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1,274 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Oxygen Solubility in Aqueous Amine Solvents with Common Additives Used for CO 2 Chemical Absorption

Major sources of anthropogenic CO 2 are power generation and transportation industries where researcher continue to explore CO 2 emission mitigation strategies as applied to these CO 2 sources through carbon capture, utilization, and sequestration (CCUS). The most mature CO 2 capture technology is post-combustion carbon capture (PCCC) using aqueous amine solutions/solvents, however solvent degradation and regeneration costs are slowing the widespread adoption of PCCC. Solvent degradation of the aqueous amine solutions is mainly caused from the temperature gradient between the absorber and stripper columns and common flue gas components such as SOx, NOx, and oxygen (O 2 ). The two main classifications of solvent degradation are thermal degradation, occurring when the amine reacts with itself at elevated temperatures and anaerobic conditions and oxidative degradation. Oxidative degradation reactions can occur from oxygen mass transfer and free radical oxidation. Metals, such as iron from the corrosion of steel structures used in industrial applications such as PCCC, can help to catalyze these reactions. Various oxidative degradation studies have shown how O 2 concentrations in flue gas and, to a lesser extent, temperature influence the extent of oxidative degradation. Accurately measuring the O 2 solubility, commonly referred to as dissolved oxygen (DO), in a quick, continuous, and efficient manner in aqueous amine solvents should contribute to determine the effectiveness of mitigation strategies for oxidative degradation. Knowing that commercial PCCC amine solvents contain components beyond water, amine, carbonate species and CO 2 , this investigation was conducted to determine the oxygen solubility changes of common aqueous amines solutions with commonly used and published solvent additives. The impact of carbon loadings with and without the additives was also examined. A commercial dissolved oxygen probe was used to measure the DO concentrations and compared them against standard Winkler Method titration values. The results show that antifoam shows minimal change in [DO]. MBT yielded lower DO values, and NaVO 3 showed a higher DO concentration due to interferences. These results indicate that most common amine solvent additives should be expected to minimally impact oxygen solubility and amine oxidative degradation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Understanding Inlet Concentration Effects on the Electrocatalytic Conversion of CO 2 to Formic Acid in Gas-Fed Electrolyzers

The electrochemical CO 2 reduction reaction (CO2RR) to produce value-added products remains a developing technology for utilizing waste CO 2 streams. Most device-level CO2RR studies use pure CO 2 gas feeds; however, the effect of dilute CO 2 on the electrolyzer performance is an important consideration for large-scale electrolyzer operation, single-pass conversion, and real-world CO 2 source utilization. This work investigates the effect that the CO 2 concentration has on the performance of formic acid (HCOOH) producing tin oxide (SnO 2 ) and bismuth oxide (Bi 2 O 3 ) catalysts in an electrolyzer device setting. Surprisingly, SnO2 demonstrated an approximately 20% increase in HCOOH selectivity (Faradaic efficiency) when the CO 2 concentration decreased from 100 to 20%. In contrast, Bi 2 O 3 consistently demonstrated high selectivity toward HCOOH across the same CO 2 concentration range. The effects of the CO 2 concentration on selectivity were further investigated with half-cell experiments and in situ Raman spectroscopy, which revealed dynamic changes in the cathodic overpotential and chemical state of the catalyst that depended on the CO 2 concentration. Density functional theory calculations showed how changes in the surface oxidation state of Sn, varying from fully oxidized SnO 2 to metallic Sn(0), affect the thermodynamic barriers of the three main observed products: HCOOH, CO, and H 2 . Our results indicate that dilute CO 2 concentrations required larger cathodic overpotentials to sustain a fixed current density, which, in turn, pushed the Sn-based catalyst toward a more reduced surface that was favorable to HCOOH formation. On the other hand, the Bi-based catalyst remained in a metallic state at CO2RR-relevant potentials and demonstrated a consistent product selectivity regardless of CO 2 concentration. These findings highlight how varying the CO 2 inlet gas concentrations affects the chemical state of catalysts and the resulting performance metrics.

