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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The effects of photosynthetic rate on respiration in light, starch/sucrose partitioning, and other metabolic fluxes within photosynthesis

In the future, plants may encounter increased light and elevated CO 2 levels. How consequent alterations in photosynthetic rates will impact fluxes in photosynthetic carbon metabolism remains uncertain. Respiration in light ( R L ) is pivotal in plant carbon balance and a key parameter in photosynthesis models. Understanding the dynamics of photosynthetic metabolism and R L under varying environmental conditions is essential for optimizing plant growth and agricultural productivity. However, measuring R L under high light and high CO 2 (HLHC) conditions poses challenges using traditional gas exchange methods. In this study, we employed isotopically nonstationary metabolic flux analysis (INST-MFA) to estimate RL and investigate photosynthetic carbon flux, unveiling nuanced adjustments in Camelina sativa under HLHC. Despite numerous flux alterations in HLHC, RL remained stable. HLHC affects several factors influencing RL, such as starch and sucrose partitioning, v o /v c ratio, triose phosphate partitioning, and hexose kinase activity. Analysis of A/C i curve operational points reveals that HLHC’s major changes primarily stem from CO 2 suppressing photorespiration. Integration of these fluxes into a simplified model predicts changes in CBC labeling under HLHC. This study extends our prior discovery that incomplete CBC labeling is due to unlabeled carbon reimported during R L , offering insights into manipulating labeling through adjustments in photosynthetic rates.

Elevated CO2↗
DOE OSTI2022

Enhanced Depolarized Electro-Membrane System (EDEMS) for Direct Capture of Carbon Dioxide from Ambient Air (Final Report)

The goal of this final project report is to summarize the work conducted on project DE-FE0031962. In accordance with the Statement of Project Objectives (SOPO), the University of Kentucky Center for Applied Energy Research (UK CAER) (Recipient) developed an intensified process to capture CO2 from ambient conditions (400 ppm CO2). The process combines low-temperature solvent-aided membrane capture with electrochemically-mediated solvent regeneration to simultaneously capture ambient CO2 while regenerating the solvent. The technology employs only two primary units (regenerator and absorber/contactor) while generating high purity hydrogen as a co-product that can be sold, used for energy storage, or cost-saving depolarization of the direct air capture (DAC) system during the grid peak demand, allowing for flexible operation. When depolarization is employed, the operating voltage is reduced by more than 1 V. Since the technology is powered directly by DC electricity, it can seamlessly tie in with power sources like solar cells without the need for AC/DC converters, therefore allowing for a remote operation to further mitigate greenhouse gas generation toward deploying a negative carbon emissions technology that is completely decoupled from the carbon emissions from the power source for the DAC unit. The project results verified that UK CAER’s integrated approach addressed the complexities of incumbent DAC systems by demonstrating at ambient conditions, including (1) low gas-side pressure-drop facile CO2 capture via a membrane contactor with in-situ generated hydroxide as capture solvent, (2) multi-functional electrochemical regenerator for hydroxide regeneration, CO2 concentration and hydrogen production, and (3) depolarization using cathode-produced hydrogen to reduce energy requirement. The EH&S Assessment of the process did not identify any obvious concern for the bench-scale operation and no apparent barriers to implementing UK CAER’s carbon capture and solvent regeneration system at a larger scale.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

BETO 2021 Peer Review - Thermochemical Platform Analysis WBS: 2.1.0.302

The objective of the NREL Thermochemical Platform Analysis (WBS 2.1.0.302) project is to inform and guide R&D priorities for thermal and catalytic conversion processes by providing process design and techno-economic analysis (TEA). This is achieved through close collaboration with researchers and external experts, along with the use of both commercially available modeling tools and the development or use of collaboration-derived domain-specific tools and resources, such as refinery integration, kinetic and reactor models, phase equilibrium models, and pertinent bio-products market studies. This project is directly aligned with DOE-BETO goals, with the enabling of technology advancements and cost reduction for biomass derived biofuels being one of its primary objectives. TEA-guided research facilitated by this project has helped achieve significant modeled cost reductions for the ex situ catalytic fast pyrolysis (CFP) pathway and the indirect liquefaction (IDL) pathway for the conversion of syngas to high-octane gasoline (HOG). Cost reduction through refinery integration, development of valuable co-products, and other options are being identified for future research to help reduce the modeled MFSP to $2.50/GGE by 2030. Additional priorities anticipated in the future, such as the use of renewable electricity for liquid fuels and products, and emphasis on waste utilization are also being explored in conjunction with research on catalytic utilization of syngas and other gases (including CO2). Industry-relevant parameters are given deliberate attention as part of the work done under this project to help answer questions important for future commercialization and address associated risks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Molecular-Level Overhaul of y-Aminopropyl Aminosilicone/Triethylene Glycol Post-Combustion CO2-Capture Solvents

