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DOE OSTI2016

Physiochemical Characterization of Lignocellulosic Biomass Dissolution by Flowthrough Pretreatment

Comprehensive understanding of biomass solubilization chemistry in aqueous pretreatment such as water-only and dilute acid flowthrough pretreatment is of fundamental importance to achieve the goal of valorizing biomass to fermentable sugars and lignin for biofuels production. In this study, poplar wood was flowthrough pretreated by water-only or 0.05% (w/w) sulfuric acid at different temperatures (220-270 °C), flow rate (25 mL/min), and reaction times (8-90 min), resulting in significant disruption of the lignocellulosic biomass. Ion chromatography (IC), Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD) analysis, and solid state cross-polarization/magic angle spinning (CP/MAS) 13C nuclear magnetic resonance (NMR) spectroscopy were applied to characterize the pretreated biomass whole slurries in order to reveal depolymerization as well as solubilization mechanism and identify unique dissolution structural features during these pretreatments. Results showed temperature-dependent cellulose decrystallization in flowthrough pretreatment. Crystalline cellulose was completely disrupted, and mostly converted to amorphous cellulose and oligomers by water-only operation at 270 °C for 10 min and by 0.05 wt % H2SO4 flowthrough pretreatment at 220 °C for 12 min. Flowthrough pretreatment with 0.05% (w/w) H2SO4 led to a greater disruption of structures in pretreated poplar at a lower temperature compared to water-only pretreatment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Characterization of Lignin Streams during Bionic Liquid-Based Pretreatment from Grass, Hardwood, and Softwood

Delignification as a function of ionic liquid (IL) pretreatment has potential in terms of recovering and converting the fractionated lignin streams to renewable products. Renewable biogenic ionic liquids, or bionic liquids (eg. cholinium lysinate, ([Ch][Lys])), provide opportunities in terms of effective, economic and sustainable lignocellulosic biomass pretreatment. We have evaluated [Ch][Lys] pretreatment in terms of sugar and lignin yields for three different feedstocks: switchgrass, eucalyptus, and pine. Four lignin streams isolated during [Ch][Lys] pretreatment and enzymatic hydrolysis were comprehensively analyzed, tracking their changes in physical-chemical structures. We observed changes in major lignin linkages and lignin aromatics units (p-hydroxyphenyl (H), guaiacyl (G), and syringil (S)) that occurred during pretreatment. A compositional analysis of the different process streams and a comprehensive mass balance in conjunction with multiple analytical techniques (Nuclear Magnetic Resonance (NMR), Mass Spectroscopy, Fourier Transform Infrared Spectroscopy (FT-IR), Gel Permeation Chromatography (GPC)) is presented. Qualitative and quantitative analyses indicates that there are significantly more lignin-carbohydrate interactions for G-rich lignin in pine. The lignin removal and extent of lignin depolymerization for switchgrass and eucalyptus were higher than pine, and follows the order of switchgrass > eucalyptus > pine. The recovered lignin from pretreated liquid contained a lower relative amount of carbohydrate signals than raw biomass, indicating a high degree of dissociation of lignin carbohydrate complex (LCC) linkages for all samples analyzed. The insights gained from this work contribute to better understanding of physiochemical properties of lignin streams generated during [Ch][Lys] pretreatment, offering a starting point for lignin valorization strategies.

