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DOE OSTI2025

Examining Ni Coarsening in Solid Oxide Electrolysis Cells by Characterizing NiH on Ni (111) Using a Combined Theoretical Approach

Ni coarsening in the fuel electrode of solid oxide cells (SOCs) is an important degradation mechanism. In this talk, density-functional theory and kinetic Monte Carlo methods are used to explore the hypothesis that the surface diffusion of NiH on Ni may promote Ni coarsening in the SOC operated in electrolysis cell mode. Using both methods and defining the diffusivity as the product of the surface coverage and single-molecule diffusivity, the diffusivity of NiH on Ni (111) is found to be sufficiently large under a significant overpotential to support the above hypothesis. Also, the time between the formation and dissociation of NiH on Ni (111) is predicted to be short at low coverages of H on Ni (111). Thus, significant progress is made toward developing a model of Ni coarsening considering both molecular and dissociated forms of NiH on Ni (111).

density functional theory (DFT)↗

DOE OSTI2025

First-Principles Thermodynamic Assessments of Sr-Containing Secondary Phase Formation in La1-xSrxMnO3±δ Perovskites for Solid Oxide Cell Applications

Sr-secondary phase formation is a potentially significant degradation mode threatening solid-oxide cell (SOC) commercial viability. A first-principles thermodynamic study was performed for rhombohedral perovskite (La1-xSrx) MnO3±δ (LSM) to assess its stability against Sr secondary phase formation in SOC applications. In this work, the Sr secondary phase formation reaction free energies were determined by combining ab initio lattice dynamics calculations for the solid phases and an ab initio thermodynamics approach for the gas phases. Furthermore, this approach goes beyond previous thermodynamic modeling studies by integrating first-principles based point-defect equilibria into the analyses. The modeling results indicate an increased tendency to form SrO oxide from LSM upon decreasing the oxygen partial pressure. Additionally, enhancing factors to form the Sr-related secondary phase from the associated SrO activity in LSM are further quantified by considering the equilibrium of SrO reacting with contaminant gas species as a function of temperature and gas pressure.

Defect and phase stability↗

DOE OSTI2025

In-Situ FTIR Detection of Transition Metal (TM)-Ion Dissolution From Cathodes in Li-Ion Batteries

Transition metal (TM) ions, commonly Ni and Mn, play a crucial role in Li-ion battery cathodes as the reaction centers for rapid redox reactions. A major challenge with TM-based cathodes is capacity degradation, particularly at higher operating voltages. This degradation is closely linked to the dissolution of TMs from the cathode materials and their subsequent deposition on the anode. This process not only modifies the surface structure of the cathode but, more significantly, alters the SEI composition on the anode [1-2]. The dissolution of TMs cations into a liquid electrolyte from cathode materials, such as Mn-ion dissolution from Mn-rich cathode (LMR), is detrimental to the cycling performance of Li-ion batteries [3-4]. Much attention has been paid to this issue but there remains a lack of characterization techniques which can detect the TM-ion dissolution from the cathode during electrochemical measurements. In our study, we use in-situ ATR-FTIR as an effective technique to probe the TM-ion dissolution from the cathode. We have first demonstrated the detrimental effects of TM ions on the electrochemical performance of Li-ion batteries by adding a small amount of TM salt (50 mM Mn(PF6)) to the electrolyte of a Li-ion coin cell with LFP and graphite electrode. We observed a rapid capacity fade after the first delithiation cycle. To investigate TM ion dissolution, we established a baseline IR spectrum for various TM solvation states (such as Mn and Ni) by measuring concentration-dependent IR spectra. This baseline spectrum helps us detect TM ion dissolution during battery cycling. In this work, we discuss in detail the effect of TM ions on the electrochemical performance of Li-ion batteries and the detection of TM ions during battery cycling using in-situ FTIR spectroscopy. We will compare TM dissolution between coated and uncoated cathodes to examine the effect of cathode coatings to mitigate degradation due to TM dissolution and cross-over from cathode to anode. References: (1) Zhan, C.; Wu, T.; Lu, J.; Amine, K. Dissolution, migration, anddeposition of transition metal ions in Li-ion batteries exemplified byMn-based cathodes - a critical review. Energy Environ. Sci. 2018, 11,243-257. (2) Jung, R.; Linsenmann, F.; Thomas, R.; Wandt, J.; Solchenbach,S.; Maglia, F.; Stinner, C.; Tromp, M.; Gasteiger, H. A. Nickel,Manganese, and Cobalt Dissolution from Ni-Rich NMC and TheirEffects on NMC622-Graphite Cells. J. Electrochem. Soc. 2019, 166,A378-A389. (3) Zhao, L.; Chenard, E.; Capraz, O. O.; Sottos, N. R.; White, S.R. Direct Detection of Manganese Ions in Organic Electrolyte by UV-Vis Spectroscopy. J. Electrochem. Soc. 2018, 165, A345-A348 (4) Zhang, Y.; Hu, A.; Xia, D.; Hwang, S.; Sainio, S.; Nordlund, D.;Michel, F. M.; Moore, R. B.; Li, L.; Lin, F. Operando characterization and regulation of metal dissolution and redeposition dynamics nearbattery electrode surface. Nat. Nanotechnol. 2023, 18, 790.

