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At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

PlasmidMaker: a Versatile, Automated, and High Throughput End-to-End Platform for Plasmid Construction

Plasmids are used extensively in basic and applied biology. However, design and construction of plasmids, specifically the ones carrying complex genetic information, remains one of the most time-consuming, labor-intensive, and rate-limiting steps in performing sophisticated biological experiments. Here, we report the development of a versatile, robust, automated end-to-end platform named PlasmidMaker that allows error-free construction of plasmids with virtually any sequences in a high-throughput manner. This platform consists of a most versatile DNA assembly method using Pyrococcus furiosus Argonaute ( Pf Ago)-based artificial restriction enzymes, a user-friendly frontend for plasmid design, and a backend that streamlines the workflow and integration with a robotic system. As a proof of concept, we used this platform to generate ~100 plasmids from six different species ranging from 5 to 18 kb in size from up to 11 DNA fragments within 3 days. PlasmidMaker should greatly expand the potential of synthetic biology.

Enghiad, Behnam↗
DOE OSTI2026

Constrained variational optimization of counting-time allocation in sequential scattering measurements: Application to Bonse–Hart USANS

Sequential scattering measurements are often performed under a fixed experimental-time budget, even though the expected count rate varies strongly across the measured coordinate. When the dwell time at each measurement position can be controlled independently, this variation creates a general resource-allocation problem: how should the available time be distributed to minimize the uncertainty of the reconstructed profile? We formulate this problem as a constrained variational optimization for measurements governed by Poisson counting statistics. When each measurement is treated independently, minimizing the averaged squared relative uncertainty yields an inverse-square-root intensity allocation. The formulation is then generalized to include correlations between neighboring measurements and an instrumental resolution operator, leading to an allocation criterion that equalizes the marginal reduction in posterior uncertainty per unit measurement time. Bonse–Hart ultra-small-angle neutron scattering (USANS), in which reciprocal space is sampled sequentially through analyzer-angle stepping, provides an experimentally grounded application. Computational benchmarking shows that the optimized allocation outperforms uniform-time and constant-relative-error strategies, while application to an experimentally measured graphite USANS profile from the Spallation Neutron Source, using Poisson resampling under alternative schedules, demonstrates how counting time should be redistributed toward weak-intensity regions under an identical total duration. The resulting framework applies to sequential scattering and related scanning measurements whenever local dwell times are adjustable and directly determine the measurement uncertainties, and when the relevant correlation and instrumental-response models are available.

Tung, Chi-Huan [ORNL] (ORCID:0000000221972074)↗
DOE OSTI2020

Enabling Large-Scale Condensed-Phase Hybrid Density Functional Theory Based Ab Initio Molecular Dynamics. 1. Theory, Algorithm, and Performance

By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semilocal density functional theory (DFT) and thereby furnish a more accurate and reliable description of the underlying electronic structure in systems throughout biology, chemistry, physics, and materials science. However, the high computational cost associated with the evaluation of all required EXX quantities has limited the applicability of hybrid DFT in the treatment of large molecules and complex condensed-phase materials. To overcome this limitation, we describe a linear-scaling approach that utilizes a local representation of the occupied orbitals (e.g., maximally localized Wannier functions (MLWFs)) to exploit the sparsity in the real-space evaluation of the quantum mechanical exchange interaction in finite-gap systems. In this work, we present a detailed description of the theoretical and algorithmic advances required to perform MLWF-based ab initio molecular dynamics (AIMD) simulations of large-scale condensed-phase systems of interest at the hybrid DFT level. We focus our theoretical discussion on the integration of this approach into the framework of Car–Parrinello AIMD, and highlight the central role played by the MLWF-product potential (i.e., the solution of Poisson’s equation for each corresponding MLWF-product density) in the evaluation of the EXX energy and wave function forces. We then provide a comprehensive description of the exx algorithm implemented in the open-source Quantum ESPRESSO program, which employs a hybrid MPI/OpenMP parallelization scheme to efficiently utilize the high-performance computing (HPC) resources available on current- and next-generation supercomputer architectures. Furthermore, this is followed by a critical assessment of the accuracy and parallel performance (e.g., strong and weak scaling) of this approach when AIMD simulations of liquid water are performed in the canonical (NVT) ensemble. With access to HPC resources, we demonstrate that exx enables hybrid DFT-based AIMD simulations of condensed-phase systems containing 500–1000 atoms (e.g., (H₂O)₂₅₆) with a wall time cost that is comparable to that of semilocal DFT. In doing so, exx takes us one step closer to routinely performing AIMD simulations of complex and large-scale condensed-phase systems for sufficiently long time scales at the hybrid DFT level of theory.