Capturing carbon dioxide (CO2) from post-combustion gas streams is an energy-intensive process that is required prior to either converting or sequestering CO2. There are a few commercial offerings of 1st and 2nd generation aqueous amine technologies, however the cost of capturing CO2 with these technologies remains high. To decrease costs of capture, researchers are designing efficient solvent systems with the goal of being drop-in replacements for 1st and 2nd generation infrastructure. One approach has seen the development of water-lean solvents that aim to increase efficiency by reducing the water content in solution. Water-lean solvents such as GE’s GAP/TEG are promising technologies, with potential to halve the parasitic load to a coal-fired power plant, only if the intrinsically high solution viscosities and hydrolysis of the siloxane moieties could be mitigated. We present here, an integrated multidisciplinary approach to overhaul the GAP/TEG solvent system at the molecular level to mitigate hydrolysis while also reducing viscosity. We present molecular-level insights into chemical speciation of CO2-containing ions, showing that co-solvents and diluents have a negligible effect on reducing viscosity and are not needed. This finding allowed for the design of singlecomponent siloxane-free diamine derivatives with site-specific incorporation of selective chemical moieties for direct placement and orientation of hydrogen bonding to reduce viscosity. Ultimately, we present new single-component diamine formulations less susceptible to hydrolysis that exhibit up to a 98% reduction in viscosity compared to the initial GAP/TEG formulation.

Cantu Cantu, David↗
DOE OSTI2025

CSP Systems Analysis 2022-2024 (Final Technical Report)

The CSP Systems Analysis project estimates the current market cost of CSP subsystems and technologies for use by the DOE Program and within NREL analysis tools. The project addresses upgrades to NREL's System Advisor Model (SAM) related to CSP, as well as development of new modules within SAM to expand the types of CSP systems that can be simulated and new tools for evaluating CSP subsystem performance or optimizing CSP system layouts, for example, SolarPILOT and SolTrace. The project evaluates the potential cost and performance of new CSP-relevant technologies and generally includes assessment of CSP technologies in support of the DOE subprogram. A primary goal of the CSP System Analysis project is to provide timely and accurate cost data to the DOE to assess the current state of CSP technologies as well as predict performance and cost for pre-commercial and emerging technologies that may impact the industry. Prior work in this task has produced technology cost reports and roadmaps for DOE and highlighted promising research paths that could reduce overall CSP cost, such as the supercritical CO2 power cycle and molten-salt trough systems. CSP Systems Analysis produces modeling tools to evaluate the performance of CSP systems, subsystems, or components, and journal articles, conference papers, technical reports and databases to disseminate information to the CSP industry. CSP technology costs are estimated and tracked using these tools as well as analysis of literature and industry sources. The CSP modeling tools and NREL analysis results are used by researchers; government, financial, and industry analysts; and other CSP stakeholders. The purpose of this final technical report is to summarize the key accomplishments of the past three years of this project. The project was divided into seven separate tasks, and in what follows, we describe the background, objectives, key accomplishments, and path forward for each task within the project, with one section of this report summarizing each task.

14 SOLAR ENERGY↗
DOE OSTI2023

Life Cycle Assessment of Innovative Carbon Dioxide Selective Membranes from Low Carbon Emission Sources: A Comparative Study

