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DOE OSTI2023

Understanding local plant extinctions before it is too late: bridging evolutionary genomics with global ecology

Understanding evolutionary genomic and population processes within a species range is key to anticipating the extinction of plant species before it is too late. However, most models of biodiversity risk under global change do not account for the genetic variation and local adaptation of different populations. Population diversity is critical to understanding extinction because different populations may be more or less susceptible to global change and, if lost, would reduce the total diversity within a species. Here, two new modeling frameworks advance our understanding of extinction from a population and evolutionary angle: Rapid climate change-driven disruptions in population adaptation are predicted from associations between genomes and local climates. Furthermore, losses of population diversity from global land-use transformations are estimated by scaling relationships of species' genomic diversity with habitat area. Overall, these global eco-evolutionary methods advance the predictability – and possibly the preventability – of the ongoing extinction of plant species.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

BioTransformer 3.0 – A Web Server for Accurately Predicting Metabolic Transformation Products

BioTransformer 3.0 is a freely available web server that supports accurate, rapid and comprehensive in silico metabolism prediction. It combines machine learning approaches with a rule-based system to predict small-molecule metabolism in human tissues, the human gut as well as the external environment (soil and water microbiota). Simply stated, BioTransformer takes a molecular structure as input (SMILES or SDF) and outputs an interactively viewable/sortable table of the predicted metabolites or transformation products (SMILES, PNG images) along with the enzymes that are predicted to be responsible for those reactions and richly annotated downloadable files (CSV and JSON). The entire process typically takes a few seconds. Previous versions of BioTransformer focused exclusively on predicting the metabolism of xenobiotics (such as plant natural products, drugs, cosmetics and other synthetic compounds) using a limited number of pre-defined steps and somewhat limited rule-based methods. BioTransformer 3.0, uses much more sophisticated methods and incorporates new databases, new constraints and new prediction modules to not only more accurately predict the metabolic transformation products of exogenous xenobiotics but also the transformation products of endogenous metabolites, such as amino acids, peptides, carbohydrates, organic acids, and lipids. BioTransformer 3.0 can also support customized sequential combinations of these transformations along with multiple iterations to simulate multi-step human and/or environmental biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40-50% more accurate, much less prone to combinatorial “explosions” and far more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Compressed Gas Energy Storage Integrated with Combined Heat and Power Plant

A new energy storage technology, Comrpessed Gas Energy Storage (CGES), seeks to achieve low-cost, long-duration eenrgy storage by combining mature commercially-available technology (compressors, high-pressure gas storage, expander generators) and utilizing existing infrastructure for transportation and distribution of industrial gases. CGES utilizes existing compressors to transform electricity into potential energy (pressure), existing pipelines or underground storage to store said potential energy, and adds new expander generators at pressure reduction points in the networkm to recover part of the energy used in compression and transform it into electricity. By adapting itself to widely available existing infrastructure, CGES recovers energy that is otherwise wasted in the gas value chain, reduces the cost of new facilities, and avoids many of the development huddles typically associated with other long-duration energy storage technologies (e.g., Compressed Air Energy Storage, CAES). This paper discusses the performance and economics associated with CGES as a standalone project and in combination with a thermal power plant, based on a concept for integration of the technology over an existing lateral pipeline transporting natural gas to an 85 MW combined heat and power plant that feeds power to a grid with increasing share of renewable power generation.