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

Evidence for autotrophic growth of purple sulfur bacteria using pyrite as electron and sulfur source

ABSTRACT Purple sulfur bacteria (PSB) are capable of anoxygenic photosynthesis via oxidizing reduced sulfur compounds and are considered key drivers of the sulfur cycle in a range of anoxic environments. In this study, we show that Allochromatium vinosum (a PSB species) is capable of autotrophic growth using pyrite as the electron and sulfur source. Comparative growth profile, substrate characterization, and transcriptomic sequencing data provided valuable insight into the molecular mechanisms underlying the bacterial utilization of pyrite and autotrophic growth. Specifically, the pyrite-supported cell cultures (“py”’) demonstrated robust but much slower growth rates and distinct patterns from their sodium sulfide-amended positive controls. Up to ~200-fold upregulation of genes encoding various c - and b -type cytochromes was observed in “py,” pointing to the high relevance of these molecules in scavenging and relaying electrons from pyrite to cytoplasmic metabolisms. Conversely, extensive downregulation of genes related to LH and RC complex components indicates that the electron source may have direct control over the bacterial cells’ photosynthetic activity. In terms of sulfur metabolism, genes encoding periplasmic or membrane-bound proteins (e.g., FccAB and SoxYZ) were largely upregulated, whereas those encoding cytoplasmic proteins (e.g., Dsr and Apr groups) are extensively suppressed. Other notable differentially expressed genes are related to flagella/fimbriae/pilin(+), metal efflux(+), ferrienterochelin(−), and [NiFe] hydrogenases(+). Characterization of the biologically reacted pyrite indicates the presence of polymeric sulfur. These results have, for the first time, put the interplay of PSB and transition metal sulfide chemistry under the spotlight, with the potential to advance multiple fields, including metal and sulfur biogeochemistry, bacterial extracellular electron transfer, and artificial photosynthesis. IMPORTANCE Microbial utilization of solid-phase substrates constitutes a critical area of focus in environmental microbiology, offering valuable insights into microbial metabolic processes and adaptability. Recent advancements in this field have profoundly deepened our knowledge of microbial physiology pertinent to these scenarios and spurred innovations in biosynthesis and energy production. Furthermore, research into interactions between microbes and solid-phase substrates has directly linked microbial activities to the surrounding mineralogical environments, thereby enhancing our understanding of the relevant biogeochemical cycles. Our study represents a significant step forward in this field by demonstrating, for the first time, the autotrophic growth of purple sulfur bacteria using insoluble pyrite (FeS2) as both the electron and sulfur source. The presented comparative growth profiles, substrate characterizations, and transcriptomic sequencing data shed light on the relationships between electron donor types, photosynthetic reaction center activities, and potential extracellular electron transfer in these organisms capable of anoxygenic photosynthesis. Furthermore, the findings of our study may provide new insights into early-Earth biogeochemical evolutions, offering valuable constraints for understanding the environmental conditions and microbial processes that shaped our planet’s history.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Removal of radon progeny from delicate surfaces

210 Po α-decay-driven neutron background is a concern for many rare event search experiments. It is a difficult to control background because its radiogenic component depends on the air exposure history of parts. In this study, we demonstrate that about half of the radon progeny 210 Po can be removed from copper and silicon surfaces relatively easily by wiping a copper sample with acetone-wetted tissue and a silicon detector with acetone soaked cotton balls. For a copper sample, we demonstrate that long-lived 210 Pb is removed with similar effectiveness. For copper, allocated the longest counting time, additional wiping was found to be largely ineffective. For silicon, the removal effectiveness has large uncertainties. Additional cleaning showed a small but statistically significant effect. Here, capitalizing on this trivial cleaning step will allow experiments to relax their requirements on the allowable air exposure time during construction, leading to cost and time savings.