lignin, ionic liquids, NMR↗

DOE OSTI2019

Lignin-KMC: A Toolkit for Simulating Lignin Biosynthesis

Lignin is a bountiful biopolymer of phenylpropanoid monomers that is critical for plant structure and function. Based on the abundance of lignin in the biosphere and interest in lignin valorization, a more comprehensive understanding of lignin biosynthesis is imperative. We introduce an open-source software toolkit, Lignin-KMC, that combines kinetic Monte Carlo and first-principles calculations of radical coupling events to model lignin biosynthesis in silico. Lignification is simulated using the Gillespie algorithm with rates derived from density functional theory calculations of individual fragment couplings. Using this approach, we confirm experimental findings regarding the impact of lignification conditions on the polymer structure such as (1) the positive correlation between sinapyl alcohol fraction and depolymerization yield and (2) the primarily benzodioxane linked structure of C-lignin. Furthermore, we identify the in planta monolignol supply rate as a possible control mechanism for lignin biosynthesis based on evolutionary stresses. These examples not only highlight the robustness of our modeling framework but also motivate future studies of new lignin types, unexplored monolignol chemistries, and lignin structure predictions, all with an overarching aim of developing a more comprehensive molecular understanding of native lignin, which, in turn, can accelerate the biological and chemistry communities interested in this important biopolymer.

09 BIOMASS FUELS↗

DOE OSTI2025

Mechanistic Insights into Molecular Copper Hydride Catalysis: the Kinetic Stability of CuH Monomers toward Aggregation is a Critical Parameter for Catalyst Performance

The activity of molecular copper hydride (CuH) complexes towards the selective insertion of unsaturated hydrocarbons under mild conditions has contributed significantly to versatile methodologies for upgrading these feedstocks. However, these catalysts are particularly susceptible to deleterious aggregation, leading to the depletion of active CuH species. Little is known about the mechanisms of CuH aggregation, how it influences overall catalyst performance, and how it can be controlled. We address these challenges with mechanistic studies on a model reaction of unactivated alkene hydroboration catalyzed by (IPr*CPh 3 )CuH (LCuH). Here, we report a comprehensive mechanistic investigation of this system, identifying an aggregation pathway that continuously depletes catalytically active LCuH to form inactive CuH clusters during turnover. Deactivation of LCuH is controlled primarily by the competition between the kinetics of the initial LCuH dimerization step and that of alkene insertion. We therefore propose that a more comprehensive understanding of CuH catalyst performance must account for the kinetics of the initial LCuH dimerization step, revising a previously explored thermodynamic understanding of CuH aggregation, where the concentration of active species is controlled by equilibria established between CuH dimers and monomers. With a series of (NHC)CuH congeners (NHC = N-heterocyclic carbene), we demonstrate that ostensibly minor structural modifications to the ligand peripheries can drastically affect the LCuH dimerization kinetics, while maintaining reactivity towards on–cycle alkene insertion. We employed a computational approach based on molecular dynamics simulations to provide an in-depth understanding of how specific structural ligand modifications can substantially increase the kinetic stability of monomeric CuH catalysts. Our combined experimental and computational studies suggest strategies for rational ligand design that can be broadly applied to molecular catalyst systems that are susceptible to deactivation via aggregation pathways.

Ryan, David E. [Pacific Northwest National Laborat↗

DOE OSTI2026

Identification of Solid-Electrolyte Interphase Species by Joint Characterization of Li-Ion Battery Chemistry by Mass Spectrometry and Electrochemical Reaction Networks

The formation and stability of the solid-electrolyte interphase (SEI) play central roles in determining the long-term performance and safety of modern electrochemical energy storage systems. Despite decades of research, the SEI’s heterogeneous, dynamic, and multiphase nature has defied comprehensive molecular-level characterization, creating a critical knowledge gap that limits rational battery design. In this work, we introduce a computational−experimental framework that integrates high-throughput quantum chemistry calculations, data-driven electrochemical reaction networks (eCRNs), stochastic algorithms, and laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry (LDI-FTICR-MS) to unravel SEI formation in carbonatebased electrolytes without imposing predefined mechanisms. We constructed the most comprehensive eCRN to date, spanning over 10,000 species and 209 million reactions. Through stochastic network analysis, we successfully recovered 27 species that were previously reported in the literature and predicted 28 novel SEI species nearly doubling our scientific knowledge in this area. Each new species was rigorously confirmed through advanced mass spectral analysis of its distinct molecular and isotopic signatures. We kinetically refined the formation pathways for a select set of both previously reported and novel SEI products, revealing kinetically feasible elementary reaction mechanisms with activation barriers below 1 eV. This computational−experimental approach deepens our molecular-level understanding of SEI chemistry by resolving which species form and through which decomposition mechanisms they emerge. Such knowledge provides the foundation necessary to connect electrolyte composition to the resulting SEI components, a critical step toward a more informed electrolyte development in next-generation lithium-based batteries.

