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DOE OSTI2023

Processes and systems for reforming of methane and light hydrocarbons to liquid hydrocarbon fuels

Processes for converting methane and/or other hydrocarbons to synthesis gas (i.e., a gaseous mixture comprising H 2 and CO) are disclosed, in which at least a portion of the hydrocarbon(s) is reacted with CO 2 . At least a second portion of the methane may be reacted with H 2 O (steam), thereby improving overall thermodynamics of the process, in terms of reducing endothermicity (ΔH) and the required energy input, compared to “pure” dry reforming in which no H 2 O is present. Such dry reforming (reaction with CO 2 only) or CO 2 -steam reforming (reaction with both CO 2 and steam) processes are advantageously integrated with Fischer-Tropsch synthesis to yield liquid hydrocarbon fuels. Further integration may involve the use of a downstream finishing stage involving hydroisomerization to remove FT wax. Yet other integration options involve the use of combined CO 2 -steam reforming and FT synthesis stages (optionally with finishing) for producing liquid fuels from gas streams generated in a number of possible processes, including the hydropyrolysis of biomass.

Marker, Terry↗

DOE OSTI2021

Online Biogenic Carbon Analysis Enables Refineries to Reduce Carbon Footprint during Coprocessing Biomass- and Petroleum-Derived Liquids

To mitigate green-house gas (GHG) emissions, governments around the world are enacting legislation to reduce carbon intensity in transportation fuels. Coprocessing biomass and petroleum-derived liquids in existing refineries is a near-term, cost-effective approach for introducing renewable carbon in fuels and enabling refineries to meet regulatory mandates. However, coprocessing biomass-derived liquids in refineries results in variable degrees of biogenic carbon incorporation, necessitating accurate quantification to verify compliance with mandates. Existing refinery control and instrumentation systems lack the means to measure renewable carbon accurately, reliably, and quickly. Furthermore, accurate measurement of biogenic carbon is key to ensuring refineries meet regulatory mandates. In this Perspective, we present existing methods for measuring biogenic carbon, point out their challenges, and discuss the need for new online analytical capabilities to measure biogenic carbon in fuel intermediates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Influence of shrub willow buffers strategically integrated in an Illinois corn-soybean field on soil health and microbial community composition

We report soil serves many important ecological functions and is an integral part of our existence as a society. However, concerns for soil health are growing globally, in part due to the negative impacts of agricultural management on soil resources. The production of perennial bioenergy crops on marginal land in row-crop production systems is one solution that could improve land-use efficiency and address the sustainability of cropland management. Because the relationship between crop management and the environment is complex, more research is needed to evaluate the potential benefits perennial bioenergy crop production has on soil health, as well as other ecosystem services. In this study, shrub willow buffers were strategically integrated into a corn-soybean cropping system with the main objective of reducing nitrate-N leaching from grain crop production while producing biomass for bioenergy. Two buffer systems (defined by landscape positions) were included for comparison, one on marginal land with exposure to nitrate-N leaching from upslope grain (southern plots) and one on fertile soils with less nitrate-N leaching potential (northern plots). Evaluation of soil (chemistry, bulk density, microbial community) and shrub willow vegetation properties ( fine roots, leaf litter decomposition, and nutrient uptake dynamics), showed that landscape position plays an important role in (1) the dynamics of soil chemical properties, (2) shrub willow's influence and productivity, and (3) the provision of additional ecosystem services such as reductions in nitrous oxide emissions and nitrate-N leaching. In addition, the combination of crop type and landscape position (N-grain, N-willow, S-grain, and S-willow) influenced the species composition of the soil microbial community, resulting in unique and identifiable communities. These results highlight the potential application of shrub willow buffers for ecosystem service provision and support of ecosystem processes; however, understanding the relationship between the microbial community, crop type, and landscape is important for understanding the sustainability of the design.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Sustainable Biofuels for Low-Carbon Maritime Transportation