42 ENGINEERING↗
NASA NTRSDate not supplied

A Simulated High CO 2 Spaceflight Environment Increases Plant Preference for Ammonium as a Nitrogen Source.

Future long-duration missions will require a sustainable supply of food to support human crews. The spaceflight cabin environment often contains very high concentrations of CO 2 due to release of CO 2 by astronauts that is not completely scrubbed from the cabin, and it is therefore crucial to understand plant responses to elevated CO 2 (eCO 2 ) environments. Much focus has been given to changes in plant photosynthetic and performance parameters in response to eCO 2 , but the effects of eCO 2 on nitrogen (N) uptake are poorly understood. Shoot nitrate reduction may be reduced at eCO 2 , likely due to less reductant available for nitrate reduction because of reduced photorespiration and increased carbon fixation 1. Relative growth rate can be reduced at eCO 2 when N is provided only as nitrate and can be unaffected when N is supplied as ammonium. However, N uptake in response to eCO 2 has, to our knowledge, not yet been studied. An increased ‘preference’ for plants to take up N as ammonium at eCO 2 could have important implications for growth in the space environment, where N is currently only supplied as nitrate. In this study, novel stable isotope approaches were used in conjunction with hydroponics and isotope ratio mass spectrometry to determine the effect of eCO 2 on N preference for ammonium or nitrate in spring barley and lettuce when both N forms are provided equally. Several varieties of spring barley displayed increased ammonium preference at eCO 2 (720 ppm) compared to ambient CO 2 (410 ppm), though this was not true for all varieties. In most cases, increases in ammonium preference were driven by increases in ammonium uptake at eCO 2 and not decreases in nitrate uptake. Current research is assessing whether similar responses are observed in the candidate space crop lettuce, at levels of CO 2 like those observed on ISS (3000 ppm), and these results will also be presented. This work will enable the development of optimized nutrient regimes for candidate crops in space environments and the selection of crop varieties adapted to eCO 2 environments. Plants adapted to ammonium nutrition may play a role in future plant-based bioregenerative life support systems with higher ammonium concentrations due to waste recycling. Moreover, this research will further our understanding of plant responses to the eCO 2 environment brought about by climate change, allowing the development of future-proof crops that will help to maintain food security.

nitrogen↗
NASA NTRS2022

Are Soybean Models Ready for Climate Change Food Impact Assessments?

An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO 2 concentration [CO 2 ] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO 2 ]. For example, under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield. Similarly, when increasing [CO 2 ] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble, ensemble mean yield responses to temperature and [CO 2 ] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO 2 ] and temperature is needed to reduce the uncertainty from the individual models.