Radioactivity↗
DOE OSTI2023

Implementation of AI/DEEP learning disruption predictor into a plasma control system

Abstract This paper reports on advances in the state‐of‐the‐art deep learning disruption prediction models based on the Fusion Recurrent Neural Network (FRNN) originally introduced in a 2019 NATURE publication [ https://doi.org/10.1038/s41586‐019‐1116‐4 ]. In particular, the predictor now features not only the “disruption score,” as an indicator of the probability of an imminent disruption, but also a “sensitivity score” in real time to indicate the underlying reasons for the imminent disruption. This adds valuable physics interpretability for the deep learning model and can provide helpful guidance for control actuators now implemented into a modern plasma control system (PCS). The advance is a significant step forward in moving from modern deep learning disruption prediction to real‐time control and brings novel AI‐enabled capabilities relevant for application to the future burning plasma ITER system. Our analyses use large amounts of data from JET and DIII‐D vetted in the earlier NATURE publication. In addition to “when” a shot is predicted to disrupt, this paper addresses reasons “why” by carrying out sensitivity studies. FRNN is accordingly extended to use more channels of information, including measured DIII‐D signals such as (i) the “n1rms” signal that is correlated with the n = 1 modes with finite frequency, including neoclassical tearing mode and sawtooth dynamics; (ii) the bolometer data indicative of plasma impurity control; and (iii) “q‐min”—the minimum value of the safety factor relevant to the key physics of kink modes. The additional channels and interpretability features expand the ability of the deep learning FRNN software to provide information about disruption subcategories as well as more precise and direct guidance for the actuators in a PCS.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Boride-Carbon Hybrid Technology to Produce Ultra-Wear and Corrosion Resistant Surfaces for Applications in Harsh Conditions (Final Technical Report)

Engineered functional surfaces play an important role to enable new products and manufacturing processes that can endure harsh service conditions such as high impact and contact loads, highly abrasive wear, extreme temperatures, and corrosive environments. Engineered surfaces can also be instrumental to improving the efficient use of energy by reducing frictional losses and extending service life. The main objective of this project was to develop a hybrid surface engineering technology that combines the advantages of a novel ultrafast boriding process with the next generation of superhard carbon coatings. The hypothesis was that this hybrid process will offer an unprecedented combination of wear and corrosion resistance, low frictional losses and affordability for treated parts so that it can be utilized in many applications. During this project, a duplex process was developed that combines the advantages of ultra-fast electrochemical boriding with those of hard tetrahedral amorphous carbon coatings. Both technologies can be combined to form a hybrid technology that is characterized by low friction and wear properties combined with corrosion and fatigue resistance. Good adhesion of both layers to each other was one main goal of this project, that has been achieved with HF1 adhesion through the Rockwell-C adhesion test. In this project, the mechanical properties of the hybrid coating were modeled through a finite-element analysis approach. We can conclude that the FEA model resembles the actual samples and be utilized to predict mechanical behavior under impacts. Based on this model, application-oriented load conditions can be simulated for optimal layer design regarding thickness and mechanical properties. To exemplify, one conclusion that can be drawn from the nanoindentation model is a boride layer thickness of 50 µm is sufficient to effectively support the carbon coating on the identified AISI 1045 low carbon steel substrate material. The duplex treatment yields wear rates as low as 6 x 10 -8 mm 3 N -1 m -1 and a coefficient of friction of 0.14 when tested against a steel counter face in a ball-on-disk test setup. On the other hand, the wear rate of the only-borided AISI 1045 steel was 5 x 10 -5 mm 3 N -1 m -1 , about three orders of magnitude higher than the duplex coating. At the same time, duplex treated samples experience corrosion resistance, which could not be achieved with single-layer carbon coatings. The developed surface treatment withstands a 3-hour exposure to 15% HCl, while the only carbon coated counter sample shows severe delamination of the coating due to pin hole corrosion. The boride layer is chemically stable and pin hole free because it is formed through an electrochemical process under high current densities (700 mA/cm 2 ) and high temperature. Additionally, the hybrid coating led to at least 3x increase in fatigue strength of the steel substrate, which exceeds the target performance of 30% improvement. There are numerous potential applications for the duplex coatings. A representative application is bearing ball coatings for off-shore windmills. Compared to currently employed surface technologies in this field, the initial costs of applying our technology might be higher due to more process steps but the performance benefits lead to an increased life time of treated parts, which will lower the maintenance and replacement costs in the long-term. To validate the technology for this specific application, the team is currently investigating the process of white etching crack initiation of the duplex coating in collaboration with ANL. Overall, this project successfully validated that the boride-carbon hybrid technology can withstand harsh conditions. One possible approach to commercialization under consideration is to transfer the technology to a startup or an existing coatings company.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A persistent adjoint method with dynamic time-scaling and an application to mass action kinetics