The marine shipping sector heavily depends on fossil fuels and is one of the largest petroleum fuel consumers [1,2]. The annual global marine fuel consumption was estimated to be around 400 million metric tons in 2019 (2.5 billion barrels). Moreover, ocean shipping is one of the most significant contributors to sulfur oxides, nitrogen oxides, and particulate matter emissions. Global shipping contributes 13% of human-caused sulfur emissions and 2.6% of anthropogenic carbon dioxide emissions. As a major source of pollutant emissions, the marine industry faces several challenges related to emission regulations. The International Maritime Organization (IMO) has established a framework for reducing the carbon intensity of shipping: 40% reduction relative to 2008 levels by 2030 and 70% reduction by 2050. As the aviation sector, the maritime shipping sector is difficult to decarbonize through electrification. Biofuels offer the best opportunities for decarbonizing marine shipping in the near and medium-term. Advanced biofuels such as pyrolysis bio-oil offer the low-cost potential for meeting carbon reduction goals. For instance, the pyrolysis bio-oil exhibited promising marginal CO2 abatement costs at less than $100/tonne CO2-equivalent at a heavy fuel oil price greater than $1.10/gal [1]. As a potential biofuel option for low-carbon maritime shipping, this presentation focuses on a comparative techno-economic analysis (TEA) of bio-oils produced via a fast pyrolysis-based conversion pathway. The pathway converts a 50/50 blend of forest residues and clean pine to bio-oil via three process options: fast pyrolysis without vapor upgrading, and fast pyrolysis with vapor phase upgrading over ZSM-5 zeolite catalyst and Pt/TiO2 catalyst. The process configuration and operation variation led to different capital and operating costs, as well as the resulting raw bio-oil’s yield and quality, e.g., the water content, total acid number, and carboxylic acid number. The study also determined the minimum upgrading of bio-oils required to enable blending with very low sulfur fuel oil (VLSFO), with the associated costs reflected in TEA. This study shows that bio-oil could be a cost-effective fuel option for decarbonizing maritime shipping. Further research is required with respect to biofuel blending properties, such as compatibility with existing fuel system infrastructure and suitable engine performance.

BIOMASS FUELS↗

DOE OSTI2022

Brown Carbon Fuel and Emission Source Attributions to Global Snow Darkening Effect

Snow and ice albedo reduction due to deposition of absorbing particles (i.e., snow darkening effect (SDE)) warms the Earth system and is largely attributed to black carbon (BC) and dust. Absorbing organic aerosol (BrC) also contributes to SDE but has received less attention due to uncertainty and challenges in model representation. This work incorporates the SDE of absorbing organic aerosol (BrC) from biomass burning and biofuel sources into the Snow Ice and Aerosol Radiative (SNICAR) model within a variant of the Community Earth System Model (CESM). Additionally, 12 different emission regions of BrC and BC from biomass burning and biofuel sources are tagged to quantify the relative contribution to global and regional SDE. BrC global SDE (0.021–0.056 Wm -2 ) is larger than other model estimates, corresponding to 37%–98% of the SDE from BC. When compared to observations, BrC simulations have a range in median bias (-2.5% to +21%), with better agreement in the simulations that include BrC photochemical bleaching. The largest relative contributions to global BrC SDE are traced to Northern Asia (23%–31%), Southeast Asia (16%–21%), and South Africa (13%–17%). Transport from Southeast Asia contributes nearly half of the regional BrC SDE in Antarctica (0.084–0.3 Wm -2 ), which is the largest regional input to global BrC SDE. Lower latitude BrC SDE is correlated with snowmelt, in-snow BrC concentrations, and snow cover fraction, while polar BrC SDE is correlated with surface insolation and snowmelt. This indicates the importance of in-snow processes and snow feedbacks on modeled BrC SDE.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO 2 Flux and its Inter-Annual Variability

Drylands occupy ~40% of the land surface and are thought to dominate global carbon (C) cycle inter-annual variability (IAV). Therefore, it is imperative that global terrestrial biosphere models (TBMs), which form the land component of IPCC earth system models, are able to accurately simulate dryland vegetation and biogeochemical processes. However, compared to more mesic ecosystems, TBMs have not been widely tested or optimized using in situ dryland CO 2 fluxes. Here, for this work, we address this gap using a Bayesian data assimilation system and 89 site-years of daily net ecosystem exchange (NEE) data from 12 southwest US Ameriflux sites to optimize the C cycle parameters of the ORCHIDEE TBM. The sites span high elevation forest ecosystems, which are a mean sink of C, and low elevation shrub and grass ecosystems that are either a mean C sink or “pivot” between an annual C sink and source. We find that using the default (prior) model parameters drastically underestimates both the mean annual NEE at the forested mean C sink sites and the NEE IAV across all sites. Our analysis demonstrated that optimizing phenology parameters are particularly useful in improving the model's ability to capture both the magnitude and sign of the NEE IAV. At the forest sites, optimizing C allocation, respiration, and biomass and soil C turnover parameters reduces the underestimate in simulated mean annual NEE. Our study demonstrates that all TBMs need to be calibrated for dryland ecosystems before they are used to determine dryland contributions to global C cycle variability and long-term carbon-climate feedbacks.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