03 NATURAL GAS↗

DOE OSTI2023

CRISPR/Cas9-based gene activation and base editing in Populus

The genus Populus has long been used for environmental, agroforestry and industrial applications worldwide. Today Populus is also recognized as a desirable crop for biofuel production and a model tree for physiological and ecological research. As such, various modern biotechnologies, including CRISPR/Cas9-based techniques, have been actively applied to Populus for genetic and genomic improvements for traits such as increased growth rate and tailored lignin composition. However, CRISPR/Cas9 has been primarily used as the active Cas9 form to create knockouts in the hybrid poplar clone “717-1B4” (P. tremula x P. alba clone INRA 717-1B4). Alternative CRISPR/Cas9-based technologies, e.g. those involving modified Cas9 for gene activation and base editing, have not been evaluated in most Populus species for their efficacy. Here we employed a deactivated Cas9 (dCas9)-based CRISPR activation (CRISPRa) technique to fine-tune the expression of two target genes, TPX2 and LecRLK-G which play important roles in plant growth and defense response, in hybrid poplar clone “717-1B4” and poplar clone “WV94” (P. deltoides “WV94”), respectively. We observed that CRISPRa resulted in 1.2-fold to 7.0-fold increase in target gene expression through transient expression in protoplasts and Agrobacterium-mediated stable transformation, demonstrating the effectiveness of dCas9-based CRISPRa system in Populus. In addition, we applied Cas9 nickase (nCas9)-based cytosine base editor (CBE) to precisely introduce premature stop codons via C-to-T conversion, with an efficiency of 13%–14%, in the target gene PLATZ which encodes a transcription factor involved in plant fungal pathogen response in hybrid poplar clone “717-1B4”. Overall, we showcase the successful application of CRISPR/Cas-based technologies in gene expression regulation and precise gene engineering in two Populus species, facilitating the adoption of emerging genome editing tools in woody species.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Cyber Secure Sensor Network for Fossil Fuel Power Generation Assets Monitoring

The energy sector is undergoing digital transformation which is to say more and more power generation assets have become automated and connected to the internet. The connected sensors can tap into plants to monitor the health of assets and manage fleets remotely. These are just some of the benefits digitalization is bringing to the power industry. Within a power plant, control systems are no longer concerned with one system or one piece of equipment, but rather whole fleet of assets inter-connected with smart sensors which have the function of continuously monitoring and transmitting real-time operational data to operators. These connected systems will form a part of the industrial internet of things (IIoT). The big data scenarios provide benefits of performing system prognostics and optimization which is a key selling point of power plant digitalization.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2025

HAMscope: a snapshot Hyperspectral Autofluorescence Miniscope for real-time molecular imaging

We introduce HAMscope, a compact, snapshot hyperspectral autofluorescence miniscope that enables real-time, label-free molecular imaging in a wide range of biological systems. By integrating a thin polymer diffuser into a widefield miniscope, HAMscope spectrally encodes each frame and employs a probabilistic deep learning framework to reconstruct 30-channel hyperspectral stacks (452-703 nm) or directly infer molecular composition maps from single images. A scalable multi-pass U-Net architecture with transformer-based attention and per pixel uncertainty estimation enables high spatio-spectral fidelity (mean absolute error ∼0.0048) at video rates. While initially demonstrated in plant systems, including lignin, chlorophyll, and suberin imaging in intact poplar and cork tissues, the platform is readily adaptable to other applications such as neural activity mapping, metabolic profiling, and histopathology. We show that the system generalizes to out-of-distribution tissue types and supports direct molecular mapping without the need for spectral unmixing. HAMscope establishes a general framework for compact, uncertainty-aware spectral imaging that combines minimal optics with advanced deep learning, offering broad utility for real-time biochemical imaging across neuroscience, environmental monitoring, and biomedicine.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Survey of Advanced Generation IV Reactor Parameters for Integrated Energy System Modeling Capabilities

This paper is to categorize and characterize advanced Generation IV nuclear reactors that are not water based according to the key parameters for the Integrated Energy Systems (IES) project[1]. A key goal of the IES project is to assess the economic viability of including an advanced nuclear reactor into an IES. The landscape of advanced reactors is heterogeneous: several unique designs with different heat profiles are currently being studied and designed. This report provides a high-level perspective of advanced reactor concepts, including primary loop nuclear output temperatures and operation schemes that could affect secondary side power production or storage. For example, this report addresses the type of thermodynamic system (Brayton, supercritical CO 2 [S-CO 2 ], etc.) and the various pressures and temperatures on the secondary side that are expected to be provided by the various primary side reactor types. Based on these characteristics, it may be possible to determine which type of nuclear power plant could be the most economical for analysis in the IES framework for power production coupled with energy storage and waste heat utilization. The results of this effort will serve as input for model development of various secondary side components, concentrating on off-the-shelf capabilities for current and near future secondary side power generation and heat rejection as simulated in TRANSFORM. Generic models can be created which are agnostic of the nuclear energy supply without violating intellectual property rights of the developer. These models can be used to make informed decisions on the economics of integrating various energy system components with nuclear plant designs. Once complete, conclusions may be drawn regarding (1) the economics, efficiency, and reliability of certain nuclear reactors, (2) certain industry processes and energy storage systems, and (3) prioritization of certain reactor types with the studied industry types.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