In this article, we consider an optimization problem where the objective function is evaluated at the fixed-point of a contraction mapping parameterized by a control variable, and optimization takes place over this control variable. Since the derivative of the fixed-point with respect to the parameter can usually not be evaluated exactly, an adjoint dynamical system can be used to estimate gradients. Using this estimation procedure, the optimization algorithm alternates between derivative estimation and an approximate gradient descent step. We analyze a variant of this approach involving dynamic time-scaling, where after each parameter update the adjoint system is iterated until a convergence threshold is passed. Here, we prove that, under certain conditions, the algorithm can find approximate stationary points of the objective function. We demonstrate the approach in the settings of an inverse problem in chemical kinetics, and learning in attractor networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Tracking structural solvent reorganization and recombination dynamics following e – photoabstraction from aqueous I – with femtosecond x-ray spectroscopy and scattering

Here we present a sub-picosecond resolved investigation of the structural solvent reorganization and geminate recombination dynamics following 400 nm two-photon excitation and photodetachment of a valence p electron from the aqueous atomic solute, I–(aq). The measurements utilized time-resolved X-ray Absorption Near Edge Structure (TR-XANES) spectroscopy and X-ray Solution Scattering (TR-XSS) at the Linac Coherent Light Source x-ray free electron laser in a laser pump/x-ray probe experiment. The XANES measurements around the L 1 -edge of the generated nascent iodine atoms (I 0 ) yield an average electron ejection distance from the iodine parent of 7.4 ± 1.5 Å with an excitation yield of about 1/3 of the 0.1M NaI aqueous solution. The kinetic traces of the XANES measurement are in agreement with a purely diffusion-driven geminate iodine–electron recombination model without the need for a long-lived (I 0 :e–) contact pair. Nonequilibrium classical molecular dynamics simulations indicate a delayed response of the caging H 2 O solvent shell and this is supported by the structural analysis of the XSS data: We identify a two-step process exhibiting a 0.1 ps delayed solvent shell reorganization time within the tight H-bond network and a 0.3 ps time constant for the mean iodine–oxygen distance changes. The results indicate that most of the reorganization can be explained classically by a transition from a hydrophilic cavity with a well-ordered first solvation shell (hydrogens pointing toward I–) to an expanded cavity around I 0 with a more random orientation of the H 2 O molecules in a broadened first solvation shell.