Carbon capture has been an important topic of the twenty-first century because of the elevating carbon dioxide (CO2) levels in the atmosphere. CO2 in the atmosphere is above 420 parts per million (ppm) as of 2022, 70 ppm higher than 50 years ago. Carbon capture research and development has mostly been centered around higher concentration flue gas streams. For example, flue gas streams from steel and cement industries have been largely ignored due to lower associated CO2 concentrations and higher capture and processing costs. Capture technologies such as solvent-based, adsorption-based, cryogenic distillation, and pressure-swing adsorption are under research, but many suffer from higher costs and life cycle impacts. Membrane-based capture processes are considered cost-effective and environmentally friendly alternatives. Over the past three decades, our research group at Idaho National Laboratory has led the development of several polyphosphazene polymer chemistries and has demonstrated their selectivity for CO2 over nitrogen (N2). Poly[bis((2-methoxyethoxy)ethoxy)phosphazene] (MEEP) has shown the highest selectivity. A comprehensive life cycle assessment (LCA) was performed to determine the life cycle feasibility of the MEEP polymer material compared to other CO2-selective membranes and separation processes. The MEEP-based membrane processes emit at least 42% less equivalent CO2 than Pebax-based membrane processes. Similarly, MEEP-based membrane processes produce 34–72% less CO2 than conventional separation processes. In all studied categories, MEEP-based membranes report lower emissions than Pebax-based membranes and conventional separation processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Robust Carbon Dioxide Plume Imaging Using Joint Tomographic Inversion of Seismic Onset Time and Distributed Pressure and Temperature Measurements (Final Report)

We develop and demonstrate rapid and cost-effective methodologies for spatiotemporal tracking of CO2 plumes during geologic sequestration using joint inversion of seismic data and distributed pressure and temperature measurements. Key elements of our methodology are: (a) a computationally efficient approach to pressure and temperature propagation, (b) analysis of time lapse seismic data using a novel ‘seismic onset time’ approach to detect fluid front propagation, and (c) data assimilation and uncertainty assessment via joint inversion of pressure, temperature and time lapse seismic data, and (d) validating the numerical tomographic inversion using a CO2 injection demonstration projects, specifically data collected from the from the Petra Nova Parish Holdings CCUS project in the West Ranch Field, Texas and the Chester-16 reef CO2 injection site in Northern Michigan which is part of the DOE Midwestern Carbon Sequestration Project. The research team is led by Texas A&M University and includes Battelle as a subcontractor with support from Shell, Anadarko, Chevron and JX Nippon. A carbon dioxide (CO2) water-alternating-gas (WAG) pilot was conducted to gain insights into tertiary oil recovery potential via CO2 flood in the West Ranch Field as part of the Petra Nova project, the world’s largest post-combustion CO2 capture and utilization initiative. With a fluvial formation geology and large contrasts in permeability, this is a challenging and novel application of CO2 enhanced oil recovery (EOR). We build a predictive dynamic model of the subsurface that incorporates the multiphase and compositional data acquired during the pilot operation. The calibrated model is used for the carbon dioxide plume imaging. The study began with an initialization of the pilot sector model extracted from a calibrated full-field model. The pilot model calibration follows a two-step hierarchical workflow. First, we performed a large-scale update of the permeability distribution by integrating available bottomhole pressure and multiphase production data. In the second step, local permeability field is fine-tuned using a streamline-based method to match CO2 breakthrough times at the producers. The predictive capability of the calibrated model was verified through two blind validation tests: (1) the model showed good agreement with saturation logs acquired at two observation wells; and (2) the model reproduced the CO2 recovery as a fraction of the injected CO2. The use of seismic onset times has shown great promise for integrating near-continuous seismic surveys for updating geologic models. In this study, we analyze the impact of seismic survey frequency on the onset time approach aiming to extend the application of onset time to infrequent seismic surveys. In addition, we quantitatively examine the nonlinearity of the onset time method and compare it to the commonly used amplitude inversion method. We carry out a sensitivity analysis of seismic survey frequency based on the complete seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. Our results show that an adequate onset time map can be obtained from the infrequent seismic surveys by interpolation between seismic surveys as long as there is no change in the dominant underlying physics between the successive surveys. The study also shows that nonlinearity of the onset time method can be -smaller than that of the amplitude inversion method by several orders of magnitude. Application to the Brugge benchmark case shows that the onset time method obtains comparable permeability update as the traditional seismic amplitude inversion method with faster computation and improved convergence characteristics. We extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process. The hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warm-back analysis of the temperature data. In addition to employing simulation models and inverse methods for CO2 plume imaging, we also initialized a data-driven technology for detecting inter-well connectivity based on production and pressure data. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO2 EOR projects utilizing the water-alternating-gas (WAG) process. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. Texas A&M University, the lead organization in the project, was primarily responsible for the development of tomographic approaches for CO2 plume mapping in conjunction with distributed pressure, temperature and seismic onset time data. Battelle, as a subcontractor, was primarily responsible for the development of analytical and empirical methods for analyzing transient injection rate and pressure data from point/line sources such as injection and monitoring wells. An additional area of emphasis for Battelle was the use of machine learning for such tasks as inferring reservoir connectivity information from injection-production data, and identifying variable importance for machine learning-based proxy models developed from full-physics simulations. The two organizations also collaborated on the application of the tomographic inversion methodology for a field data set.