Agricultural Model Inter-comparison and Improvemen↗
DOE OSTI2023

Grid-Integrated Production of Fischer-Tropsch Synfuels from Nuclear Power

Idaho National Laboratory (INL) investigates the relative economic profitability of an integrated energy system (IES) coupling an NPP with a synfuel production process at selected case study locations across the United States. In the synfuel IES, a high-temperature steam electrolysis (HTSE) plant is thermally and electrically coupled with an NPP to produce zero-carbon hydrogen. The synthetic fuel is produced from this H 2 combined with a CO 2 supply using the reverse water gas shift process followed by the Fischer-Tropsch (FT) reaction. This analysis considers a system in which the CO 2 is sourced from regional CO 2 emitters via the construction and operation of pipeline-based CO 2 supply networks. Locating the FT plant at the same site as the NPP and HTSE plants enables the NPP to provide zero-carbon heat and power to the HTSE plant and zero-carbon power to the FT plant as well as avoid the requirement for long-distance H 2 product transport from the HTSE plant to the FT plant. Hydrogen storage is used to enable the NPP to dispatch power to the electrical grid (instead of the HTSE plant) when grid demand increases, thus enabling the FT plant to continue to operate in a steady-state production mode. The ability to cease hydrogen production for several hours within each day enables the NPP to provide power to the grid to balance the electricity market during peak periods and maximize revenues for the nuclear synfuel IES. The FT process design considered has a 99% carbon conversion efficiency. The use of nuclear energy and nuclear energy-derived hydrogen enables synfuel production to achieve this high level of carbon utilization. Additionally, the life-cycle carbon emissions of the nuclear-based synfuel production process are very low, with WTW emissions of approximately 25 gCO 2 e/MJ, including steam credits (generated from FT process excess heat), and approximately 7 gCO 2 e/MJ, if steam credits are excluded. This compares favorably with the WTW emissions of 90.5 gCO 2 e/MJ for a compression-ignition, direct injection (CIDI) vehicle with a fuel economy of 31.6 miles per gallon gasoline equivalent (MPGGE), using low-sulfur diesel produced using conventional petroleum production and refining processes. Several NPPs in various regions of the U.S. are considered as case study analyses. Supply locations and transportation via pipeline of the CO 2 feedstock to the NPP site are analyzed through the National Energy Technology Laboratory (NETL) CO 2 Transport Cost model. The team finds that the amount of CO 2 generated by different sectors is sufficient for the synfuel production process at all locations considered. The CO 2 transportation costs are functions of the distance of the source to the NPP location, the CO 2 capture cost at the source, and the quantity of CO 2 transported. Historical electricity prices for the NPP case study locations are collected and analyzed. Monthly average prices, price range, and duration of negative-price periods vary among these locations. For each location, an auto-regressive moving average (ARMA) model is trained on historical electricity price data. ARMA validation is done to ensure the synthetic price distributions represent one of historical prices with high fidelity. Synthetic time series from these ARMA models are used in a coupled dispatch and system optimization in the Holistic Energy Resource Optimization Network (HERON) to compute the differential net present value (NPV) of the IES. The team finds that this econometric is positive, ranging from $14M–1.3bn (2020) depending on the location. The optimal synfuel IES configuration to obtain this increase in NPV often maximizes the size of the synfuel production process with regards to the size of the NPP. However, the team shows that the NPP still plays a stabilizing role for the grid: In periods of high prices and high loads, more electricity from the NPP is sent to the grid. A high variability of electricity prices and extreme maximum prices tend to drive up electricity production. While it requires significant investment, the synfuel IES could increase the economic profitability for the existing fleet of LWRs across the country while still maintaining the grid stabilizer role of NPPs. During its lifetime, the main costs for the nuclear synfuel IES are the carbon feedstock transportation costs, followed by the capital expenses (CAPEX) and operation and maintenance (O&M) costs while the revenue comes first from the IRA H 2 production tax credit (PTC) and then from the sales of synfuel products. The profitability of the synfuel IES is most sensitive to the value of the hydrogen PTC and the synfuel products as well as the cost of the carbon feedstock, highlighting the importance of governmental incentives regarding hydrogen, carbon emissions, and synfuel in driving the deployment of future nuclear synfuel IESs.

08 HYDROGEN↗
DOE OSTI2022

Steric and Lewis Basicity Influence of the Second Coordination Sphere on Electrocatalytic CO 2 Reduction by Manganese Bipyridyl Complexes