25 ENERGY STORAGE↗

DOE OSTI2026

Beyond sequence similarity: toward function-based screening of nucleic acid synthesis

Synthetic nucleic acids are a key input to modern biotechnology, yet they represent dual-use materials that require robust screening to mitigate biosecurity risks. The prevailing screening paradigm, which identifies sequences of concern (SoCs) through sequence similarity to controlled pathogens and toxins, may not fully capture risks posed by AI tools that can decouple biomolecular function from reliance on known sequences. Rapidly advancing biodesign capabilities enable the generation of genes and proteins that might evade sequence-based detection. We highlight the critical need for function-based screening approaches that can detect sequences capable of hazardous biological functions, regardless of similarity to known SoCs. We examine the feasibility of function-based screening with an initial focus on proteins, arguing that, while protein sequence space is vast, biologically functional proteins are significantly constrained by biophysical and biochemical requirements that can be learned and modeled. We propose a concrete implementation framework organized along a continuum of complexity, starting with toxins as the most tractable targets before expanding to more complex pathogenic functions. We then discuss open challenges and describe a research and development strategy to address them.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Relationship of Soil Microbiota to Seed Kernel Metabolism in Camellia oleifera Under Mulched

An experiment was conducted from 2016 to 2017 to assess the effect of kernel metabolism in development stages after organic mulching compared to control. Organic mulching significantly increased crop yields (higher 128% in 2016, higher 60% in 2017), oil content (the highest oil content was 27.6% higher than that of the control), and improved soil properties (SOC, SAN, AP, and AK). In this study, soil pH, SOC, AN, AP, and AK in 0–30 cm soil depth were measured. Results showed that the effect of mulching on soil pH was not significant at the harvesting stage. The greatest metabolic differences occurred during the period of high oil conversion (S2–S4), primarily involving 11 relevant metabolic pathways. This further verified that Camellia oleifera oil yield was improved after mulching. A total of 1,106 OTUs were detected by using 16S rRNA, and Venn diagram showed that there were 106 unique OTUs in control and 103 OTUs in the treatment, respectively. Correlation analysis showed that soil pH and soil temperature were two indicators with the most correlations with soil microbiota. The yield was significantly positively correlated with soil microbial Proteobacteria, Bacteroidetes, and soil nutrition indexes. Organic mulching improved the physicochemical properties of soils, caused differences in the relative abundance of dominant bacteria in soil bacteria, and improved the soil microbiological environment to promote plant growth, indicating that organic mulching is an effective measure to alleviate seasonal drought.

Ye, Honglian↗

DOE OSTI2025

A Software/Hardware Framework for Efficient and Safe Emergency Response in Post-Crash Scenarios of Battery Electric Vehicles

The adoption rate of battery electric vehicles (EVs) is rapidly increasing. Electric vehicles differ significantly from conventional internal combustion engine vehicles and vary widely across different manufacturers. Emergency responders (ERs) and recovery personnel may have less experience with EVs and lack timely access to critical information such as the extent of the stranded energy present, high-voltage safety hazards, and post-crash handling procedures in a user-friendly manner. This paper presents a software/hardware interactive tool named Electric Vehicle Information for Incident Response Solutions (EVIRS) to aid in the quick access to emergency response and recovery information. The current prototype of EVIRS identifies EVs using the VIN or Make, Model, and Year, and offers several useful features for ERs and recovery personnel. These features include integration and easy access to emergency response procedures tailored to an identified EV, vehicle structural schematics, the quick identification of battery pack specifications, and more. For EVs that are not severely damaged, EVIRS can perform calculations to estimate stranded energy in the EV’s battery and discharge time for various power loads using either EV dashboard information or operational data accessed through the CAN interface. Knowledge of this information may be helpful in the post-crash handling, management, and storage of an EV. The functionality and accuracy of EVIRS were demonstrated through laboratory tests using a 2021 Ford Mach-E and associated data acquisition system. The results indicated that when the remaining driving range was used as an input, EVIRS was able to estimate the pack voltage with an error of less than 3 V. Conversely, when pack voltage was used as an input, the estimated state of charge (SOC) error was less than 5% within the range of 30–90% SOC. Additionally, other features, such as retrieving emergency response guides for identified EVs and accessing lessons learned from archived incidents, have been successfully demonstrated through EVIRS for quick access. EVIRS can be a valuable tool for emergency responders and recovery personnel, both in action and during offline training, by providing crucial information related to assessing EV/battery safety risks, appropriate handling, de-energizing, transport, and storage in an integrated and user-friendly manner.