02 PETROLEUM↗
DOE OSTI2024

Large-Scale CO2 Storage and Reservoir Management Strategies: Case Studies

This poster describes large-scale CO2 storage and reservoir management case studies for presentation at the AGU2024 conference. The analysis in this study provides a wealth of details on the designs and strategies that can lead to an optimal solution for pressure and CO2 plume management under critical constraints. These findings not only promise a reduction in the CO2 storage footprint but also hold the potential to create a more environmentally friendly ecosystem. Furthermore, this research offers valuable insights into the decision-making process when considering multi-project deployment in a shared basin for large-scale and gigatons of CO2 storage, paving the way for a more sustainable future. Poster presented (virtual) at the 2024 AGU Conference, December 9-13, 2024, Washington, D.C.

Liu, Guoxiang [NETL]↗
DOE OSTI2025

CFD Simulation of Aerobic Gas Fermentation to Enable Commercial Conversion of CO 2 into Aquaculture and Animal Feed: Cooperative Research and Development (Final Report)

NovoNutrients’ fermentation technology uses energy from hydrogen to transform industrial CO2 emissions into premium animal feed ingredients and other valuable products. A single NovoNutrients’ commercial manufacturing plant will capture and convert over 200,000 tons/yr of CO2 into over 100,000 tons/yr of high-protein feed. Key to the rapid and widespread deployment of the technology is maximization of its productivity and energy efficiency. Robust, physically based computational models of the technology will significantly increase productivity and efficiency, accelerating NovoNutrients’ technology to manufacturing scale. NREL has unique capabilities for creating and running such computational models. NREL's existing aerobic bioreaction computational fluid dynamics (CFD) models will be adapted to NovoNutrients’ gas fermentation (CO2, H2, O2) technology. The multiphysics CFD simulations require thousands of high-performance computing (HPC) node hours to simulate the complex geometries and contents of NovoNutrients’ industrial bioreactors. The experimentally validated CFD models were used to identify optimally efficient and productive bioreactor designs and operating conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Upgrading of C1 Building Blocks

This project is developing the centerpiece technology for a market-responsive, integrated biorefinery concept based on the conversion of renewable C1 intermediates (e.g., syngas, CO2, methanol) to a suite of fuels and co-products with improved carbon efficiency, reduced capital expense, and control of the product distribution to meet market demand. Advanced upgrading technologies of syngas are critically needed for the successful commercialization of fuel production at a scale relevant for biomass gasification. Research tasks within this project leverage complementary catalyst and process design for the conversion of CO2-rich syngas (15-20% CO2 in syngas) to achieve high carbon yields of gasoline and jet fuels as the major products. The conversion pathways generate high quality fuels (e.g., high octane gasoline with low aromatics, desirable jet-range hydrocarbons), and potential to achieve favorable cost targets by 2022. Research progress is compared against the Mobil Olefin to Gasoline and Distillate (MOGD) process, which also offers control over the gasoline and distillate products, as an industrial benchmark. The pathway for direct conversion of CO2-rich syngas to hydrocarbon fuels seeks to exceed the carbon efficiency of biomass-sourced MOGD (31.8%). Recent catalyst and process development achievements are highlighted by improvements in carbon-selectivity to fuels and carbon yields, along with evidence of incorporation of carbon from CO2 into the hydrocarbon products.

bioenergy↗
DOE OSTI2020

Subtask 1.3 – Integrated Carbon Capture and Storage for North Dakota Ethanol Production