This study aims to provide a greater insight into the balance between steric (bpy vs (Ph) 2 bpy vs mes 2 bpy ligands) and Lewis basic ((Ph) 2 bpy vs (MeOPh) 2 bpy vs (MeSPh) 2 bpy ligands) influence on the efficiencies of the protonation-first vs reduction-first CO 2 reduction mechanisms with [Mn I (R 2 bpy)(CO) 3 (CH 3 CN)] + precatalysts, and on their respective transition-state geometries/energies for rate-determining C–OH bond cleavage toward CO evolution. The presence of only modest steric bulk at the 6,6'-diphenyl-2,2'-bipyridyl ((Ph) 2 bpy) ligand has here allowed unique insight into the mechanism of catalyst activation and CO 2 binding by navigating a perfect medium between the nonsterically encumbered bpy-based and the highly sterically encumbered mes2bpy-based precatalysts. Cyclic voltammetry conducted in CO 2 -saturated electrolyte for the (Ph) 2 bpy-based precatalyst [2-CH 3 CN] + confirms that CO 2 binding occurs at the two-electron-reduced activated catalyst [2] – in the absence of an excess proton source, in contrast to prior assumptions that all manganese catalysts require a strong acid for CO 2 binding. This observation is supported by computed free energies of the parent–child reaction for [Mn–Mn] o dimer formation, where increased steric hindrance relative to the bpy-based precatalyst correlates with favorable CO 2 binding. A critical balance must be adhered to, however, as the absence of steric bulk in the bpy-based precatalyst [1-CH 3 CN] + maintains a lower overpotential than [2-CH 3 CN] + at the protonation-first pathway with comparable kinetic performance, whereas an ~2-fold greater TOF max is observed at its reduction-first pathway with an almost identical overpotential as [2-CH 3 CN] + . Notably, excessive steric bulk in the mes2bpy-based precatalyst [3-CH 3 CN] + results in increased activation free energies of the C–OH bond cleavage transition states for both the protonation-first and the reduction-first pathways relative to both [1-CH 3 CN] + and [2-CH 3 CN] + . In fact, [3-CH 3 CN] + requires a 1 V window beyond its onset potential to reach its peak catalytic current, which is in contrast to the narrower (<0.30 V) potential response window of the remaining catalysts here studied. Furthermore, voltammetry recorded under 1 atm of CO 2 with 2.8 M (5%) H 2 O establishes [2-CH 3 CN] + to have the lowest overpotential (η = 0.75 V) in the series here studied, attributed to its ability to lie “on the fence” when providing sufficient steric bulk to hinder (but not prevent) [Mn–Mn] o dimerization, while simultaneously having a limited steric impact on the free energy of activation for the rate-determining C–OH bond cleavage transition state. While the methoxyphenyl bpy-based precatalyst [4-CH 3 CN] + possesses an increased steric presence relative to [2-CH 3 CN] + , this is offset by its capacity to stabilize the C–OH bond cleavage transition states of both the protonation-first and the reduction-first pathways by facilitating second coordination sphere H-bonding stabilization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Spatially Patterned Architectures to Modulate CO 2 Reduction Cascade Catalysis Kinetics

Electrochemical CO 2 reduction using renewable sources of electrical energy holds promise for converting CO 2 into fuels and chemicals. The complex interactions among chemical/electrochemical reactions and mass transport make it difficult to analyze the effect of an individual process on electrode performance based only on experimental methods. Here, we developed a generalized steady-state simulation to describe an electrode surface in which sequential cascade catalysts are patterned in a periodic trench design. If appropriately constructed, this trench geometry is hypothesized to be able to yield a higher net current density for a CO 2 reduction (CO 2 R) cascade reaction. We have used realistic experimental reaction kinetics to investigate the role of trench geometry in mass transport, local microenvironments, and selectivity for a model CO 2 R cascade reaction. The model considers local concentration gradients of bicarbonate species at quasi-equilibrium and catalytic surface reactions based on concentration-dependent Butler–Volmer kinetics. Our results suggest that varying the spatial distribution of active sites plays a significant role in facilitating effective mass transport between active sites, modulating selectivity for the cascade reaction, and enhancing the yield of desirable cascade products. Moreover, we observe that this trench geometry significantly alters the cascade reaction rate by affecting the local pH, which can cause inadvertent depletion of available aqueous CO 2 to limit the CO 2 R cascade kinetics and modest suppression of the hydrogen evolution reaction (HER). The results highlight the trade-offs between mass transport, pH, and reaction kinetics that become apparent only when considering the coupled physics of all processes at the electrode surface. Here, this model can thus serve as a primary tool to build more selective and efficient patterned architectures for the CO 2 R cascade catalysis.

