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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Rapid spatial analysis of surrogate TRISO fuel particles using laser-induced breakdown spectroscopy image analysis

Laser-induced breakdown spectroscopy (LIBS) mapping enables rapid elemental and spatial analysis of solid samples. In this work, surrogate TRi-structural ISOtropic (TRISO) particles with various layers consisting of Zr, W, C, and Si were used to demonstrate the use of a new thickness measurement tool developed to analyze elemental images generated from LIBS maps. Zr particles with varying outer-layer thicknesses ranging from 16 to 32 μm were measured with scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) and LIBS with both a complementary metal-oxide-semiconductor (CMOS) detector and an intensified charged coupled device (ICCD) detector. LIBS maps of particles were completed using CMOS and ICCD spectrometers with effective spatial resolutions of 4 and 2 μm, respectively. The novel thickness measurement tool identified layer regions within a LIBS map and then identified the locations of the boundaries between these layers. The tool then generated up to 1000 random profiles stemming radially from the center region, which were used to measure the layer thickness/radius. This image analysis tool demonstrated LIBS's ability to provide values comparable with SEM-EDS (3.7% relative difference) along with a 95% reduction in measurement time. Furthermore, the precision of these measurements was on par with the SEM-EDS measurements at <15% relative standard deviation. Following the analysis of the Zr particles, W particles with increased complexity (e.g., five versus three layers) were analyzed using both the CMOS and ICCD spectrometers simultaneously. The spatial dimensions were extracted with an average relative difference of 2.7% and an average relative standard deviation of 9%.

Andrews, Hunter B. [Oak Ridge National Laboratory ↗
DOE OSTI2025

A Polynomial-Time Classical Algorithm for Noisy Quantum Circuits

We provide a polynomial-time classical algorithm for noisy quantum circuits. The algorithm computes the expectation value of any observable for any circuit, with a small average error over input states drawn from an ensemble (e.g., the computational basis). Our approach is based upon the intuition that noise exponentially damps nonlocal correlations relative to local correlations. This enables one to classically simulate a noisy quantum circuit by keeping track of only the dynamics of local quantum information. Our algorithm also enables sampling from the output distribution of a circuit in quasipolynomial time, so long as the distribution anticoncentrates. A number of implications are discussed, including a fundamental limit on the efficacy of noise mitigation strategies: For constant noise rates, any quantum circuit for which error mitigation succeeds in polynomial-time on most input states can also be classically simulated in polynomial-time on most input states. Our algorithms scale exponentially in the inverse noise rate, which is fundamental and makes them impractical for current quantum devices.

decoherence↗
DOE OSTI2025

Real-space simulations of the hyperbolic plasmon polaritons in 1T′ tungsten ditelluride

Recent discoveries of hyperbolic surface plasmon polaritons (SPPs) in 1T′ WTe 2 have garnered significant attention in 2D materials and nanophotonics. In this study, we employ finite-element simulations to investigate the real-space characteristics of hyperbolic SPPs in thin WTe 2 flakes. Our results show that the SPPs exhibit a pronounced sensitivity to excitation energy and sample thickness. By analyzing the plasmonic field patterns, we extract key plasmonic parameters including plasmon wavelengths, hyperbolic angles, and plasmonic figures of merit. In addition, we examine SPP modes in stacked WTe 2 flakes with varying twist angles, demonstrating that the plasmonic field patterns can be effectively tuned by adjusting the twist angle. Notably, as the twist angle increases, the SPPs undergo a topological transition from open hyperbolic modes to closed elliptic modes. This twist-engineering capability offers promising potential for the development of tunable plasmonic devices based on WTe 2 .