25 ENERGY STORAGE↗

DOE OSTI2016

Leaner Lifted-Flame Combustion Enabled by the Use of an Oxygenated Fuel in an Optical CI Engine

Leaner lifted-flame combustion (LLFC) is a mixing-controlled combustion strategy for compression-ignition engines that does not produce soot because the equivalence ratio at the lift-off length, Φ(H), is less than or equal to approximately two. In addition to preventing soot formation, LLFC can simultaneously control emissions of nitrogen oxides because it is tolerant to the use of exhaust-gas recirculation for lowering in-cylinder temperatures. LLFC can be achieved through the use of oxygenated fuels and enhanced fuel/charge-gas mixing upstream of the lift-off length. Enhanced mixing can be obtained via higher injection pressures, smaller injector orifice diameters, lower intake-manifold and coolant temperatures, and/or more retarded injection timings. This study focuses on the effects of an oxygenated fuel blend (T50) made up of 50% by volume tri-propylene glycol mono-methyl ether with a #2 ULSD emissions-certification fuel (CFA), compared against baseline testing of the CFA fuel without the oxygenate. Experimental measurements include crank-angle resolved natural luminosity (NL) and chemiluminescence (CL) imaging diagnostics. EGR is simulated by adding nitrogen and carbon dioxide to the intake charge to produce a 16% intake-oxygen mole fraction (XO2), and results are compared against cases with no charge dilution (i.e., 21% XO2). Initial experiments with a two-hole tip achieved soot-free LLFC at low loads with the T50 fuel, 240 MPa injection pressure, 50 °C intake-manifold temperature (IMT), 95 °C coolant temperature, and -5 crank-angle degree (CAD) after top-dead-center (ATDC) start of combustion (SOC) timing. The strategy was extended to more moderate loads by employing a 6-hole injector tip, where lowering the IMT to 30 °C, reducing the coolant temperature to 85 °C, and retarding the SOC timing to +5 CAD ATDC resulted in sustained LLFC at both 21% and 16% XO2 at up to 6.2 bar gross indicated mean effective pressure (gIMEP) with T50. The achievement of LLFC was confirmed by independent soot measurements using a smoke meter, spatially integrated natural luminosity from the NL diagnostics, and laser-induced incandescence for measuring soot volume fraction in the exhaust stream. In contrast to the results with T50, LLFC was not achieved under any of the test conditions with CFA. Combustion was stable under LLFC conditions, with a coefficient of variation of gIMEP less than 1.5%. Nitrogen oxides and hydrocarbon emissions were also lowered by up to 25% each for LLFC with T50 relative to using the same operating conditions with CFA. Combustion noise was similarly reduced by 4-6 dBA, and ringing intensity by 60-80%, for LLFC with T50.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Data from: Soil carbon change in intensive agriculture after 25 years of conservation management

Changes in soil organic carbon (SOC) and nitrogen (SON) are strongly affected by land management, but few long-term comparative studies have surveyed changes throughout the whole soil profile. We quantified 25-year SOC and SON changes to 1 m in 10 replicate ecosystems at an Upper Midwest, USA site. We compared four annual cropping systems in maize ( Zea mays )-soybean ( Glycine max)-winter wheat ( Triticum aestivum ) rotations, each managed differently (Conventional, No-till, Reduced input, and Biologically based); in three managed perennial systems (hybrid Poplar ( Populus × euramericana ), Alfalfa ( Medicago sativa ), and Conifer ( Pinus spp.); and in three successional systems (Early, Mid- and Late succession undergoing a gradual change in species composition and structure over time).

agricultural sciences↗

DOE OSTI2018

Global soil consumption of atmospheric carbon monoxide: an analysis using a process-based biogeochemistry model