CO2 reduction↗
DOE OSTI2020

Characterizing Pore-Scale Geochemical Alterations in Eagle Ford and Barnett Shale from Exposure to Hydraulic Fracturing Fluid and CO 2 /H 2 O

As demand increases for an affordable energy source that is tied to an environmental obligation to reduce greenhouse gas emissions and water usage, there is a growing consideration in shale production utilizing processes such as 1) enhancing hydrocarbon recovery via carbon dioxide (CO 2 ) flooding, 2) using CO 2 as a fracturing agent to minimize water use, and 3) storing CO 2 in depleted shale formations to mitigate emissions to the atmosphere. Understanding the geochemical reactions and alterations that occur as shale is exposed to fluids and CO 2 is necessary to develop and optimize each of these processes for field applications. While the majority of shale formations are stimulated using traditional fracturing fluid, some may be fractured using CO 2 or other non-traditional means. We examine the effect fracturing fluid has on shale and how it behaves with secondary exposure to dry CO 2 or CO 2 -saturated water using in situ Fourier Transform infrared spectroscopy (FTIR), feature relocation scanning electron microscopy (SEM), and surface area and pore size analysis using volumetric gas sorption. These techniques were performed on Eagle Ford and Barnett shale samples that were exposed to fracturing fluid and unexposed (as received). Shales that have been exposed to traditional fracturing fluid experienced two reaction fronts. The first reaction front was formed during exposure to the fracturing fluid (pH of ~1.4). A secondary reaction front was formed as a result of CO 2 -saturated fluid exposure in the form of carbonic acid (pH ~5.6). These two different reaction mechanisms drove multiple dissolution and precipitation cycles which altered petrophysical properties of the shale and could lead to a significant impact on flow pathways. FTIR showed that equilibration of carbonate dissolution and precipitation cycles could take as long as 35 days. Samples exposed to fracturing fluid showed significantly less carbonate reactivity compared to those exposed to water. Pore size analysis results indicate exposure to fracturing fluid blocked small nanopores (10-15 nm) reducing BET surface area and total pore volume. SEM results show barite precipitated heavily during exposure to fracturing fluid. It appeared that carbonic acid was able to extract sulfur from organic matter to form gypsum evaporites. The mineralogical (barite precipitation and calcite dissolution/precipitation) and pore-scale alterations observed in these samples may lead to enhancement of flow pathways for injected CO 2 or produced hydrocarbons.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2022

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .

42 ENGINEERING↗
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↗
DOE OSTI2025

Influence of extreme temperature conditions on CO 2 direct air capture using amino-acid solutions

Geological features play a pivotal role in determining the feasibility of deploying CO₂ direct air capture (DAC) technologies, primarily because they influence the availability of cost-effective energy sources, such as natural gas and geothermal energy, and also due to the potential for CO₂ sequestration. Many regions face challenges due to variable weather conditions including seasonal temperature fluctuations, high or low humidity, and sub-ambient temperatures. These extremes can reduce DAC performance or even lead to catastrophic events. Aqueous solvents considered for DAC systems are particularly vulnerable to seasonal variations in colder climates, where the solvent may underperform or freeze. It is therefore essential to investigate the CO₂ capture efficiency of aqueous solvents across a broad range of environmental temperatures, spanning sub-zero to hot conditions (>30 °C). In this study, DAC operation is examined using a high-flux solvent–air crossflow contactor under two major weather scenarios: (i) cold conditions below 0 °C and (ii) hot conditions above 30 °C. A parametric study is conducted to investigate the contactor performance regarding CO₂ removal efficiency, uptake capacity, and reaction kinetics versus temperature when the air velocity through the contactor exceeds 1 m/s. The efficacy of the contactor is systematically investigated using various anti-freeze amino-acid solvent formulations. A mass-transfer mechanistic model is developed to assess the process performance over a wide temperature range and propose scalable design guidelines. Machine learning is also employed to identify key parameters affecting the CO₂ capture efficiency. It is shown that air velocity and temperature are the primary factors influencing CO₂ uptake. Based on performance data obtained under subfreezing temperatures, a technoeconomic analysis is conducted to evaluate the feasibility of using aqueous solvents in seasonal cold regions. In conclusion, the findings of this study provide valuable insights into siting considerations for deploying solvent-based DAC, thereby contributing to the advancement of sustainable carbon removal solutions.