25 ENERGY STORAGE↗

DOE OSTI2014

Conditions for critical effects in the mass action kinetics equations for water radiolysis

We report on a subtle global feature of the mass action kinetics equations for water radiolysis that results in predictions of a critical behavior in H2O2 and associated radical concentrations. While radiolysis kinetics has been studied extensively in the past, it is only in recent years that high speed computing has allowed the rapid exploration of the solution over widely varying dose and compositional conditions. We explore the radiolytic production of H2O2 under various externally fixed conditions of molecular H2 and O2 that have been regarded as problematic in the literature – specifically, “jumps” in predicted concentrations, and inconsistencies between predictions and experiments have been reported for alpha radiolysis. We computationally map-out a critical concentration behavior for alpha radiolysis kinetics using a comprehensive set of reactions. We then show that all features of interest are accurately reproduced with 15 reactions. An analytical solution for steady-state concentrations of the 15 reactions reveals regions in [H2] and [O2] where the H2O2 concentration is not unique – both stable and unstable concentrations exist. The boundary of this region can be characterized analytically as a function of G-values and rate constants independent of dose rate. Physically, the boundary can be understood as separating a region where a steady-state H2O2 concentration exists, from one where it does not exist without a direct decomposition reaction. We show that this behavior is consistent with reported alpha radiolysis data and that no such behavior should occur for gamma radiolysis. We suggest experiments that could verify or discredit a critical concentration behavior for alpha radiolysis and could place more restrictive ranges on G-values from derived relationships between them.

Wittman, Richard S.↗

DOE OSTI2015

Atomic Structure of Au 329 (SR) 84 Faradaurate Plasmonic Nanomolecules

To design novel nanomaterials, it is important to precisely control the composition, determine the atomic structure, and manipulate the structure to tune the materials property. Here we present a comprehensive characterization of the material whose composition is Au329(SR)84 precisely, therefore referred to as a nanomolecule. The size homogeneity was shown by electron microscopy, solution X-ray scattering, and mass spectrometry. We proposed its atomic structure to contain the Au260 core using experiments and modeling of a total-scattering-based atomic-pair distribution functional analysis. HAADF- STEM images shows fcc-like 2.0 ± 0.1 nm diameter nanomolecules.

Kumara, Chanaka↗

DOE OSTI2014

Development of an Enhanced Metaproteomic Approach for Deepening the Microbiome Characterization of the Human Infant Gut

The early-life microbiota establishment in the human infant gut is highly variable and plays a crucial role in host nutrients and immunity maturation. While high-performance mass spectrometry (MS)-based metaproteomics is a powerful method for the functional characterization of complex microbial communities, the construction of comprehensive metaproteomic information in human fecal samples is inhibited by the presence of abundant human proteins. To alleviate this restriction, we have designed a novel metaproteomic strategy based on Double Filtering (DF) to enhance microbial protein characterization in complex fecal samples from healthy premature infants. We improved the overall depth of infant gut proteome measurement, with an increase in the number of identified low abundance proteins, and observed greater than twofold improvement in metrics for microbial protein identifications and quantifications with a relatively high rank correlation to control. We further showed the substantial enhancement of this approach for extensively interpreting microbial functional categories between infants by affording more detailed and confident identified categories. This approach provided an avenue for in-depth measurement in the microbial component of infant fecal samples and thus comprehensive characterization of infant gut microbiome functionality.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Model Hierarchies for Understanding Atmospheric Circulation