The Energy & Environmental Research Center (EERC), in partnership with Red Trail Energy, LLC (RTE), a North Dakota ethanol producer; the North Dakota Industrial Commission (NDIC) Renewable Energy Program (REP); and the U.S. Department of Energy (DOE), conducted a feasibility and implementation study for a commercial carbon capture and storage (CCS) effort. This subtask provided technical support and developed recommended practices of how small-scale industrial CO2 emitters (<1,000,000 tonnes of CO2 emitted annually) may economically deploy CCS. The 64-million-gallon dry mill RTE ethanol facility, emitting an average 180,000 tonnes of CO2 annually, was the subject of the case study. The positive outcome of this research, which shows technical and economic potential for ethanol–CCS in North Dakota, has resulted in RTE acquiring an approved Permit to Drill on December 2, 2019, for a stratigraphic test well in early 2020, a necessary step toward a North Dakota CO2 Storage Facility Permit (SFP) for the RTE CCS effort. Outcomes include 1) validating the Broom Creek Formation as a regional target for CCS, 2) determining the full carbon life cycle of an industrial fuel production facility with CCS, 3) developing a field implementation plan (FIP) for small-scale CCS, and 4) determining the validity and pathway for using CCS to meet low-carbon fuel (LCF) standards. The technical team included the EERC, RTE, Trimeric Corporation, Schlumberger Carbon Services, and Computer Modelling Group (CMG). Findings from activities conducted November 2016 – May 2020 are summarized as follows. Several key steps have been accomplished toward validating the Broom Creek Formation as a CO2 injection and storage target at the RTE CCS site, including verification of the presence and structure of sandstone layers that may comprise the potential CO2 storage reservoir and the several thousand feet of overlying confining zone. Work included site characterization using existing data, interpretation of seismic data within the study area, and geologic modeling simulation of CO2 injection. Interpreted results estimate 3000 feet of confining zone between the Broom Creek Formation (storage target) and the lowermost underground source of drinking water (e.g., the Fox Hills Formation). The thickness of the Broom Creek injection target varies 230–420 ft within the survey area. Results were used to inform the location of a stratigraphic test well and associated characterization test program. No impediments were identified within the targeted CO2 storage complex that would prevent the project from moving forward. A stratigraphic test well is the next step to validate these results and to acquire remaining data necessary to develop a North Dakota CO2 SFP application. The EERC generated full carbon life cycle estimates for CCS integration with the RTE ethanol facility. Results indicated that an average 40% reduction in CO2 emissions is possible through CCS implementation. Approximately half of the carbon in the overall life cycle for a dry mill ethanol plant is generated through the fermentation process and emitted to the atmosphere; this is the CO2 stream targeted for CCS. The remaining carbon is attributed to corn feedstock farming (diesel, fertilizer), energy for fuel processing (natural gas, electricity), and transportation (diesel) of the fuel product. Life cycle carbon estimates are also affected by the anticipated energy consumption of a potential capture facility, which depend on the type of CO2 product generated. For example, a 30%–40% net CO2 emission reduction is estimated if a liquefied CO2 facility were incorporated compared to a 40%–50% net CO2 reduction if a supercritical “injection-grade” CO2 product is generated; i.e., more energy is required to further refine the CO2 stream, affecting the full carbon life cycle estimates. A CCS FIP was developed and initiated at the RTE CCS site, resulting in the development of several guidance documents: a CO2 Capture Process Design Package, a North Dakota CO2 geologic Storage Permits Template, and a Public Outreach Package for CCS in North Dakota. General FIP components include CO2 capture system, pipeline and well designs; monitoring, verification, and accounting (MVA) plans; geologic characterization and testing programs; and permitting and outreach plans. Vendor bids were also acquired for the CO2 liquefaction facility. Near-surface characterization (groundwater and soil gas sampling) and geologic characterization (seismic survey) were initiated to inform development of a UIC Class VI-compliant MVA plan compliant with a North Dakota CO2 SFP. Designs (well and geologic testing) were completed for a stratigraphic test well compliant with a North Dakota CO2 SFP. In addition, the outreach plan was executed, including community open houses, meetings with city/county/state officials, and development of public materials. Although other entities continued to mature incentive programs in 2019–2020, California and the Internal Revenue Service (IRS) currently provide the most advanced economic opportunities for CCS integrated with fuel production. The California Low-Carbon Fuel Standard (LCFS) adopted a CCS Protocol in January 2019, allowing submittal of a design-based pathway (DBP) application for an approved temporary (not certified) carbon intensity value for a fully engineered facility. An ethanol–CCS DBP application to the California LCFS Program (officially approved February 28, 2020) was developed to show that the RTE CCS effort meets LCFS requirements. The approved DBP provides confidence to advance the project and supports potential investments. Other entities continue to mature incentive programs. The IRS issued guidance in February 2020 that addresses the definition of beginning of construction and revenue procedure on partnerships for the Enhancement of Carbon Dioxide Sequestration Credit (a.k.a. Section 45Q) CCS tax credit program; the IRS anticipates issuing further guidance on issues such as secure geologic storage, utilization qualifications, and recapture of claimed credits. Maturing incentive programs coupled with workable permitting regulations provide confidence to advance CCS projects in North Dakota and support financial investment to proceed with designing, constructing, and implementing CCS projects at small-scale fuel production facilities. The largest hurdles for CCS implementation at small-scale industrial systems are often business/economic-related (i.e., not technical). Market uncertainty already exists for agriculture-based alternative fuels such as corn ethanol, for which production has increased by ~33%, and prices have correspondingly lowered since 2015. Passing the Section 45Q tax credit program improves economic feasibility for CCS but may require external investors for a small business to achieve maximum benefits. Public–private partnerships with NDIC and DOE have resulted in foundational technical and regulatory knowledge, growing stakeholder confidence, and a pathway to implementation that enables similar industrial CCS projects in the region to advance. This subtask was funded through the EERC–DOE Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by NDIC and RTE. The authors would also like to thank CMG, ESRI, IHS, Neuralog, and Schlumberger for allowing the use of their software packages in support of this work.