Air–liquid contactor↗
DOE OSTI2021

Tracking the phase changes in micelle-based NiGa nanocatalysts for methanol synthesis under activation and working conditions

The hydrogenation of CO 2 into high energy density fuels such as methanol, where the required H 2 is obtained from renewable sources, is of utmost importance for a sustainable society. In recent years, NiGa alloys have attracted attention as promising catalyst material systems for the hydrogenation of CO 2 into methanol at ambient pressures. They thus represent an energy-saving alternative to the Cu-based catalysts employed in today's catalytic industry that require high pressures for the CO 2 hydrogenation. However, the underlying reaction mechanisms for the NiGa system are still under debate. One of the challenges here is to unravel the evolution and coexistence of the different species in the heterogeneous NiGa catalyst system under activation and reaction conditions. To shed light on their evolution under activation in H 2 and their catalytic roles under CO 2 hydrogenation working conditions on well-defined Ni 3 Ga 1 and Ni 5 Ga 3 nanoparticle (NP) catalysts, we employed a multi-probe approach in this study. It included advanced machine learning-based analysis of operando X-ray absorption spectroscopy data combined with operando powder X-ray diffraction and near ambient pressure X-ray photoelectron spectroscopy measurements, as well as reactivity studies using bed-packed mass flow reactors. In addition, we employed atomic force microscopy and scanning transmission electron microscopy for structural characterization. Under H 2 activation at 1 bar total pressure, we concluded the formation of metallic Ni, starting for Ni 3 Ga 1 at 300 °C, and for Ni 5 Ga 3 at 400 °C. At higher temperatures, the formation of NiGa alloys follows. The α'-Ni 3 Ga 1 alloy phase is predominantly formed for the Ni 3 Ga 1 NPs, while the coexistence of α'-Ni 3 Ga 1 , δ-Ni 5 Ga 3 and Ga 2 O 3 phases is observed for the Ni 5 Ga 3 NPs after the H 2 activation. The formation of the Ga 2 O 3 phase also results in the presence of excess metallic Ni. Under CO 2 hydrogenation reaction conditions, Ga partially oxidizes again to form a Ga 2 O 3 -rich particle shell for both NP compositions, yet, to a larger extent for the Ni 3 Ga 1 NPs, which, in turn, feature a higher amount of excess Ni. We reveal that metallic Ni is responsible for the high selectivity of the Ni 3 Ga 1 NPs towards the production of methane in our catalytic tests. In contrast, the Ni 5 Ga 3 NPs display a strong selectivity toward methanol production (>92%), more than one order of magnitude higher than that for the Ni 3 Ga 1 NPs, which we ascribe to the presence of the δ-Ni 5 Ga 3 phase.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Southwest Regional Partnership on Carbon Sequestration: Phase III (Final Scientific/Technical Report)