p -Coumaroylated Lignins Are Natively Produced in Three Rosales Families

Carbon-rich plant cell walls contain biopolymers that, with some processing, could replace fossil fuels as a major component of the current petrochemical production. To realize this, biorefineries need to be paired with biomass that during the deconstruction and fractionation processes transforms into the desired products. One component of interest is p-coumarate that, in some species, can account for up to 1% of the biomass’ dry weight. When p-coumarate is present in eudicot cell walls, it is mostly part of the suberin (bark and root), acylates the γ-hydroxy group of the lignin, in part of the tannins, or is a metabolite. The current understanding of eudicot plant cell wall composition is that the lignin is sometimes acylated with acetate and rarely with hydroxycinnamates (p-coumarate or ferulate). This study identified a clear division in the Rosales in which three families produce p-coumaroylated lignins whereas the other six families showed no evidence of the trait.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Plant litter traits control microbial decomposition and drive soil carbon stabilization

Efforts to manage soils for carbon (C) sequestration remain limited by our understanding of how differences in plant traits and microbial traits mechanistically drive soil organic C (SOC) storage. Addressing this uncertainty is particularly critical in bioenergy agriculture, due to its potential to enhance soil C and provide a C neutral fuel. As such, we examined differences between two contrasting feedstocks, Zea mays (corn) and Miscanthus x giganteus (miscanthus), in the ability of their litter to form new chemically resistant particulate SOC vs. physically protected mineral associated SOC and used this data to improve the parameterization of a microbial SOC model. We tested a hypothesized conceptual model whereby easy to decompose corn litters drive greater microbial carbon use efficiency (CUE) and the formation of more mineral associated SOC over particulate SOC than more complex miscanthus litters. To do this, we performed a soil microcosm experiment where we added 13 C enriched aboveground and belowground litters to soils and traced the fate of the 13 C into microbial respiration and SOC pools. We found that corn litters promoted higher microbial CUE (0.37) than miscanthus litters (0.24). In turn, corn litter formed approximately 50% more mineral associated SOC than miscanthus litters. Similarly, structurally complex root litters promoted a lower CUE and formed less mineral associated SOC than leaf and shoot litters for both crops. When we used our data to parameterize the SOC model, we found that modelling microbial trait differences uniquely allowed the model to capture the fate of litter C in SOC. Collectively, we found a robust link between litter quality, microbial efficiency, and the formation of SOC. This link bridges the empirical uncertainty in how different crops can form new soil C and provides an empirical basis for modelling SOC transformations.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Functional analysis of Salix purpurea genes support roles for ARR17 and GATA15 as master regulators of sex determination

Abstract The Salicaceae family is of growing interest in the study of dioecy in plants because the sex determination region (SDR) has been shown to be highly dynamic, with differing locations and heterogametic systems between species. Without the ability to transform and regenerate Salix in tissue culture, previous studies investigating the mechanisms regulating sex in the genus Salix have been limited to genome resequencing and differential gene expression, which are mostly descriptive in nature, and functional validation of candidate sex determination genes has not yet been conducted. Here, we used Arabidopsis to functionally characterize a suite of previously identified candidate genes involved in sex determination and sex dimorphism in the bioenergy shrub willow Salix purpurea . Six candidate master regulator genes for sex determination were heterologously expressed in Arabidopsis, followed by floral proteome analysis. In addition, 11 transcription factors with predicted roles in mediating sex dimorphism downstream of the SDR were tested using DAP‐Seq in both male and female S. purpurea DNA. The results of this study provide further evidence to support models for the roles of ARR17 and GATA15 as master regulator genes of sex determination in S. purpurea , contributing to a regulatory system that is notably different from that of its sister genus Populus . Evidence was also obtained for the roles of two transcription factors, an AP2 / ERF family gene and a homeodomain‐like transcription factor, in downstream regulation of sex dimorphism.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Technical, economic, and load-following capabilities assessment of grid-connected geothermal and geothermal-solar hybrid systems