42 ENGINEERING↗
DOE OSTI2025

Data for "Sustainable Potassium Sorbate Production from Triacetic Acid Lactone in Food-Grade Solvents"

This study advances the production of potassium sorbate (KS) from triacetic acid lactone (TAL) utilizing food-grade solvents, ethanol (EtOH) and isopropyl alcohol (IPA). We have previously demonstrated the route to produce KS from TAL in tetrahydrofuran (THF) as the main solvent, but the use of THF is associated with environmental and health risks especially for food applications. The process employs a catalytic approach in food-grade solvents and includes three main steps: hydrogenation, etherification and hydrolysis, and ring-opening hydrolysis to produce KS from TAL. In the synthesis of KS from TAL, the use of IPA leads to higher yields and reduced reaction times compared to EtOH. As a result, the overall reaction time in IPA was reduced to 35.7 h, compared to 42.1 h in our previous study using THF and EtOH, while achieving a comparable KS yield of 84% from TAL. The synthesized KS exhibits a trans-2, trans-4 geometrical configuration, identical to that of commercially available KS. Through techno-economic analysis (TEA) and life cycle assessment (LCA), we estimated full-scale production of KS from sugarcane with the developed process in IPA could achieve a minimum product selling price (MPSP) of $8.27 per kg with a range of $7.06–10.16 per kg [5th–95th percentiles from 6000 Monte Carlo simulations] and a carbon intensity (CI) of 13.7 [9.6–18.6] kg CO2-eq per kg. This study highlights the synthesis of KS from TAL using food-grade solvents, demonstrating improved economic viability and environmental sustainability compared to our previous research (MPSP of $9.68 per kg [$8.47–11.45 per kg] and CI of 16.2 [12.0–21.2] kg CO2-eq per kg), as the total required reaction decreases while achieving the comparable overall yield of KS from TAL.

bioproducts↗
DOE OSTI2024

Novel microbial based enzymatic CO2 fixation mechanisms: Conformational control of enzymatic reactivity