2D materials↗
DOE OSTI2025

Ferroelectricity of wurtzite Al 1−x Hf x N heterovalent alloys

Thin films of aluminum hafnium nitride (Al1−xHfxN) were synthesized via reactive magnetron sputtering for Hf contents up to x = 0.13. X-ray diffraction showed a single c-axis oriented wurtzite phase for all films. Hard x-ray photoelectron spectroscopy demonstrated homogeneous Al:Hf distribution through the thin films and confirmed their insulating character. A collection of complementary tests showed unambiguous polarization inversion, and thus ferroelectricity in multiple samples. Current density vs electric field hysteresis measurements showed distinct ferroelectric switching current peaks, the piezoelectric coefficient d 33,f,meas measured using a double beam laser interferometer (DBLI) showed a reversal in sign with similar magnitude, and anisotropic wet etching confirmed field-induced polarization inversion. This demonstrates the possibility of using tetravalent–and not just trivalent–alloying elements to enable ferroelectricity in AlN-based thin films, highlighting the compositional flexibility of ferroelectricity in wurtzites and greatly expanding the chemistries that can be considered for future devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Magnetically dependent photovoltages in permalloy films and gratings

Significant photoinduced voltages observed in permalloy structures consist of two contributions with different origins, which depend on illumination conditions, structure geometry and magnetic field in distinct ways. The first component is the plasmon drag effect voltage closely associated with plasmon propagation. The second contribution is magnetically dependent and can be related to photoinduced gradients in the sample temperature and spin polarization. Possible mechanisms of the effect are discussed

36 MATERIALS SCIENCE↗
DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Magnetic anisotropy and GGG substrate stray field in YIG films down to millikelvin temperatures

Abstract Quantum magnonics investigates the quantum-mechanical properties of magnons, such as quantum coherence or entanglement for solid-state quantum information technologies at the nanoscale. The most promising material for quantum magnonics is the ferrimagnetic yttrium iron garnet (YIG), which hosts magnons with the longest lifetimes. YIG films of the highest quality are grown on a paramagnetic gadolinium gallium garnet (GGG) substrate. The literature has reported that ferromagnetic resonance (FMR) frequencies of YIG/GGG decrease at temperatures below 50 K despite the increase in YIG magnetization. We investigated a 97 nm-thick YIG film grown on 500 μm-thick GGG substrate through a series of experiments conducted at temperatures as low as 30 mK, and using both analytical and numerical methods. Our findings suggest that the primary factor contributing to the FMR frequency shift is the stray magnetic field created by the partially magnetized GGG substrate. This stray field is antiparallel to the applied external field and is highly inhomogeneous, reaching up to 40 mT in the center of the sample. At temperatures below 500 mK, the GGG field exhibits a saturation that cannot be described by the standard Brillouin function for a paramagnet. Including the calculated GGG field in the analysis of the FMR frequency versus temperature dependence allowed the determination of the cubic and uniaxial anisotropies. We find that the total crystallographic anisotropy increases more than three times with the decrease in temperature down to 2 K. Our findings enable accurate predictions of the YIG/GGG magnetic systems behavior at low and ultralow millikelvin temperatures, crucial for developing quantum magnonic devices.

Serha, Rostyslav O.↗
DOE OSTI2024

Skipper-in-CMOS: Nondestructive Readout With Subelectron Noise Performance for Pixel Detectors

The Skipper-in-CMOS image sensor integrates the nondestructive readout capability of skipper charge coupled devices (Skipper-CCDs) with the high conversion gain of a pinned photodiode (PPD) in a CMOS imaging process while taking advantage of in-pixel signal processing. This allows both single photon counting as well as high frame rate readout through highly parallel processing. The first results obtained from a ${15} \times {15}~\mu $ m2 pixel cell of a Skipper-in-CMOS sensor fabricated in Tower Semiconductor’s commercial 180-nm CMOS image sensor process are presented. Measurements confirm the expected reduction of the readout noise with the number of samples down to deep subelectron noise of $0.15\text {e}^ - $ , demonstrating the charge transfer operation from the PPD and the single photon counting operation when the sensor is exposed to light. This article also discusses new testing strategies employed for its operation and characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