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

Boron‐Doped Nano‐Crystalline Coated Carbon Fibers for Phasic Dopamine Sensing

Real time, chronic electrochemical detection of neurotransmitters will provide a positive step in the treatment and understanding of neurological disease. However, current electrodes using carbon fibers (CF) fail to perform chronically. While diamond-based coatings show promise in improving their longevity, achieving a uniform layer of such coatings on CFs is challenging, and the electrodes often lose sensitivity after coating. In this work, a complete and uniform boron-doped nanocrystalline material grown in a diamond reactor (B-NCD) was developed to coat CF microelectrodes for neurochemical sensing. The coating was characterized electrically, optically, mechanically, and chemically. The B-NCD coated CF electrodes were able to detect phasic dopamine at a sensitivity comparable to the most widely used alternatives (uncoated and PEDOT:Nafion coated CFs). During biofouling testing, the B-NCD coated CF electrodes demonstrated better stability than uncoated CFs and comparable performance to PEDOT:Nafion coated CFs. Moreover, B-NCD exhibited no signs of degradation during consecutive FSCV applications, while uncoated and PEDOT:Nafion coated CF electrodes degraded significantly over time. Furthermore, the B-NCD coating supported the survival and development of neurons and astrocytes in vitro, exhibited excellent adhesion and durability during mechanical bending testing, and enabled successful in vivo recording of phasic dopamine release in the rat brain. Overall, B-NCD coated CFs present as an ideal candidate for chronic, flexible neural implantable electrodes for long-term neurochemical monitoring.

carbon fiber↗
DOE OSTI2024

Controlled mechanochemical coupling of anti-junctions in DNA origami arrays

Abstract Allostery is a hallmark of cellular function and important in every biological system. Still, we are only starting to mimic it in the laboratory. Here, we introduce an approach to study aspects of allostery in artificial systems. We use a DNA origami domino array structure which–upon binding of trigger DNA strands–undergoes a stepwise allosteric conformational change. Using two FRET probes placed at specific positions in the DNA origami, we zoom in into single steps of this reaction cascade. Most of the steps are strongly coupled temporally and occur simultaneously. Introduction of activation energy barriers between different intermediate states alters this coupling and induces a time delay. We then apply these approaches to release a cargo DNA strand at a predefined step in the reaction cascade to demonstrate the applicability of this concept in tunable cascades of mechanochemical coupling with both spatial and temporal control.

Science & Technology - Other Topics↗
DOE OSTI2023

Incorporating Operational Uncertainties into the Dispatch of an Integrated Solar and Storage System

The economic assessment of hybrid energy systems (HES) pairing battery energy storage systems (BESSs) and photovoltaics (PV) is highly important for advancing their deployment in power systems. This paper presents an innovative assessment framework, including an optimal control policy for dispatch under uncertainty and procedures for exploring control parameters that maximize economic benefits. The proposed dispatch policy consists of two steps using system forecast information. The first step is to determine whether a BESS will be used within an operational scheduling time frame based on the probability of events and their thresholds. Once the dispatch of BESS is triggered, a model predictive control (MPC) is carried out in the second step for scheduling using the expected value of system information. By exercising this policy with different thresholds, one can explore the trade-offs between short-term benefits and battery lifetime, and identify an optimal threshold that maximizes the total economic benefits within the battery lifetime. An evaluation study in a real-world HES project is presented to illustrate the proposed framework. Compared with traditional optimal dispatch algorithms, the proposed method can significantly improve the economic benefits of an HES scheduled under forecast uncertainties.

Ma, Xu↗
DOE OSTI2022

Facile Electrodeposition and Aging to Generate 3-Dimensional α-MnO 2 Battery Cathodes

Conventional tape casting forms 2-dimensional (2D) electrodes containing active material, conductive additive, and binder with restricted ion access as electrodes increase in thickness. To improve the transport properties, 3D architectures were developed using electrodeposition to ensure contact between the active material with the substrate, and provide enhanced electrolyte access. This paper investigates electrodeposition of cryptomelane ( α -MnO 2 ) as a model cathode material to efficiently accommodate (de)lithation and increase areal capacity vs conventional 2D coatings. Electodeposited samples on titantium (Ti) foil substrates were characterized using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM) and show a linear increase of the average oxidation of Mn (3.5–3.8) and active mass loading (1.27–9.9 mg) with deposition and aging times (0–120 min). The initial deposition is amorphous and forms the crystalline material during the elevated temperature aging step. The active material, α -MnO 2 , was also deposited on C-cloth and these cathodes at deposition times of 3, 6, and 9 min deliver 9, 36, and 69% higher areal capacities, respectively, at 0.2 mA cm −2 compared to conventional 2D electrodes with a mass loading equal to the 3 min sample. These results demonstrate the benefit of α -MnO 2 within a porous architecture providing enhanced transport properties.