In this review, we highlight the complementary relationship between simple and comprehensive models in addressing key scientific questions to describe Earth's atmospheric circulation. The systematic representation of models in steps, or hierarchies, connects our understanding from idealized systems to comprehensive models and ultimately the observed atmosphere. First, we define three interconnected principles that can be used to characterize the model hierarchies of the atmosphere. We explore the rich diversity within the governing equations in the dynamical hierarchy, the ability to isolate and understand atmospheric processes in the process hierarchy, and the importance of the physical domain and resolution in the hierarchy of scale. We center our discussion on the large-scale circulation of the atmosphere and its interaction with clouds and convection, focusing on areas where simple models have had a significant impact. Our confidence in climate model projections of the future is based on our efforts to ground the climate predictions in fundamental physical understanding. This understanding is, in part, possible due to the hierarchies of idealized models that afford the simplicity required for understanding complex systems.

58 GEOSCIENCES↗

DOE OSTI2021

Reanalysis of Water Withdrawal for Irrigation, Electric Power, and Public Supply Sectors in the Conterminous United States, 1950–2016

Accurately measuring water use by the economy is essential for developing reliable models of water resource availability. Indeed, these models rely on retrospective analyses that provide insights into shifting human population demands and adaptions to water shortages. However, accurate, methodologically consistent, empirically authentic, and spatiotemporally comprehensive historical datasets for water withdrawals are scarce. Herein, we present a reanalysis of annual resolution (1950–2016) historical data set on irrigation, electric power, and public supply water withdrawal within the conterminous United States (US) at the county-level, and, for power plants, at the site-level. To estimate electric power water use, we synthesized a historically comprehensive list of generators and historic patterns in generation across fuels, prime movers, and cooling technologies. Irrigation water use estimation required building a crop-demand model that utilized historical information on irrigated acreage for crops and golf courses, stage-specific crop water demand, and climate information. To estimate public water supply use, we developed a random forest model constructed from information on population, infrastructure, climate, and land cover. These estimates generally agree with total county and state water use information provided by the US Geological Survey (USGS) water use circular and estimates generated from independent studies for specific years. However, we also observed discrepancies between our estimates and USGS data that appear to be caused by inconsistencies in the methods used by the USGS's primary data sources at the state level over decades of data collection, highlighting the importance of reanalysis to yield spatiotemporally consistent and intercomparable estimates of water use.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Event‐Based Training in Label‐Limited Regimes

Abstract The distribution of attributes assigned using data on independent sensors for a specific source, for example, magnitude, can be richly descriptive for final event characterization and associated uncertainty. Attribute distributions can also provide powerful context for event characterization in the absence of comprehensive annotation. This work develops a way to leverage distributional information across a set of sensors in the absence of comprehensive annotation as a domain‐informed regularization term applied during gradient‐based learning. The regularization term is the basis of event‐based training which I show can be a powerful semi‐supervised learning (SSL) approach. I first use a simple feed forward neural network and a toy data set to outline how data set structure interacts with the assumptions inherent to many semi‐supervised learning approaches. I then demonstrate the effectiveness of event‐based training using a deep convolutional neural network for seismic event classification in Utah, which increases SSL accuracy from 92% to 97% on event classification with a limited number of training labels.

Linville, Lisa M.↗

DOE OSTI2024

Characterizing Mesoscale Cellular Convection in Marine Cold Air Outbreaks With a Machine Learning Approach

Abstract During marine cold‐air outbreaks (MCAOs), when cold polar air moves over warmer ocean, a well‐recognized cloud pattern develops, with open or closed mesoscale cellular convection (MCC) at larger fetch over open water. The Cold‐Air Outbreaks in the Marine Boundary Layer Experiment provided a comprehensive set of ground‐based in situ and remote sensing observations of MCAOs at a coastal location in northern Norway. MCAO periods that unambiguously exhibit open or closed MCC are determined. Individual cells observed with a profiling Ka‐band radar are identified using a watershed segmentation method. Using self‐organizing maps (SOMs), these cells are then objectively classified based on the variability in their vertical structure. The SOM nodes contain some information about the location of the cell transect relative to the center of the MCC. This adds classification noise, requiring numerous cell transects to isolate cell dynamical information. The SOM‐based classification shows that comparatively intense convection occurs only in open MCC. This convection undergoes an apparent lifecycle. Developing cells are associated with stronger updrafts, large spectrum width, larger amounts of liquid water, lower surface precipitation rates, and lower cloud tops than mature and weakening cells. The weakening of these cells is associated with the development of precipitation‐induced cold pools. The SOM classification also reveals less intense convection, with a similar lifecycle. More stratiform vertical cloud structures with weak vertical motions are common during closed MCC periods and are separated into precipitating and non‐precipitating stratiform cores. Convection is observed only occasionally in the closed MCC environment.