Carbon monoxide (CO) plays an important role in controlling the oxidizing capacity of the atmosphere by reacting with OH radicals that affect atmospheric methane (CH 4 ) dynamics. We develop a process-based biogeochemistry model to quantify the CO exchange between soils and the atmosphere with a 5 min internal time step at the global scale. The model is parameterized using the CO flux data from the field and laboratory experiments for 11 representative ecosystem types. The model is then extrapolated to global terrestrial ecosystems using monthly climate forcing data. Global soil gross consumption, gross production, and net flux of the atmospheric CO are estimated to be from -197 to -180, 34 to 36, and -163 to -145 Tg CO yr -1 (1 Tg = 10 12 g), respectively, when the model is driven with satellite-based atmospheric CO concentration data during 2000–2013. Tropical evergreen forest, savanna and deciduous forest areas are the largest sinks at 123 Tg CO yr -1 . The soil CO gross consumption is sensitive to air temperature and atmospheric CO concentration, while the gross production is sensitive to soil organic carbon (SOC) stock and air temperature. By assuming that the spatially distributed atmospheric CO concentrations (~ 128 ppbv) are not changing over time, the global mean CO net deposition velocity is estimated to be 0.16–0.19 mm s -1 during the 20th century. Under the future climate scenarios, the CO deposition velocity will increase at a rate of 0.0002–0.0013 mm s -1 yr -1 during 2014–2100, reaching 0.20–0.30 mm s -1 by the end of the 21st century, primarily due to the increasing temperature. Areas near the Equator, the eastern US, Europe and eastern Asia will be the largest sinks due to optimum soil moisture and high temperature. The annual global soil net flux of atmospheric CO is primarily controlled by air temperature, soil temperature, SOC and atmospheric CO concentrations, while its monthly variation is mainly determined by air temperature, precipitation, soil temperature and soil moisture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Biochemical and structural controls on the decomposition dynamics of boreal upland forest moss tissues

Mosses contribute an average of 20 % of boreal upland forest net primary productivity and are frequently observed to degrade slowly compared to vascular plants. If this is caused primarily by the chemical complexity of their tissues, moss decomposition could exhibit high temperature sensitivity (measured as Q 10 ) due to high activation energy, which would imply that soil organic carbon (SOC) stocks derived from moss remains are especially vulnerable to decomposition with warming. Alternatively, the physical structure of the moss cell-wall biochemical matrix could inhibit decomposition, resulting in low decay rates and low temperature sensitivity. We tested these hypotheses by incubating mosses collected from two boreal forests in Newfoundland, Canada, for 959 days at 5°C and 18°C, while monitoring changes in the moss tissue composition using total hydrolyzable amino acid (THAA) analysis and 13 C nuclear magnetic resonance (NMR) spectroscopy. Less than 40 % of C was respired in all incubations, revealing a large pool of apparently recalcitrant C. The decay rate of the labile fraction increased in the warmer treatment, but the total amount of C loss increased only slightly, resulting in low Q 10 values (1.23–1.33) compared to L horizon soils collected from the same forests. NMR spectra were dominated by O-alkyl C throughout the experiment, indicating the persistence of potentially labile C. The accumulation of hydroxyproline (derived primarily from plant cell-wall proteins) and aromatic C indicates the selective preservation of biochemicals associated with the moss cell wall. This was supported by scanning electron microscope (SEM) images of the moss tissues, which revealed few changes in the physical structure of the cell wall after incubation. This suggests that the moss cell-wall matrix protected labile C from microbial decomposition, accounting for the low temperature sensitivity of moss decomposition despite low decay rates. Climate drivers of moss biomass and productivity, therefore, represent a potentially important regulator of boreal forest SOC responses to climate change that needs to be assessed to improve our understanding of carbon–climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Combined Mesonet and Tracker