The Southwest Regional Partnership on Carbon Sequestration (SWP) is one of 7 regional partnerships formed in 2003 under the U.S. Department of Energy’s (DOE) Regional Carbon Sequestration Partnerships (RCSPs) initiative. The overall purpose of the initiative was to help determine and implement the technology, infrastructure, and regulations most appropriate to promote carbon storage in different regions of the country. Covering Arizona, Colorado, New Mexico, Oklahoma, Utah, and parts of Texas, Wyoming, and Kansas, the SWP evaluated regional carbon storage and utilization potential and focused on technologies and sites that could complement the region’s strong position in energy production. The project progressed through three phases: • Phase I (2003–2005): Characterized regional geologic formations and CO 2 sources, assessed sequestration potential, and identified pilot test sites. • Phase II (2005–2013): Conducted small-scale field tests to validate sequestration methods, including geologic and terrestrial projects. • Phase III (2008–2022): Demonstrated large-scale CO 2 injection at a commercial oil field to test monitoring, verification, and long-term storage strategies. This report covers Phase III. The final project site, the Farnsworth Unit (FWU) in Texas, provided real-world testing of reservoir characterization, monitoring, and risk evaluation tools and processes that could be used in any commercial scale carbon capture, utilization, and storage (CCUS) project. Extensive data collection and analysis helped refine best practices for reservoir characterization, injection monitoring, and storage verification. The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Key lessons learned include the importance of robust data management, strategic site selection, regulatory navigation, and effective industry collaboration. The project’s findings will inform ongoing and future carbon storage initiatives. Task 1 (Regional Characterization) • The SWP continued to participate in national outreach efforts and NATCARB. • The SWP evaluated multiple potential sites before selecting the FWU as the primary field test location. Task 2 (Public Outreach and Education) • The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Task 3 (Permitting and Regulatory Compliance) • The SWP ensured compliance with federal and state regulations, including National Environmental Policy Act (NEPA) requirements. • The SWP obtained all necessary permits for drilling, injection, and monitoring activities. Task 4 (Site Characterization and Planning) • The SWP developed work plans for four key activities: characterization, simulation, monitoring and verification, and risk evaluation. • The SWP collected and synthesized legacy data from multiple sources to build initial static geological models and dynamic reservoir models demonstrating project feasibility. • The SWP conducted an initial risk evaluation and developed mitigation plans. Task 5 (Field Operations and Data Collection) • The SWP drilled, logged, and cored three characterization wells to gather critical subsurface data. • The SWP conducted multiple geophysical surveys, including 3D seismic, crosswell seismic, and vertical seismic profiling, to improve reservoir characterization. Task 6 (Monitoring and Verification) • The SWP performed extensive geological characterization using data from characterization wells and seismic surveys. • The SWP established a surface monitoring network to track CO 2 flux in soil gas, groundwater chemistry, and near-surface atmospheric CO 2 levels. • The SWP built and refined reservoir models to study the effects of relative permeability on simulation behavior and improve calibration with experimental data. Task 7 (Risk Assessment and Model Refinement) • The SWP conducted multiple studies to evaluate reservoir integrity, predict CO 2 plume behavior and improve predictive modeling capabilities. • The SWP refined geological models and used them to enhance the accuracy of simulation models. • The SWP continued quantitative risk assessment of top-ranked risks and strengthened the link between qualitative and quantitative risk methodologies.

02 PETROLEUM↗
DOE OSTI2025

Overestimated natural biological nitrogen fixation translates to an exaggerated CO 2 fertilization effect in Earth system models

CO 2 fertilization of the terrestrial biosphere is limited by nitrogen. Biological nitrogen fixation (BNF) is the dominant natural nitrogen source to the terrestrial biosphere and can alleviate nitrogen limitation but is poorly constrained in Earth system models (ESMs). Here, in this study, we compare terrestrial BNF from an ensemble of ESMs of the 6th Coupled Model Intercomparison Project to a new global synthesis of observations across natural and agricultural biomes. We find that compared to observations, ESMs underestimate agricultural BNF but overestimate natural BNF in the present day by over 50%. Natural BNF is overestimated in the most productive ecosystems that contribute most to the terrestrial carbon sink (forests and grasslands). ESMs with different BNF representations yield a range of BNF responses to CO 2 enrichment. Some ESMs with phenomenological representations of BNF predict a natural BNF increase in response to a doubling of CO 2 that aligns with a meta-analysis of CO 2 enrichment experiments (31% increase) but fail to account for the substantial carbon cost of BNF. In contrast, ESMs with mechanistic representations of BNF account for its carbon cost as well as its regulation by nitrogen limitation but overestimate the BNF response to a doubling of CO 2 (135% increase). Overall, all current BNF representations in ESMs fall short of fully capturing its response to rising atmospheric CO 2 . Finally, we find a positive correlation between modeled present-day natural BNF and the CO 2 fertilization effect across ESMs, suggesting that overestimated natural BNF translates to an exaggerated CO 2 fertilization effect of approximately 11% in ESMs.

Biological nitrogen fixation↗