Meteorology & Atmospheric Sciences↗

DOE OSTI2026

Modeling Large Dust Aerosols in the Community Earth System Model Version 2 (CESM2)

Dust aerosols have a wide size distribution from less than 0.1 to over 100 μm and dominate Earth's atmospheric aerosol mass. However, most Earth system models (ESMs) inadequately represent dust aerosols larger than 10 μm in diameter, limiting the accuracy of the simulated dust cycle and climate impacts. Here, we introduce a new modeling framework that captures the full observed size distribution of dust aerosols, incorporating recent advances into a mineral-resolved version of the Community ESM, while addressing known issues in previous versions. Comprehensive evaluation against diverse observations of bulk dust and component minerals demonstrates that the model reproduces the observed dust cycle across particle sizes. Incorporating the previously unrepresented large-dust fractions substantially alters dust budget estimates, highlighting potential changes in simulated climate impacts and underscoring the importance of comprehensive size-resolved dust modeling. Despite these advancements, uncertainties persist. Our results indicate that a size-dependent reduction in settling velocity is required to reproduce the observed dust size distribution downwind of source regions. Specifically, in the new model, the gravitational settling velocity of dust particles larger than 10 μm in diameter must be reduced by as much as 85% to achieve agreement with observations. This empirical reduction serves as a constraint on physics-based models of dust settling. Future developments should address misrepresented physical processes that hinder accurate modeling of the large dust aerosol transport. Expanding observational data sets covering the full-size distribution is also essential to better constrain the dust cycle and improve the representation of dust optical properties and climate effects.

Li, Longlei [Cornell Univ., Ithaca, NY (United Sta↗

DOE OSTI2014

Comparative analysis of the transcriptome across distant species

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features[1,2,3,4,5,6]. Specifically, we discovered co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we used expression patterns to align the stages in worm and fly development, finding a novel pairing between worm embryo and fly pupae, in addition to the expected embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we found that the extent of non-canonical, non-coding transcription is similar in each organism, per base-pair. Finally, we found in all three organisms the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a “universal model,” based on a single set of organism-independent parameters. Our comparison used the ENCODE-modENCODE RNA resource (Fig. ED1). This resource comprises: (1) deeply sequenced RNA-Seq data from many distinct samples from all three organisms; (2) comprehensive annotation of transcribed elements and (3) uniformly processed, standardized analysis files, focusing on non-coding transcription and expression patterns. Where practical, these datasets match comparable samples across organisms and to other types of functional genomics data. In total, the resource contains 575 different experiments containing >67B sequence reads. It encompasses many different RNA types, including poly(A)+, poly(A)- and ribosomal-RNA-depleted RNA and short and long RNA.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Genome-wide compendium and functional assessment of in vivo heart enhancers

Whole-genome sequencing is identifying growing numbers of non-coding variants in human disease studies, but the lack of accurate functional annotations prevents their interpretation. We describe the genome-wide landscape of distant-acting enhancers active in the developing and adult human heart, an organ whose impairment is a predominant cause of mortality and morbidity. Using integrative analysis of > 35 epigenomic data sets from mouse and human pre-and postnatal hearts we created a comprehensive reference of > 80,000 putative human heart enhancers. To illustrate the importance of enhancers in the regulation of genes involved in heart disease, we deleted the mouse orthologs of two human enhancers near cardiac myosin genes. In both cases, we observe in vivo expression changes and cardiac phenotypes consistent with human heart disease. Our study provides a comprehensive catalogue of human heart enhancers for use in clinical whole-genome sequencing studies and highlights the importance of enhancers for cardiac function.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