The technical and economic performance as well as the load-following capabilities of grid-connected geothermal hybrid systems were assessed in this work. The analyzed geothermal hybrid configuration is composed of a binary geothermal plant integrated with a concentrating solar-thermal system and underground thermal energy storage (UTES) through a primary heat exchanger. Physics-based models for the hybrid system for plant generation capacities of 1, 25, and 50 MW were developed from validated models for each subsystem. Also, an economic model was developed that accounts for different hybrid system capabilities, solar field sizes, and thermal storage duration. The advantage of the geothermal hybrid system was assessed by comparing the performance with the baseline benchmark geothermal plant with a similar configuration and generation capacity. It was found that hybridizing geothermal plants with concentrating solar and thermal energy storage not only improves the thermal efficiency by up to 8 percentage points when additional heat from the solar-UTES loop rises the evaporator temperatures from 70 to 125 °C, but also enhances the load-following capability for the geothermal plant, which can meet a typical residential load profile with a power rate of change 0.25 kW/s with an absolute error under 13 kW for a 1 MW plant. Other benefits of hybridization include resource preservation and a potential LCOE reduction of up to 56% for a 50 MW geothermal hybrid plant having a 50% solar share, a 1.4 solar multiple, and 24-h storage capacity. The results presented in this work demonstrate that hybridizing geothermal systems transforms them into a flexible and cost-effective solution for addressing the dynamic requirements of modern electric grids.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Chronological changes in soil biogeochemical properties of the glacier foreland of Midtre Lovénbreen, Svalbard, attributed to soil-forming factors

We report that Glacier forelands provide an excellent opportunity to investigate vegetation succession and soil development along the chronosequence; however, there are few studies on soil biogeochemical changes from environmental factors, aside from time. This study aimed to investigate soil development and biogeochemical changes in the glacier foreland of Midtre Lovenbreen, Svalbard, by considering various factors, including time. Eighteen vegetation and soil variables were measured at 38 different sampling sites of varying soil age, depth, and glacio-fluvial activity. Soil organic matter (SOM) was quantitatively measured, and the compositional changes in SOM were determined following size-density fractionation. In the topsoil, the soil organic carbon (SOC) and total nitrogen (N) content was found to increase along the soil chronosequence and were highly correlated with vegetation-associated variables. These findings suggest that plant-derived material was the main driver of the light fraction of SOM accumulation in the topsoil. The heavy fractions of SOM were composed of microbially transformed organic compounds, eventually contributing to SOM stabilization within short 90-yr deglaciation under harsh climatic conditions. In addition to time, the soil vertical profiles showed that other environmental parameters, also affected the soil biogeochemical properties. The high total phosphorous (P) content and electrical conductivity in the topsoil were attributed to unweathered subglacial materials and a considerable amount of inorganic ions from subglacial meltwater. The high P and magnesium content in the subsoil were attributed to parent materials, while the high sodium and potassium content in the surface soil were a result of sea-salt deposition. Glacio-fluvial runoff hampered ecosystem development by inhibiting vegetation development and SOM accumulation. This study emphasizes the importance of considering various soil-forming factors, including parent/subglacial materials, aeolian deposition, and glacio-fluvial runoff, as well as soil age, to obtain a comprehensive understanding of the ecosystem development in glacier forelands.

glacio-fluvial runoff↗

DOE OSTI2024

Using Generative AI to implement the discrepancy checker for a Nearly Autonomous Management and Control System for Advanced Reactors