Title: Combined Mesonet and Trackers (UNL Mobile Mesonets) Authors University of Nebraska PI: Adam Houston, UNL Professor (ahouston2@unl.edu) Mailing Address: 126 Bessey Hall P.O. Box 880340 Lincoln, NE 68588-0340 CoMeT Overview The University of Nebraska-Lincoln operates three Combined Mesonet and Tracker (CoMeTs). CoMeTs are Ford Explorers (model years 2015, 2017, and 2019) with forward-mounted suites of meteorological sensors and dual moonroofs, combining the capability of a mobile mesonet to collect near-surface observations with the capability of an unmanned aircraft systems (UAS) tracker vehicle, which enables an observer in the second row of seats to see the aircraft and maintain compliance with Federal Aviation Administration policies on UAS operation. The CoMeTs collect observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155A, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, and vehicle heading using a KVH Industries C-100 fluxgate compass (Barbieri et al. 2019). The HMP155A and 109SS-L thermistor are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). This list of sensors is also included in the CoMeT data file metadata. Manufacturer specifications for these instruments are given in Table 1 of Hanft and Houston (2018). The reported measured quantities are summarized below.CoMeT-3 was funded through an equipment allocation included in the NSF TORUS award (AGS-1824649). Instrument Description The specific sensors included on each CoMeT are summarized in the table at the end of this section. In general each CoMeT collects observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210 barometer with a Gill pressure port, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, position using a Garmin 19x HVS receiver, and vehicle heading using a KVH Industries C-100 fluxgate compass. The HMP155 and 109SS are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). Fast temperature and corrected RH measurements (using sensors housed within the U-tube) have a time constant of 10-12 s based on data collected across a temperature and RH shock during the CLOUD-MAP 2017 calibration/validation tests on June 26, 2017. Vehicle speed was < 10 kts for this test. CoMeT-1 CoMeT-2 CoMeT-3 Slow Temperature Slow RH Vaisala HMP155A-L20-PT Part #: 22280-7 Vaisala HMP155E Part #: E1AA11A0B1A1A0A Vaisala HMP155E Part #: E1AA11A0B1A1A0A Fast temperature Campbell Scientific 109SS-L20-PT Part #: 21448-3 Campbell Scientific 109SS-L12-PW Part #: 21448-109 Campbell Scientific 109SS-L12-PW Part #: 21448-150 Pressure Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Wind RM Young 05103-L20-PT Part #: 18435-310 RM Young 05103-L20-PW Part #: 18435-244 RM Young 05103-L20-PW Part #: 18435-244 GPS Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Compass KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 Logger Campbell Scientific CR6-NA-XT-SW Part #: 28385-9 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Data Collection and Real-Time Processing The reported measured quantities are summarized in the table below. Quantity Units Source Epoch time Seconds GPS Latitude and longitude Degrees GPS Altitude m GPS Pressure hPa PTB210 Temperature (fast) deg C 109SS-L Temperature (slow) deg C HMP155 RH (slow) % HMP155 Vehicle speed m/s GPS Vehicle heading deg C-100 and GPS In addition to the measured variables, several derived variables are calculated. Corrected/fast relative humidity (%) Relative humidity is adjusted to the fast temperature following Richardson et al. (1998) and Houston et al. (2016). Water vapor mixing ratio (g/kg) Dew point temperature (°C) Potential temperature (Kelvin) Virtual potential temperature (Kelvin) Equivalent potential temperature (Kelvin) Regular intercomparisons between all three CoMeTs were performed during TORUS 2019. Comparisons were also conducted between CoMeT-1 and CoMeT-2 during LAPSE-RATE (2018) on 14 July. In these intercomparisons, the vehicles were parked adjacent to each other aligned perpendicular to (and facing into) the wind. To minimize engine heating effects, intercomparisons were only conducted when the wind speed was >10 kts. Data Format Original data files for each deployment are saved as text files and then converted to NetCDF. NetCDF versions have units that are CF compliant and may not match the original units in the txt files. The naming convention for the NetCDF files is as follows: UNL.CoMeT3.{deployment date YYYYMMDD}.