A comparative study of machine learning models for predicting the state of reactive mixing

Mixing phenomena are important mechanisms controlling flow, species transport, and reaction processes in fluids and porous media. Accurate predictions of reactive mixing are critical for many Earth and environmental science problems such as contaminant fate and remediation, macroalgae growth, and plankton biomass evolution. Here, to investigate the evolution of mixing dynamics under different scenarios (e.g., anisotropy, fluctuating velocity fields), a finite-element-based numerical model was built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios. A total of 2,315 simulations were performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast (i.e., ratio of longitudinal-to-transverse dispersion), and molecular diffusion. The outputs comprised concentration profiles of reactants and products. The inputs to and outputs from these simulations were concatenated into feature and label matrices, respectively, to train 20 different machine learning (ML) models intended to emulate system behavior. These 20 ML emulators, based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptrons (MLPs), were trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing species production, decay (i.e., average concentration, square of average concentration), and degree of mixing (i.e., variances of species concentration). Unsurprisingly, linear classifiers and regressors failed to reproduce the QoIs; however, ensemble methods (classifiers and regressors) and the MLP model accurately classified the state of reactive mixing and the QoIs. Among ensemble methods, random forest and decision-tree-based AdaBoost faithfully predicted the QoIs. At run time, trained ML emulators produced results times faster than the finite-element simulations. Due to their low computational expense and high accuracy, ensemble and MLP models are excellent emulators for these numerical simulations and great utilities in uncertainty quantification exercises, which can require 1,000s of forward model runs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R 2 > 0.6, RMSE <390 g C m -2 ) relative to net ecosystem exchange of CO 2 (NEE) (R 2 > 0.4, RMSE <180 g C m -2 ). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R 2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

One thousand soils for molecular understanding of belowground carbon cycling

While significant progress has been made in understanding global carbon (C) cycling, the mechanisms regulating belowground C fluxes and storage are still uncertain. New molecular technologies have the power to elucidate these processes, yet we have no widespread standardized implementation of molecular techniques. To address this gap, we introduce the Molecular Observation Network (MONet), a decadal vision from the Environmental Molecular Sciences Laboratory (EMSL), to develop a national network for understanding the molecular composition, physical structure, and hydraulic and biological properties of soil and water. These data are essential for advancing the next generation of multiscale Earth systems models. In this paper, we discuss the 1000 Soils Pilot for MONet, including a description of standardized sampling materials and protocols and a use case to highlight the utility of molecular-level and microstructural measurements for assessing the impacts of wildfire on soil. While the 1000 Soils Pilot generated a plethora of data, we focus on assessments of soil organic matter (SOM) chemistry via Fourier-transform ion cyclotron resonance-mass spectrometry and microstructural properties via X-ray computed tomography to highlight the effects of recent fire history in forested ecosystems on belowground C cycling. We observed decreases in soil respiration, microbial biomass, and potential enzyme activity in soils with high frequency burns. Additionally, the nominal oxidation state of carbon in SOM increased with burn frequency in surface soils. This results in a quantifiable shift in the molecular signature of SOM and shows that wildfire may result in oxidation of SOM and structural changes to soil pore networks that persist into deeper soils.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Modeling Yield, Biogenic Emissions, and Carbon Sequestration in Southeastern Cropping Systems With Winter Carinata