Developments related to generative artificial intelligence (AI) have brought a major breakthrough in AI. These developments are rapidly accelerating developments in different science and engineering applications. Nearly Autonomous Management and Control (NAMAC) system provides recommendations to the operator for maintaining the safety and performance of the reactor. The discrepancy checker (DC) is an important component of the NAMAC) system, whose goal is to determine if the plant is moving towards the expected system state after the control actions are injected. In this work, we explore generative AI methods, particularly, a generative pretrained transformer (GPT) for implementing the DC function in NAMAC. The GPT-based DC aims to alert the operator in situations outside NAMAC’s scope and act as a chatbot the operator can use to retrieve relevant information. This study involves two versions of GPT developed by OpenAI: GPT-3.5 and GPT-4. These GPTs are trained on huge amounts of undisclosed general domain datasets. We explored two methods to adapt GPTs for DC implementation in NAMAC: fine-tuning and retrieval augmented generation. A small knowledge base (information file) that encompasses rules for DC implementation and some general information related to NAMAC has been created to support DC implementation using GPT. In this work, the GPT-based DC implementations have been tested for their reasoning abilities, comprehension, information retrieval, and extraction abilities. It should be noted that this paper only presents a preliminary study to test the feasibility of DC implementation using generative AI technology. Given the potential risks and severe consequences associated with nuclear reactor applications, combined with the black-box nature of AI, extensive offline and online testing and reliability analyses of GPT-based DCs are needed for further developing such capabilities.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Multi-Port Autonomous Reconfigurable Solar Power Plant

As the penetration level of power electronics increases and remote photovoltaic (PV) generation is integrated into the alternating current (ac) grid, the short-circuit ratio (SCR) at the point of interconnection of a hybrid PV–energy storage system (ESS) plant may be low. Additionally, the inertia of the alternating current (ac) grid may be low. The low SCRs and inertias can lead to reliability challenges in the power grid. These operating conditions require additional reinforcements, such as synchronous condensers, static var compensators, static synchronous compensators, and high-voltage direct current (HVdc) links/grids. HVdc links or grids may also provide the additional capability of connecting the plant to asynchronous grids and/or connecting asynchronous grids, among others. This scenario leads to discrete development of solar inverters, energy storage inverters, HVdc converters, and several transformers. Some of the problems associated with this discrete development and inverter-based generation include increased cost, lower reliability, and reduced efficiency associated with duplication of power electronics (PEs); competing controls of several individual discrete inverter-based generators due to the presence of multiple PEs, which leads to derating of the system; and transient stability problems arising from inverter-based generation, such as voltage/frequency events leading inverter shutdowns, voltage instability and control interactions in the formed weak grid, and harmonics caused by resonances of multiple inverters.

14 SOLAR ENERGY↗

DOE OSTI2020

A Tool for Performing Link Analysis, Operational Sequence Analysis, and Workload Analysis to Support Nuclear Power Plant Control Room Modernization

Nuclear power continues to have an imperative role for the U.S.’s electricity generation. However, for these nuclear power plants (NPPs) to remain economically viable, new strategies for reducing operations and maintenance costs must be explored. The U.S. Department of Energy Light Water Reactor Sustainability Program is researching how to digitally transform existing analog and hybrid main control rooms into a fully integrated control room that addresses this challenge. The transformation will fundamentally change the conduct of operations for these U.S. NPPs. Human factors engineering has a vital role in this effort, where traditional task analysis methods are important in informing the design of advanced human-system interface displays. This work describes a preliminary tool to support task analysis for the development of these advanced displays. Details on the use of this tool, including the specific task analysis methods offered, are presented in this paper.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

A distinct class of ferredoxin:NADP + oxidoreductase enzymes driving thermophilic ethanol production