The broad, long-term goal of the research is to provide insight into the mechanism of novel carboxylation and electron bifurcation reactions as paradigms to study how enzyme utilized protein conformational change to protect reactive intermediates and/or control the pathway in a catalytic cycle. catalyzed by enzymes involved in bacterial alkene, ketone, and epoxide metabolism. We are examining two paradigms in which conformational change is a key mechanistic feature of 1) protecting reaction intermediates that are sensitive to breakdown in the presence of water and 2) directing electron flow in reactions that split electron pairs along two pathways in reactions that fine tune metabolic efficiency in microbial metabolism. Both paradigms are of key importance in defining the molecular determinants of reactions relevant to CO 2 capture and fuel upcycling. In the first paradigm the compounds acetone and bicarbonate are activated by the addition of phosphate creating reaction intermediates (phosphoenolacetone and carboxyphosphate) that are very sensitive to breakdown in the presence of water. We are studying the mechanism of how the enzyme protects these reactive intermediates from water by encapsulating them upon their formation within a hydrophobic protein channel leading to an enzyme active site approximately 40 Å away. In the second paradigm, we are examining how the direction of electron transfer reactions can be modulated through protein conformational change that alters either the difference driving force or distance or both between electron transfer pathways in enzymes that catalyze electron bifurcation. We are using a combination of enzyme kinetics, mass spectrometry, spectroscopy, and structural analysis through X-ray diffraction and scattering methods to characterize the mechanism of conformational changes along the catalytic path. Within the context of the mission of the Department of Energy and the core activities of the Energy Biosciences, the results obtained in the study will reveal new insights into the fundamentals of novel carboxylation / CO 2 capture chemical transformations and electron transfer reactions that equate to biological fuel upcycling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Final Technical Report

The capture of CO2 and its simultaneously conversion to useful chemical fuels driven by solar energy represents one of the best solutions to resolve our growing energy and environmental concerns. The most critical challenge to this endeavor is the rational design of a photocatalytic architecture that can effectively couple a given photosensitizer (PS) with an appropriate catalyst, thereby enabling efficient photosensitization of a multi-electron reduction catalysis. This research program aims to address this challenge using an interdisciplinary approach that combines innovative material design and synthesis, fundamental mechanistic studies, and photocatalytic performance evaluation. The strategies include 1) constructing and investigating a novel class of 2D COF hybrid photocatalysts with an effective photoactive organic building block as PS and a precisely incorporated CO2 reduction molecular catalyst (MC); and 2) mechanistic origins of CO2 photoreduction using a set of complementary time-resolved and in situ spectroscopic techniques. The novelty of the proposed hybrid system lies in the unprecedented combination of the unique advantage of porous crystalline COF PS with the precise catalytic function of MC for photocatalytic CO2 reduction. In the periods of the support (09/01/2019-12/31/2022), we have made research progress in four projects: 1) Exploring 2D COFs with incorporated Mn complex for light driven CO2 reduction; 2) The dependence of excited state and charge transfer dynamics on monomer structure of 2D COFs; and 3) Control over Charge Separation by Imine Structural Isomerization in Covalent Organic Frameworks with Implications on CO2 Photoreduction; and 4) The impact of monomer structure on the photoluminescence properties of COFs. We found that both monomer structure and linker chemistry can effectively impact the excited state dynamics, charge transfer properties, and photoluminescence quantum yields, the important properties that dictate their applications in photocatalysis. In addition, we found that the direction of imine linker determines charge transfer direction and thus controls the types of catalytic reactions (e.g. water oxidation or CO2 reduction reactions). The result from these fundamental studies provides important information for correlating the structure of the COF photocatalysts with their photophysical properties and catalytic functions, paving the way for their novel application in photocatalysis. We expect that our findings will contribute to addressing current shortcomings of semiconductor- and molecular-based photocatalytic systems that suffer from inefficient light harvesting and charge separation and poor adsorption and activation of reactants. In turn, this research will contribute towards the development of novel photocatalytic systems for CO2 reduction to generate renewable chemical fuels and simultaneously address the problem of mitigating climate change due to CO2 accumulation. In addition, the experimental approaches employed in this research can be easily transferred to other energy technologies and are expected to broadly impact fields involving photocatalysis, optoelectronic devices, and solar energy conversion. The proposed research has also been integrated with educational activities and serve as a basis to raise awareness around the critical issues of global energy production and consumption, and to develop the next generation of solar energy scientists.

14 SOLAR ENERGY↗
DOE OSTI2024

Application of Modified Meshgraphnets for Subsurface Prediction during CO2 Sequestration