Sustainable aviation fuel (SAF) production from lipids is a technologically mature approach for replacing conventional fossil fuel use in the aviation sector, and there is increasing demand for such feedstocks. The oilseed Brassica carinata (known as Ethiopian mustard or simply carinata) is a promising SAF feedstock that can be grown as a supplemental cash crop over the winter fallow season of various annual crop rotations in the Southeast US, avoiding land use changes and potentially achieving some of the soil carbon sequestration and ecosystem service benefits of winter cover crops. However, carinata may require more intensive management than traditional cover crops, potentially leading to additional soil greenhouse gas (GHG) emissions through increased carbon losses from soil tillage and nitrous oxide (N 2 O) emissions from nitrogen fertilizer application. In this work, the 2017 version of the process-based DayCent ecosystem model was used to establish initial expectations for the total regional SAF production potential and associated soil GHG emissions when carinata is integrated as a winter crop into the existing crop rotations across its current suitability range in southern Alabama, southern Georgia, and northern Florida. Using data from academic and industry carinata field trials in the region, DayCent was calibrated to reproduce carinata yield, nitrogen response, harvest index, and biomass carbon-to-nitrogen ratio. The resulting model was then used to simulate the integration of carinata every third winter across all 2.1 Mha of actively cultivated cropland in the study area. The model predicted regional average yields of 2.9–3.0 Mg carinata seed per hectare depending on crop management assumptions. That results in the production of more than two million Mg of carinata seed annually across the study area, enough to supply approximately one billion liters of SAF. Conventional management of carinata led to only modest increases in soil carbon storage that were largely offset by additional N2O emissions. Climate-smart management via adopting no-till carinata establishment or using poultry litter as a nitrogen source resulted in a substantial net soil GHG sink (0.23–0.31 Mg CO 2 e ha -1 y -1 , or 0.24–0.32 Mg CO2e per Mg of seed produced) at the farms where carinata is cultivated.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Molecular simulation and artificial intelligence for the circular economy of bioenergy and bioproducts

The concept of the circular bioeconomy is a carbon neutral, sustainable system with zero waste. One vision for such an economy is based upon lignocellulosic biomass. This lignocellulosic circular bioeconomy requires CO 2 absorption from biomass growth and the efficient deconstruction of recalcitrant biomass into solubilized and fractionated biopolymers, which are then used as precursors for the sustainable production of high-quality liquid fuels, chemical bioproducts, and bio-based materials. Here, in this study, we summarize the roles that molecular dynamics (MD) simulations and machine learning (ML) are playing in overcoming several fundamental challenges hindering the adoption of a circular bioeconomy. Specifically, we discuss the role of MD and ML/AI in overcoming lignocellulose recalcitrance by designing biomass pretreatment methods to efficiently produce solubilized cellulose/lignin/hemicellulose and of that in improving energy-intensive manufacturing of biomass-based materials and their structural and mechanical properties. Quantum mechanical methods and MD simulations, in addition to offering a mechanistic understanding of biomass deconstruction and biomaterials design, can provide meaningful structural, energetics, and physiochemical properties as inputs to train AI/ML models. The ML models can guide the experimental prioritization of materials/solvents and process parameters that significantly accelerate the development of biofuel and biomaterial components of the circular bioeconomy.

Smith, Jeremy C. [Oak Ridge National Laboratory (O↗

DOE OSTI2023

Metaoptics Enabled Multifunctional Imaging

Despite the fact that most microorganisms occupy two- and three-dimensional space in heterogeneous arrangements and in proximity to other microorganisms of different species, much of our current knowledge about their metabolic processes is derived from homogenizing, then analyzing, the chemical content of cultures containing a single microbial species. This project addressed this fundamental dichotomy by developing a set of optical imaging principles combining multiple new technologies. The imaging strategies developed in this project combine metaoptics structures with active in situ nanoscale control of the chemical environment and applied them to a microbial system, Myxococcus xanthus, with particular relevance to the DoE mission. The combination of metaoptical architectures and nanoscale control over the molecular environment enables: (a) precise control over the electromagnetic (EM) field at length scales smaller than the wavelength of light; (b) control of the interaction of the EM field with critical molecular systems in DOE relevant microbes; (c) control over the chemical environment – especially the presence and quantity of reactive oxygen species (ROS) that can affect redox homeostasis; and (d) the ability to ask new kinds of questions not accessible to ‘omics’ approaches or standard methods of biological imaging. These capabilities are applicable to detailed studies of metabolic pathways in microbes and to lignocellulosic biomass deconstruction. To accomplish these objectives, we pursued two over-arching technical goals: (1) the development of new metaoptics-enabled approaches to imaging and spectroscopic characterization; and (2) the development of tools to control the chemical environment of a microbial sample with nanometer-scale precision. Goal 1 was addressed through the design, fabrication, and characterization of new metasurfaces capable of super-resolution imaging through extreme confinement of the optical field. Goal 2 was addressed by controlling the redox potential on the nanoscale in microbial communities and characterizing their effect on intrinsic bacterial fluorophores which act as molecular sentinels and through characterization of soluble factors secreted by Myxococcus xanthus by confocal Raman imaging. The optical imaging/sensing approaches developed here make it possible to use these powerful new imaging and sensing modalities in metabolic studies by making it possible to visualize and track the spatial and temporal expression patterns of natural or engineered pathways in microorganisms.