{start time of observation collection in UTC HHMM}.L2.{post-processing codes}.cdf example: UNL.CoMeT3.20190627.1931.L2.g1.f1.cdf Post-processing codes are included to track modifications to the raw data. These codes are closely connected to error flags associated with each record. Each letter corresponds to a particular instrument: g: GPS p: Barometer tf: Fast temperature ts: Slow temperature rh: Relative humidity f: Compass w: Wind monitor a: All instruments Each number corresponds to a particular post-processing action described more below. Measured and derived variables are included in the following table. Variable Heading Standard Name Units time Time seconds since 00:00:00, 01-01-1970 Alt Altitude meters lat Latitude degrees north lon Longitude degrees east fast_temp Air Temperature kelvin slow_temp Air Temperature kelvin pressure Air Pressure pascals logger_RH Relative Humidity percent calc_corr_RH Relative Humidity percent wind_speed Wind Speed meters per second wind_dir Wind From Direction degrees vehicle_dir Vehicle Direction degrees dewpoint Dew Point Temperature kelvin mixing_ratio Humidity Mixing Ratio g/g theta Air Potential Temperature kelvin theta_v Virtual Potential Temperature kelvin theta_e Equivalent Potential Temperature Kelvin error_flag The error_flag variable is a string that matches the post-processing codes listed above. All instruments will have an associated code, but will have a “0” if the datum is unchanged from the initial processed value. Error Codes The following table summarizes the error codes for data collected before 2020: Error Code Relevant CoMeT Description g1 1,2,3 Exact correction. GPS position and time reprocessed from raw data g2 1 As far as we can tell this is an exact correction to an error in the GPS time. During the correct time periods the time suddenly went backwards ~250s and stayed at this offset for 750s when it corrected itself. The offset was applied to the “time warp” period. p1 2 Approximate correction. Hole in the pressure tube connecting the pressure port to the barometer. Resulted in erroneously low air pressure measurements when the vehicle was in motion. Derived variables recalculated (dew point temperature [e depends on qv and p], water vapor mixing ratio, potential temperature, virtual potential temperature, equivalent potential temperature) a1 3 Exact correction. Missing data reprocessed from raw data a2 1 Bug fix to bias correction for ts1, ts2, and rh1: water vapor mixing ratio was off by a factor of 10 and virtual potential temperature was wrong because of this. f1 3 No correction, missing data. Fluxgate compass inoperable. Wind speed and direction calculated using GPS-derived vehicle heading instead. rh1 1 Approximate correction. Constant bias of +1.7% removed from relative humidity. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts1 1 Approximate correction. Constant bias of +0.6 K removed from slow temperature. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts2 1 Approximate correction. Constant bias of +1.0 K removed. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) References Bolton, D., 1980: The Computation of Equivalent Potential Temperature. Mon. Wea. Rev., 108, 1046–1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2. Hanft, W., and A. L. Houston, 2018: An Observational and Modeling Study of Mesoscale Air Masses with High Theta-E. Mon. Wea. Rev., 146, 2503–2524, https://doi.org/10.1175/MWR-D-17-0389.1.Wexler Houston, A. L., R. J. Laurence III, T. W. Nichols, S. Waugh, B. Argrow, and C. L. Ziegler, 2016: Intercomparison of unmanned aircraft-borne and mobile mesonet atmospheric sensors. Journal of Atmospheric and Oceanic Technology. 33, 1569-1582, doi: 10.1175/JTECH-D-15-0178.1. Lowe, P. R., 1977: An Approximating Polynomial for the Computation of Saturation Vapor Pressure. J. Applied Meteorology, 16, 100–103. Richardson, S. J., S. E. Frederickson, F. V. Brock, and J. A. Brotzge, 1998: Combination temperature and relative humidity probes: Avoiding large air temperature errors and associated relative humidity errors. Preprints, 10th Symp. On Meteorological Observations and Instrumentation, Phoenix, AZ, Amer. Meteor. Soc., 278–283. Waugh, S., and S. E. Frederickson, 2010: An improved aspirated temperature system for mobile meteorological observations, especially in severe weather. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P5.2. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_176205.htm.]

