learned

Purpose: Radiation Oncology departments are faced with many options for pairing their treatment machines with record and verify systems. Recently, there is a push to have a single-vendor-solution. In order to achieve this, the department must go through an intense and rigorous transition process. Our department has recently completed this process and now offer a detailed description of the process along with lessons learned. Methods: Our cancer center transitioned from a multi-vendor department to a single-vendor department over the 2015 calendar year. Our staff was partitioned off into superuser groups, an interface team, migration team, and go-live team. Six months after successful implementation, a detailed survey was sent to the radiation oncology department to determine areas for improvement as well as successes in the process. Results: The transition between record and verify systems was considered a complete success. The results of the survey did point out some areas for improving inefficiencies with our staff; both interactions between each other and the vendors. Conclusion: Though this process was intricate and lengthy, it can be made easier with careful planning and detailed designation of project responsibilities. Our survey results and retrospective analysis of the transition are valuable to those wishing to make this change.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-P-20: Predicting Waiting Times in Radiation Oncology Using Machine Learning

Purpose: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts of data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient’s waiting experience. Methods: In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested. Results: We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes. This means that the majority of our estimates are within 6.5 minutes of the actual wait time. Conclusion: The goal of this project was to define an appropriate machine learning algorithm to estimate waiting times based on the collective knowledge and experience learned from previous patients. Our results offer an opportunity to improve the information that is provided to patients and family members regarding the amount of time they can expect to wait for radiotherapy treatment at our centre. AJ acknowledges support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290) and from the 2014 Q+ Initiative of the McGill University Health Centre.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Potential Demand Reduction from Buildings in a Simulated Utility

The availability of large-area sensing, scalable algorithms, and high-performance computing has enabled the possibility of urban-scale building energy modeling using new methods not limited to the scalability of manual building energy model creation or retrieval of county-by-county tax assessor’s data. Automatic Building detection and Energy Model creation (AutoBEM) has created 178,368 building energy models for the Electric Power Board (EPB) of Chattanooga, TN, and compared simulation performance to 15-minute data from each building. These models leverage several data sources (e.g. imagery, GIS, utility), software tools to extract building properties (e.g. footprint, height, façade type, window-to-wall ratio, occupancy, building type), simulate at scale on two of the world’s #1 fastest supercomputers, and provide web-based visual analytics.Grid-interactive efficient buildings offer the potential to reduce utility and rate-payer energy costs during each calendar month’s hour of critical generation – when the least efficient, most costly, and often dirtiest generation assets must be brought online. In this paper, EnergyPlus is used to simulate over 150,000 buildings to assess the technical potential of utility-controlled smart thermostats. This is analyzed under a couple scenarios leveraging buildings as thermal batteries via pre-conditioning to coast through peak hours. Results are provided in box and whisker plots assessing the distribution of peak demand reduction at the utility-scale along with breakouts of energy and demand savings by building type and vintage.

Bass, Brett↗

DOE OSTI2017

Comprehensive Modeling of Temperature-Dependent Degradation Mechanisms in Lithium Iron Phosphate Batteries

For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately.For parameterization, a lifetime test study is conducted including storage and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. The model error for the cell capacity loss in the application-based tests is at the end of testing below 1 % of the original cell capacity.

25 ENERGY STORAGE↗