09 BIOMASS FUELS↗

DOE OSTI2024

Computational Analysis of Different Sparging Systems and their Influence in the Fluid-Dynamic Behavior of Bubble Column Reactors

Bubble column bioreactors are being actively considered for gas fermentation applications, specifically for CO2 utilization, and sugars to fuels conversion. Their main advantages include good mass transfer without any moving parts and low-cost of operation and maintenance. However, the design and scale-up of such reactors is challenging specifically for carbon capture applications where a mixture of gases (e.g. CO2/CO/H2) with variable solubilities is used. The overall performance of scaled-up bioreactors (e.g., mass transfer rate) is largely affected by gas holdup, bubble size distribution (BSD), and multiphase hydrodynamics. We investigate the effect of gas sparger designs on the performance of these large-scale bioreactors using computational fluid dynamics simulations in this work, so as to improve CO2 conversion at scale. The gas distribution systems in bubble column reactors not only determines operational regime, but also affects the evolution of the BSD, which in turn influences interfacial mass transfer and ultimately the efficiency of the gas-liquid exchange process. In addition to the BSD, uniformity in gas sparging affects gas holdup and bubble residence time which constitute important metrics of performance in gas-liquid systems. In this work, we use computational models to simulate high fidelity representations of different sparger designs and their effect on the operation of a bubble column reactor. Four different types of spargers have been selected for the computational study (Fig. 1): ladder, multi-ring, single-ring and toroidal. Their effect on superficial velocity, gas holdup mixing efficiency, and BSD will be evaluated in this work. The model uses a multiphase Eulerian framework similar to [1] and include a composition of mixtures of H2/CO/CO2 gases, common in fermentation applications.

BIOMASS FUELS,MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Microalgae Hydrothermal Liquefaction and Biocrude Upgrading: 2022 State of Technology

A preliminary techno-economic analysis (TEA) was developed for the fiscal year (FY) 2022 state of technology (SOT) assessment to evaluate the benefits and risks of using demonstrated, high-productivity algae strains for fuels generation, including sustainable aviation fuel (SAF). In 2022, the marine algal strain, Picochlorum celeri, which demonstrated the highest outdoor biomass productivities reported to date in the DOE-funded open-pond raceway testbed at the Arizona Center for Algae Technology and Innovation (AzCATI), was tested for continuous hydrothermal liquefaction (HTL) processing at PNNL. HTL testing results demonstrated a biocrude yield of 0.33 g/g algae on an ash-free dry weight (AFDW) basis from P. celeri. The hydrotreatment testing of the HTL biocrude from P. celeri was also conducted to investigate the production of jet fuel from marine algal biomass. To the best of our knowledge, this is the first report of jet fuel production from autotrophically grown marine algal biomass. The current hydrotreating testing demonstrated approximately 22.7 wt% of the hydrotreated oil within the typical boiling-point range of jet fuel (150–250 °C). Initial testing of the jet fuel cut (JFC) showed that the physical properties under investigation were within typical ranges for petroleum-based jet fuels. The experimental work of this study closes the gap between outdoor algae cultivation and algae conversion to critical transportation fuels using the same algae strain for both cultivation and conversion testing. The continuous HTL and the upgrading testing described herein demonstrate the potential of producing sustainable aviation fuel (SAF) from algae cultivated in open-pond systems using the primary inputs of sunlight and carbon dioxide.

09 BIOMASS FUELS↗

DOE OSTI2025

The levelized cost of exergy: a technoeconomic framework for energy system comparison

While the levelized costs of electricity and heat have been quantified before, these two metrics cannot be directly compared, due to the different exergy content of heat and work. To address this, we develop a levelized cost of exergy (LCOEx) framework that enables direct comparisons between energy sources and processes. We find that moderate- and high-grade heat have an LCOEx that is comparable to electricity (5–10 ¢ per kWh ex ), while low-grade heat sources have much higher LCOEx values (>50 ¢ per kWh ex ). The LCOEx of a system's output is affected by (i) the LCOEx of the system input, (ii) the CAPEX of the system, and (iii) the exergetic efficiency of the system. We use our framework to identify which processes are already achieved with relatively high cost effectiveness (production of fuels, hydrogen, and ammonia) and which have room for improvement (dehumidification, food production).