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

CERF: IM3 Projected Western US Power Plant Locations

Overview The Capacity Expansion Regional Feasibility (CERF) model is an open-source geospatial python package that provides new power plant locations at a 1km resolution. The model ingests U.S. state or regional-scale electricity system capacity expansion plans, such as those produced by the Global Change Analysis Model (GCAM-USA), and identifies feasible, site-specific locations for individual new power plants (renewable and non-renewable). CERF combines high-resolution geospatial suitability analyses with an economic algorithm that selects individual plant siting locations based on grid interconnection costs and the locational marginal value of new generation. The model incorporates a wide range of dynamic constraints and opportunities, such as protected lands, population density, existing infrastructure, and water availability. This dataset provides CERF power plant siting results for IM3 Phase 2 simulations across eight different scenarios for the Western US through 2055. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States (see, https://tgw-data.msdlive.org/). These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 CERF siting results in this dataset correspond to capacity expansion plans in the GCAM-USA IM3 Phase 2 simulation data and are available for each of the above scenarios. Data Details Temporal Range: 2015-2055 in 5-year timesteps. Note that 2015 is the experiment base year and 2020 and beyond represent model simulation years. Spatial Range: Plant locations are provided for the eleven states in the Western US including Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, and Wyoming. Spatial Resolution: 1 km-squared, provided in x and y coordinates Geospatial Projection: Albers Equal Area Conic (ESRI:102003) File Type: csv The dataset contains subdirectories for each of the eight scenarios described in the overview. Each scenario folder contains two subfolders with the following information: 1. Power Plant Data This directory contains a single .csv file of power plant locations for both pre-existing (non-CERF sited plants in operation in 2015) and new (CERF-sited) power plants across the temporal range along with additional CERF model output parameters for CERF-sited plants. Plant with a siting year earlier than 2020 correspond to facilities that are operational leading into the first timestep CERF simulation. For a more detailed description of CERF model output parameters, see the CERF model documentation. Note that the cerf_plant_id parameter is unique within each scenario file but not across scenario files. Parameter Descriptions scenario - Name of scenario cerf_plant_id - Unique siting identifier cerf_sited - If True, indicates that plant was sited by CERF model. If False, indicates pre-existing facility region_name - Name of region (state) tech_id - Technology ID tech_name - Full generation technology name inclusive of cooling type (if applicable) and additional characteristics tech_simple - Simplified generation technology type unit_size_mw - Power plant unit size (MW) xcoord - X coordinate in the default CRS (meters) ycoord - Y coordinate in the default CRS (meters) index - Index position in the flattend 2D array buffer_in_km - Exclusion buffer around site (km) sited_year - Year of siting retirement_year - Year of retirement lmp_zone - Locational marginal price (LMP) zone ID locational_marginal_price_usd_per_mwh - Locational marginal price ($/MWh) generation_mwh_per_year - Generation output (MWh/yr) operating_cost_usd_per_year - Cost of plant operations ($/yr) net_operational_value - Net operational value based on LMP and and operating costs ($/yr) interconnection_cost - Cost of interconnection for transmission & gas pipeline (if applicable) net_locational_cost -- Difference of interconnection cost and operating value ($/yr) capacity_factor_fraction - Capacity factor (fraction) carbon_capture_rate_fraction - Carbon capture rate (fraction) fuel_co2_content_tons_per_btu - Fuel CO2 content (tons/Btu) fuel_price_usd_per_mmbtu - Fuel price ($/MMBtu) fuel_price_esc_rate_fraction - Fuel price escalation rate (fraction) heat_rate_btu_per_kWh - Heat rate (Btu/kWh) lifetime_yrs - Technology lifetime for annuity (years) operational_life_yrs - Operational lifetime for retirement (years) variable_om_usd_per_mwh - Variable operation and maintenance costs of yearly capacity use ($/MWh) variable_om_esc_rate_fraction - Variable operation and maintenance costs escalation rate (fraction) carbon_tax_usd_per_ton - Carbon tax ($/ton) carbon_tax_esc_rate_fraction - Carbon tax escalation rate (fraction) 2. Storage Data This directory contains information on new and pre-existing energy storage facilities operational in each timestep along with various storage operational parameters. The 2015 timestep provides pre-existing energy storage data and corresponds with facilities that are operational leading into the first model simulation timestep. Note that coordinates in the storage files correspond to the interconnection point on the grid (substation location), not individual energy storage locations. Energy storage is added in a cumulative process at each given interconnection point. That is, each individual file provides the total operational storage capacity interconnected to the specified substation for the given timestep, inclusive of previously installed storage at that location and new storage installed in that timestep at that location. Parameters scenario - Name of scenario timestep - Simulation timestep name - Unique storage identifier s_typ - Type of energy storage technology (battery or pumped storage hydro) s_node - Node ID of interconnecting substation xcoord - X coordinate in the default CRS (meters) ycoord - Y coordinate in the default CRS (meters) charge_rate - Maximum charge rate (power capacity) of storage system (MW) discharge_rate - Maximum discharge rate (power capacity) of storage system (MW) duration - Duration of storage system (hours) max_SoC - Allowed maximum state of charge (energy capacity) of storage system (MWh) min_SoC -Allowed minimum state of charge (energy capacity) of storage system (MWh) charge_eff - Efficiency of charge (fraction between 0 and 1) discharge_eff - Efficiency of discharge (fraction between 0 and 1) Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program.

CERF↗

NASA NTRS1979

Silicon on Ceramic Process: Silicon Sheet Growth and Device Development for the Large-area Silicon Sheet and Cell Development Tasks of the Low-cost Solar Array Project

The technical and economic feasibility of producing solar cell quality sheet silicon was investigated. It was hoped this could be done by coating one surface of carbonized ceramic substrates with a thin layer of large-grain polycrystalline silicon from the melt. Work was directed towards the solution of unique cell processing/design problems encountered with the silicon-ceramic (SOC) material due to its intimate contact with the ceramic substrate. Significant progress was demonstrated in the following areas; (1) the continuous coater succeeded in producing small-area coatings exhibiting unidirectional solidification and substatial grain size; (2) dip coater succeeded in producing thick (more than 500 micron) dendritic layers at coating speeds of 0.2-0.3 cm/sec; and (3) a standard for producing total area SOC solar cells using slotted ceramic substrates was developed.