Electrochemistry↗
DOE OSTI2022

Resonant double-core excitations with ultrafast, intense X-ray pulses

Intense few-to-sub-femtosecond soft X-ray pulses can produce neutral, two-site excited double-core-hole states by promoting two core electrons to the same unoccupied molecular orbital. We theoretically investigate double nitrogen K-edge excitations of nitrous oxide (N$_2$O) with multiconfigurational electronic structure calculations. We show that the second core-excitation energy is reduced with respect to its ground state value. A site-selective double core-excitation mechanism using intense few-femtosecond x-rays is investigated using time-dependent Schrodinger equation (TDSE) simulations. The subsequent two-step Auger-Meitner and two-electrons-one-electron decay spectra of the double core-excited states are analysed using a Mulliken population analysis of the multiconfirational wavefunctions. The change in the electron emission lineshape between the absorption of 1 or 2 photons in the resonant core-excitation is predicted by combining this approach with the TDSE simulations. Furthermore, we examine the possibility of resolving the double core-excited states with X-ray pump-probe techniques by calculating the chemical shifts of the core-electron binding energy of the core-excited states and decay products.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Integrating very-high-resolution imagery, Sentinel-2 time-series data, and machine learning to map shrub fractional abundance across arid and semi-arid ecosystems in China

Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, we generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km 2 each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R 2 = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R 2 = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 °C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.

Arid and semi-arid landscapes↗
DOE OSTI2025

Efficient Simulation of Cascading Outages Using an Energy Function-Embedded Quasi-Steady-State Model

Here, this paper proposed an energy function-embedded quasi-steady-state model for efficient simulation of cascading outages on a power grid while addressing transient stability concerns. Compared to quasi-steady-state models, the proposed model incorporates short-term dynamic simulation and an energy function method to efficiently evaluate the transient stability of a power grid together with outage propagation without transient stability simulation. Cascading outage simulation using the proposed model conducts three steps for each disturbance such as a line outage. First, it performs time-domain simulation for a short term to obtain a post-disturbance trajectory. Second, along the trajectory, the system state with the local maximum potential energy is found and used as the initial point to search for a relevant unstable equilibrium by Newton's method. Third, the transient energy margin is estimated based on this unstable equilibrium to predict an out-of-step condition with generators. The proposed energy function-embedded quasi-steady-state model is tested in terms of its accuracy and time performance on an NPCC 140-bus power system and compared to a quasi-steady-state model embedding transient stability simulation.

Guo, Zhenping [Univ. of Tennessee, Knoxville, TN (↗
DOE OSTI2025

Lignin Extraction and Condensation as a Function of Temperature, Residence Time, and Solvent System in Flow-through Reactors

Solvolytic extraction of lignin from biomass is a critical step in lignin-first biorefining, including the reductive catalytic fractionation (RCF) process. Key to optimal RCF processing is the ability to rapidly extract lignin from biomass at high delignification extents and transfer the lignin molecules to a catalyst surface in a time frame that minimizes lignin condensation reactions. Here, we use a flow-through reactor to study the effects of temperature (175-250 °C), residence time (9 to 36 min), and solvent composition (methanol and methanol-water) on lignin extraction and condensation. We evaluated three metrics at each condition: total delignification, delignification rate, and extent of condensation, the latter measured by a decrease in monomer yield for batch hydrogenolysis reactions of solvolysis liquor compared to batch RCF reactions. We observe that delignification is predominantly determined by temperature, while residence time dictates the lignin condensation extent. Moreover, the extent of both extraction and condensation increased in the methanol-water solvent system compared to that in the methanol system. Lignin extracted in methanol is stable up to 18-min residence times at or below 225 °C, while a majority of the lignin extracted in methanol-water is condensed with a 9-min residence time at 200 °C. These results can inform reactor designs and solvent selection for lignin-first biorefining processes that aim to physically separate the biomass and catalyst.

09 BIOMASS FUELS↗