A parts list for fungal cellulosomes revealed by comparative genomics

Here, cellulosomes are large, multiprotein complexes that tether plant biomass-degrading enzymes together for improved hydrolysis. These complexes were first described in anaerobic bacteria, where species-specific dockerin domains mediate the assembly of enzymes onto cohesin motifs interspersed within protein scaffolds. The versatile protein assembly mechanism conferred by the bacterial cohesin–dockerin interaction is now a standard design principle for synthetic biology. For decades, analogous structures have been reported in anaerobic fungi, which are known to assemble by sequence-divergent non-catalytic dockerin domains (NCDDs). However, the components, modular assembly mechanism and functional role of fungal cellulosomes remain unknown. Here, we describe a comprehensive set of proteins critical to fungal cellulosome assembly, including conserved scaffolding proteins unique to the Neocallimastigomycota. High-quality genomes of the anaerobic fungi Anaeromyces robustus , Neocallimastix californiae and Piromyces finnis were assembled with long-read, single-molecule technology. Genomic analysis coupled with proteomic validation revealed an average of 312 NCDD-containing proteins per fungal strain, which were overwhelmingly carbohydrate active enzymes (CAZymes), with 95 large fungal scaffoldins identified across four genera that bind to NCDDs. Fungal dockerin and scaffoldin domains have no similarity to their bacterial counterparts, yet several catalytic domains originated via horizontal gene transfer with gut bacteria. However, the biocatalytic activity of anaerobic fungal cellulosomes is expanded by the inclusion of GH3, GH6 and GH45 enzymes. These findings suggest that the fungal cellulosome is an evolutionarily chimaeric structure—an independently evolved fungal complex that co-opted useful activities from bacterial neighbours within the gut microbiome.

09 BIOMASS FUELS↗

DOE OSTI2021

Impacts of long-term temperature change and variability on electricity investments

Long-term temperature change and variability are expected to have significant impacts on future electric capacity and investments. This study improves upon past studies by accounting for hourly and monthly dynamics of electricity use, long-term socioeconomic drivers, and interactions of the electric sector with rest of the economy for a comprehensive analysis of temperature change impacts on cooling and heating services and their corresponding impact on electric capacity and investments. Using the United States as an example, here we show that under a scenario consistent with a socioeconomic pathway 2 (SSP2) and representative concentration pathway 8.5 (RCP 8.5), mean temperature changes drive increases in annual electricity demands by 0.5-8% across states in 2100. But more importantly, peak temperature changes drive increases in capital investments by 3-22%. Moreover, temperature-induced capital investments are highly sensitive to both long-term socioeconomic assumptions and spatial heterogeneity of fuel prices and capital stock characteristics, which underscores the importance of a comprehensive approach to inform long-term electric sector planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

A catalog of the diversity and ubiquity of bacterial microcompartments

Abstract Bacterial microcompartments (BMCs) are organelles that segregate segments of metabolic pathways which are incompatible with surrounding metabolism. BMCs consist of a selectively permeable shell, composed of three types of structurally conserved proteins, together with sequestered enzymes that vary among functionally distinct BMCs. Genes encoding shell proteins are typically clustered with those for the encapsulated enzymes. Here, we report that the number of identifiable BMC loci has increased twenty-fold since the last comprehensive census of 2014, and the number of distinct BMC types has doubled. The new BMC types expand the range of compartmentalized catalysis and suggest that there is more BMC biochemistry yet to be discovered. Our comprehensive catalog of BMCs provides a framework for their identification, correlation with bacterial niche adaptation, experimental characterization, and development of BMC-based nanoarchitectures for biomedical and bioengineering applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