Biofuel production from lignocellulosic biomass offers a transformative solution to reduce global fossil fuel dependency. Certain thermophilic anaerobes, including Clostridium thermocellum, show promise for renewable ethanol production due to their ability to break down plant material at high temperatures. However, achieving commercially viable ethanol yields has proven challenging despite extensive engineering efforts. Here, we characterized 27 ferredoxin:NADP + oxidoreductase (Fnor) enzymes for their enzyme activity, nicotinamide cofactor specificity, thermotolerance, and functional expression in C. thermocellum . We identified a subset of 10 of these enzymes as a novel class of Fnor enzymes suited for metabolic pathways aimed at high-titer ethanol production. When expressed in engineered C. thermocellum , these enzymes increased ethanol production up to 2.2-fold. These findings establish a novel ethanol pathway and provide insights into physiological roles and biotechnological applications of this new class of Fnor enzymes.

09 BIOMASS FUELS↗

DOE OSTI2021

Mountain waves can impact wind power generation

Abstract. Mountains can modify the weather downstream of the terrain. In particular, when stably stratified air ascends a mountain barrier, buoyancy perturbations develop. These perturbations can trigger mountain waves downstream of the mountains that can reach deep into the atmospheric boundary layer where wind turbines operate. Several such cases of mountain waves occurred during the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River basin in the lee of the Cascade Range bounding the states of Washington and Oregon in the Pacific Northwest of the United States. Signals from the mountain waves appear in boundary layer sodar and lidar observations as well as in nacelle wind speeds and power observations from wind plants. Weather Research and Forecasting (WRF) model simulations also produce mountain waves and are compared to satellite, lidar, and sodar observations. Simulated mountain wave wavelengths and wave propagation speeds (group velocities) are analyzed using the fast Fourier transform. We found that not all mountain waves exhibit the same speed and conclude that the speed of propagation, magnitudes of wind speeds, or wavelengths are important parameters for forecasters to recognize the risk for mountain waves and associated large drops or surges in power. When analyzing wind farm power output and nacelle wind speeds, we found that even small oscillations in wind speed caused by mountain waves can induce oscillations between full-rated power of a wind farm and half of the power output, depending on the position of the mountain wave's crests and troughs. For the wind plant analyzed in this paper, mountain-wave-induced fluctuations translate to approximately 11 % of the total wind farm output being influenced by mountain waves. Oscillations in measured wind speeds agree well with WRF simulations in timing and magnitude. We conclude that mountain waves can impact wind turbine and wind farm power output and, therefore, should be considered in complex terrain when designing, building, and forecasting for wind farms.

17 WIND ENERGY↗

DOE OSTI2021

Soil metabolome response to whole-ecosystem warming at the Spruce and Peatland Responses under Changing Environments experiment

In this study, a suite of complementary environmental geochemical analyses, including NMR and gas chromatography-mass spectrometry (GC-MS) analyses of central metabolites, Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) of secondary metabolites, and lipidomics, was used to investigate the influence of organic matter (OM) quality on the heterotrophic microbial mechanisms controlling peatland CO2, CH4, and CO2:CH4 porewater production ratios in response to climate warming. Our investigations leverage the Spruce and Peatland Responses under Changing Environments (SPRUCE) experiment, where air and peat warming were combined in a whole-ecosystem warming treatment. We hypothesized that warming would enhance the production of plant-derived metabolites, resulting in increased labile OM inputs to the surface peat, thereby enhancing microbial activity and greenhouse gas production. Because shallow peat is most susceptible to enhanced warming, increases in labile OM inputs to the surface, in particular, are likely to result in significant changes to CO2 and CH4 dynamics and methanogenic pathways. In support of this hypothesis, significant correlations were observed between metabolites and temperature consistent with increased availability of labile substrates, which may stimulate more rapid turnover of microbial proteins. An increase in the abundance of methanogenic genes in response to the increase in the abundance of labile substrates was accompanied by a shift toward acetoclastic and methylotrophic methanogenesis. Our results suggest that as peatland vegetation trends toward increasing vascular plant cover with warming, we can expect a concomitant shift toward increasingly methanogenic conditions and amplified climate–peatland feedbacks.

Wilson, Rachel M.↗