08 HYDROGEN↗

DOE OSTI2025

Updates to Anaerobic Digestion Pathways for Animal Manure in R&D GREET 2025

Livestock and poultry manure management in the U.S. is a greenhouse gas (GHG) intensive process, emitting 81.7 MMT CO 2 e in 2022 (1.5% of net U.S. GHG emissions). The primary GHG is methane (CH 4 ), with 2,312 kt released in 2022 (9% of U.S. CH 4 emissions). Manure management methods are commonly categorized by whether they are anaerobic (“wet”) or aerobic (“dry”) techniques. Although dry methods manage the largest share of manure, the majority of GHG emissions are generated during storage of manure in anaerobic conditions – typically in water-filled tanks, pits, or lagoons. There has been a 65% increase in emissions from 1990, primarily due to an increasing cattle population. Also, this rise in population has been coupled with a rise in animal confinement and density, which typically adopt wet manure management methods. Within wet methods, anaerobic bacteria proliferate and decompose volatile solids (VS) within the manure in a process called anaerobic digestion to produce roughly equal mixtures of CH 4 and carbon dioxide (CO 2 ). These GHGs are fugitive, in that they are assumed to be released to the atmosphere and contribute to GHGs within U.S. GHG inventories. If the methane is captured and purified (i.e., “upgraded”) this simultaneously mitigates GHGs that would have otherwise been released and produces a valuable energy product known colloquially as Renewable Natural Gas (RNG). Such processes are acknowledged by U.S. policy through programs such as the U.S. Renewable Fuels Standard (RFS), the federal Clean Fuel Production Credit (45Z), and state clean fuel standards (CFS). In the 45Z and CFS schemes, the GHG emissions of the business-as-usual (BAU) manure management system is taken as a baseline, and credits are received based on GHG reductions relative to this baseline. Thus, estimating the GHG emissions of the BAU scenario (also known as the “counterfactual”) is necessary.

09 BIOMASS FUELS↗

DOE OSTI2024

Genome reduction improves octanoic acid production in scale down bioreactors

Microorganisms in large-scale bioreactors are exposed to heterogeneous environmental conditions due to physical mixing constraints. Nutritional gradients can lead to transient expression of energetically wasteful stress responses and as a result, can reduce the titres, rates and yields of a bioprocess at larger scales. To what extent these process parameters are impacted is often unknown and therefore bioprocess scale-up comes with major risk. Designing platform strains to account for these intermittent stresses before introducing synthesis pathways is one strategy for de-risking bioprocess development. For example, Escherichia coli strain RM214 is a derivative of wild-type MG1655 that has had several genes and whole operons removed from its genome based on their metabolic cost. In this study, we engineered E. coli strain RM214 (referred to as WG02) to produce octanoic acid from glycerol in batch-flask and fed-batch bioreactor cultivations and compared it to an octanoic acid-producing E. coli MG1655 (WG01). In batch flask cultivations, the two strains performed similarly. However, in carbon limited fed-batch bioreactor cultivations, WG02 provided a greater than 22% boost to biomass compared to WG01 while maintaining similar titres of octanoic acid. Reducing the biomass accumulation of WG02 with nitrogen limited fed-batch cultivation resulted in a 16% improvement in octanoic acid titre over WG01. Finally, in a scale-down system consisting of a stirred tank reactor (representing a well-mixed zone) and plug flow reactor (representing an intermittent carbon starvation zone), WG02 again improved octanoic acid titre by almost 18% while maintaining similar biomass concentrations as WG01.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

CRISPR/Cas9 editing of p-COUMAROYL-CoA:MONOLIGNOL TRANSFERASE 1 in maize alters phenolic metabolism, lignin structure, and lignin-first biomass processing

Valorization of lignocellulosic biomass for sustainable production of high-value chemicals is challenged by the complexity of lignin, a phenolic biopolymer. Beyond the classical lignin monomers derived from p-coumaryl, coniferyl, and sinapyl alcohol, grass lignins incorporate substantial amounts of monolignol p-coumarates that are produced by p-COUMAROYL-CoA:MONOLIGNOL TRANSFERASE (PMT). Here, the CRISPR/Cas9-mediated mutation of ZmPMT1 in maize enabled the design of biomass depleted in p-coumaroylated lignin and enriched in guaiacyl lignin. Lignin-first biorefining of stem biomass from zmpmt1 mutants by reductive catalytic fractionation (RCF) generated a lignin oil depleted in carboxylates and enriched in guaiacyl-derived alcohols, which are desirable substrates for bio-based polyurethane synthesis. Furthermore, the reported lignin engineering in maize is a promising strategy for designing a dual-purpose crop, providing both food and feed, along with a renewable feedstock for the production of plant-based chemicals.

59 BASIC BIOLOGICAL SCIENCES↗