In the face of the increasingly dire consequences of anthropogenic climate change, capturing and storing carbon dioxide is paramount. However, several impediments exist to the safe and effective subsurface storage of CO2, such as cost of transport, identification of suitable sites for subsurface storage, and assessment of long-term risk from storage in subsurface aquifers. Accurate subsurface modeling is necessary to ensure that CO2 storage is both safe and effective. Still, such modeling has traditionally required either substantial time and computational power (numerical simulation) or a substantial amount of pre-existing data for training (machine learning models). Additionally, these models lack flexibility in dealing with both changes in discretization of the input data and generalizability beyond the data on which they are trained. In order to address these issues, this research applies graph neural networks (GNNs) to predict subsurface saturation and pressure during CO₂ injection in a model of the Illinois Basin-Decatur Project (IBDP). GNNs provide a flexible, intuitive method for representing and manipulating complex unstructured data, which is often found in many practical domain problems such as fluid flow and subsurface characterization. These unstructured grids are easily represented in GNNs by representing spatially-localized features such as permeability, porosity, saturation, and pressure as nodes in a graph and relationships between these properties as edges connecting these nodes. This research applies a specific GNN model called MeshGraphNets (MGN) to model the change in CO2 saturation and pressure over a 50-month time period (36 months of injection, 14 months post-injection). The MGN model leverages a message passing process that allows the network to learn both the spatial and temporal dynamics of this system simultaneously. Additionally, training on a limited dataset (64 realizations, 20 time points each) resulted in a high degree of accuracy in saturation prediction both within the same timeframe as the training (20 months, 0.039 average RMSE) and when projecting out to the end of injection (36 months, 0.053 average RMSE). Temporal predictions such as those generated by MGNs and other similar models are prone to accumulated error over time; in order to address this, a multi-step rollout (MSR) training process was applied to calculate training loss. This method mimics the forward prediction during inference by “rolling out” multiple time points in a single training step using the previous prediction as input to the MGN model. By calculating the loss several time steps forward from the current prediction, the model is forced to find a more stable state over time. Application of MSR to the MGN model resulted in an average 15% reduction in inference error over time during forward prediction. This study showcases the immense potential of GNNs as a game-changing methodology for predicting pressure and saturation evolution in CCS projects, ultimately paving the way for more sustainable and effective carbon storage solutions. Presentation prepared for the 2024 AiChE Annual Meeting, October 27 to November 1 2024, San Diego, CA.

Holcomb, Paul↗
DOE OSTI2025

Harnessing Ultra-Intense Long-Wave Infrared Lasers: New Frontiers in Fundamental and Applied Research

This review explores two main topics: the state-of-the-art and emerging capabilities of high-peak-power, ultrafast (picosecond and femtosecond) long-wave infrared (LWIR) laser technology based on CO2 gas laser amplifiers, and the current and advanced scientific applications of this laser class. The discussion is grounded in expertise gained at the Accelerator Test Facility (ATF) of Brookhaven National Laboratory (BNL), a leading center for ultrafast, high-power CO2 laser development and a National User Facility with a strong track record in high-intensity physics experiments. We begin by reviewing the status of 9–10 μm CO2 laser technology and its applications, before exploring potential breakthroughs, including the realization of 100 terawatt femtosecond pulses. These advancements will drive ongoing research in electron and ion acceleration in plasma, along with applications in secondary radiation sources and atmospheric energy transport. Throughout the review, we highlight how wavelength scaling of physical effects enhances the capabilities of ultra-intense lasers in the LWIR spectrum, expanding the frontiers of both fundamental and applied science.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Widespread underestimation of rain-induced soil carbon emissions from global drylands

Dryland carbon fluxes, particularly those driven by ecosystem respiration, are highly sensitive to water availability and rain pulses. However, the magnitude of rain-induced carbon emissions remains unclear globally. Here we quantify the impact of rain-pulse events on the carbon balance of global drylands and characterize their spatiotemporal controls. Using eddy-covariance observations of carbon, water and energy fluxes from 34 dryland sites worldwide, we produce an inventory of over 1,800 manually identified rain-induced CO2 pulse events. Based on this inventory, a machine learning algorithm is developed to automatically detect rain-induced CO2 pulse events. Our findings show that existing partitioning methods underestimate ecosystem respiration and photosynthesis by up to 30% during rain-pulse events, which annually contribute 16.9 ± 2.8% of ecosystem respiration and 9.6 ± 2.2% of net ecosystem productivity. We show that the carbon loss intensity correlates most strongly with annual productivity, aridity and soil pH. Finally, we identify a universal decay rate of rain-induced CO2 pulses and use it to bias-correct respiration estimates. Our research highlights the importance of rain-induced carbon emissions for the carbon balance of global drylands and suggests that ecosystem models may largely underrepresent the influence of rain pulses on the carbon cycle of drylands.

Nguyen, Ngoc B↗