Chapman, P. W.↗

NASA NTRS1979

Silicon Solar Cell Process Development, Fabrication and Analysis

The standard solar cells (2x2 cm) from the cast silicon (HEM) showed a maximum AMO efficiency of 10.1%. Cells from the low resistivity material (0.5 ohm-cm) showed lower performance than those of the high resistivity cast silicon (3 ohm-cm), an average efficiency 9.5% versus 7.6%. Maximum AMO efficiency of the standard solar cells from the EFG (RH) ribbons was about 7.5%. The solar cells from the controlled SiC, using the displaced die, showed more consistent and better performance than those of the uncontrolled SiC ribbons, an average efficiency of 6.6% versus 5.4%. The average AMO efficiency of the standard SOC solar cells were about 6%. These were large area solar cells (an average area of 15 sq cm). A maximum efficiency of 7.3% was obtained. The SOC solar cells showed both leakage and series resistance problems, leading to an average curve fill factor of about 60%.

Yoo, H. I.↗

NASA NTRS1979

Silicon Solar Cell Process Development, Fabrication and Analysis

The standard solar cells (2x2 cm) from the cast silicon heatexchanger method) showed a maximum AMO efficiency of 10.1%. Cells from low resistivity material (0.5 ohm-cm) showed lower performance than those of the high resistivity cast silicon (3 ohm-cm), an average efficiency 9.5% versus 7.6% Maximum AMO efficiency of the standard solar cells (2x2 cm) from the EFG (RH) ribbons was about 7.5%. The solar cells from controlled SiC, using the displaced die, showed more consistent and better performance than those of the uncontrolled SiC ribbons, an average efficiency of 6.6% versus 5.4% The average AMO efficiency of the standard silicon ceramic (soc) solar calls were about 6%. These were large area solar cells (an average area of 15 sq cm). A maximum efficiency of 7.3% was obtained. The SOC solar cells showed both leakage and series resistance problems, leading to an average curve fill factor of about 60%.

Yoo, H. I.↗

NASA NTRS1981

Silicon-on ceramic process: Silicon sheet growth and device development for the large-area silicon sheet task of the low-cost solar array project

The technical feasibility of producing solar-cell-quality sheet silicon to meet the Department of Energy (DOE) 1986 overall price goal of $0.70/watt was investigated. With the silicon-on-ceramic (SOC) approach, a low-cost ceramic substrate is coated with large-grain polycrystalline silicon by unidirectional solidification of molten silicon. This effort was divided into several areas of investigation in order to most efficiently meet the goals of the program. These areas include: (1) dip-coating; (2) continuous coating designated SCIM-coating, and acronym for Silicon Coating by an Inverted Meniscus (SCIM); (3) material characterization; (4) cell fabrication and evaluation; and (5) theoretical analysis. Both coating approaches were successful in producing thin layers of large grain, solar-cell-quality silicon. The dip-coating approach was initially investigated and considerable effort was given to this technique. The SCIM technique was adopted because of its scale-up potential and its capability to produce more conventiently large areas of SOC.

Grung, B. L.↗

NASA NTRS1982

Space Operations Center system analysis study extension. Volume 1: Executive summary

The analysis fo Space Operations Center (SOC) systems is summarized. Design considerations, configurations of the manned orbital space station, planned operational and research missions, and subsystem tradeoffs are considered. Integration into the space transportation system is discussed. A modular design concept permitting growth of the SOC as its functions are expanded is described. Additional considerations are special requirements for habitat modules, design modifications needed to operate in geosynchronous orbits, and use of the external tank for cryogenic propellant storage or as a pressurized hangar. A cost summary is presented.

Source record↗

NASA NTRS1981

Space Operations Center, Shuttle Interaction Study. Volume 2: Appendices, Book 1 of 2

The feasibility of shuttle orbiter docking to the Space Operations Center (SOC) is studied. The in-orbit relative motion of the free flying orbiter and SOC was simulated, accounting for the Orbiter RCS and digital autopilot (DAP) systems, orbital mechanics, center of gravity offset of the orbiter docking port, aero and gravity gradient effects, and other pertinent natural and man-made phenomena. Since there is no specified flight path and procedure for docking, terminal closure sensitivities were investigated. Orbiter approach direction, Orbiter approach attitude out of plane, DAP thruster compensation mode, final ballistic docking distance and time to dock, rate and excursion attitude deadbands, and selection of various thruster combinations (differing from nominal) for translational pulses are considered.

Source record↗