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DOE OSTI2024

Tools for genetic engineering and gene expression control in Novosphingobium aromaticivorans and Rhodobacter sphaeroides

ABSTRACT Alphaproteobacteria have a variety of cellular and metabolic features that provide important insights into biological systems and enable biotechnologies. For example, some species are capable of converting plant biomass into valuable biofuels and bioproducts that have the potential to contribute to the sustainable bioeconomy. Among the Alphaproteobacteria, Novosphingobium aromaticivorans , Rhodobacter sphaeroides , and Zymomonas mobilis show promise as organisms that can be engineered to convert extracted plant lignin or sugars into bioproducts and biofuels. Genetic manipulation of these bacteria is needed to introduce engineered pathways and modulate expression of native genes with the goal of enhancing bioproduct output. Although recent work has expanded the genetic toolkit for Z. mobilis , N. aromaticivorans and R. sphaeroides still need facile, reliable approaches to deliver genetic payloads to the genome and to control gene expression. Here, we expand the platform of genetic tools for N. aromaticivorans and R. sphaeroides to address these issues. We demonstrate that Tn 7 transposition is an effective approach for introducing engineered DNA into the chromosome of N. aromaticivorans and R. sphaeroides . We screen a synthetic promoter library to identify isopropyl β-D-1-thiogalactopyranoside-inducible promoters with regulated activity in both organisms (up to ~15-fold induction in N. aromaticivorans and ~5-fold induction in R. sphaeroides ). Combining Tn 7 integration with promoters from our library, we establish CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) interference systems for N. aromaticivorans and R. sphaeroides (up to ~10-fold knockdown in N. aromaticivorans and R. sphaeroides ) that can target essential genes and modulate engineered pathways. We anticipate that these systems will greatly facilitate both genetic engineering and gene function discovery efforts in these species and other Alphaproteobacteria. IMPORTANCE It is important to increase our understanding of the microbial world to improve health, agriculture, the environment, and biotechnology. For example, building a sustainable bioeconomy depends on the efficient conversion of plant material to valuable biofuels and bioproducts by microbes. One limitation in this conversion process is that microbes with otherwise promising properties for conversion are challenging to genetically engineer. Here we report genetic tools for Novosphingobium aromaticivorans and Rhodobacter sphaeroides that add to the burgeoning set of tools available for genome engineering and gene expression in Alphaproteobacteria. Our approaches allow straightforward insertion of engineered pathways into the N. aromaticivorans or R. sphaeroides genome and control of gene expression by inducing genes with synthetic promoters or repressing genes using CRISPR interference. These tools can be used in future work to gain additional insight into these and other Alphaproteobacteria and to aid in optimizing yield of biofuels and bioproducts.

Hall, Ashley N.↗

DOE OSTI2024

Detection and Quantitation of Hydrogen Emissions Role and Status of Detection Technology

Hydrogen is a critical strategy to decarbonize energy and manufacturing industries. Hydrogen is nontoxic and can be handled safely, but potential for secondary greenhouse impacts. Hydrogen releases arise from a variety of mechanisms (process, design features, "leaks") that contribute to total hydrogen releases. Detection methodologies will be critical to detect and quantify hydrogen emissions. Detection is to be integrated with advanced analytics (AI) and behavior modelling to effectively identify, quantify, and source locate hydrogen releases. Modelling of emissions will contribute to facility safety and reliability. DOE is committed to develop the tools to model and mitigate the impact of hydrogen releases which include: Support modeling to elucidate released hydrogen degradation; Support the development of tools for emissions quantitation; and Support engineering advancements to minimize hydrogen losses along the value chain (including process, design features, and leaks).

detection↗

DOE OSTI2026

Dual Context: Leveraging Structured Application Context for Code Generation and Runtime Feature Activation via Chat Interfaces

Integrating artificial intelligence (AI) capabilities into software applications typically involves two common paths. For developers, AI assists in generating and documenting source code and other related software engineering efforts. For users, AI assists them through question-and-answer exchanges via chatbots. Both approaches have their value, but neither effectively leverages the modularity of component-based architectures that modern web application frameworks offer. We implement a proof of concept within a centralized suite of applications used for the Atmospheric Radiation Measurement (ARM) Data Center Operational Tools, where we introduce a third integration path through the ARM Context Engine (ACE). ACE is a context driven system that uses structured contextual specifications to enable Large Language Models (LLMs) to render interactive and feature-rich user interface (UI) components directly within chat responses, alongside or in place of conventional text outputs. These specifications serve two important purposes across what we call code context and UI context. Code context provides AI-assisted development tools with structured application knowledge beyond raw code, including component relationships, architectural patterns and schematic information, enabling the generation of consistent, well-structured code. UI context defines the rules for enabling and rendering component features at runtime based on the user's natural language input, allowing end users to activate capabilities such as data export, filtering, and pagination within chat responses, without requiring code changes or redeployment. We demonstrate, through a comparative evaluation against general-purpose AI chatbots, that context-driven component rendering provides interactive capabilities that text-based responses cannot replicate, including deterministic component behavior, application-consistent design language, and on-demand feature activation. A development effort comparison further shows that features that traditionally require multi-step development cycles can be activated with a single naturallanguage request. In this ongoing work, we present ACE as an emerging approach to AI integration that positions modular, well-documented software architecture as the foundation for AI-ready applications. ACE treats context as a shared resource across both development and user-facing AI, bringing cohesion to conventionally disconnected efforts, bridging developer tooling and end-user capabilities within a single framework.

Tadimeti, Vijay [ORNL]↗

DOE OSTI2024

Synthesizing realistic sand assemblies with denoising diffusion in latent space

Abstract The shapes and morphological features of grains in sand assemblies have far‐reaching implications in many engineering applications, such as geotechnical engineering, computer animations, petroleum engineering, and concentrated solar power. Yet, our understanding of the influence of grain geometries on macroscopic response is often only qualitative, due to the limited availability of high‐quality 3D grain geometry data. In this paper, we introduce a denoising diffusion algorithm that uses a set of point clouds collected from the surface of individual sand grains to generate grains in the latent space. By employing a point cloud autoencoder, the three‐dimensional point cloud structures of sand grains are first encoded into a lower‐dimensional latent space. A generative denoising diffusion probabilistic model is trained to produce synthetic sand that maximizes the log‐likelihood of the generated samples belonging to the original data distribution measured by a Kullback‐Leibler divergence. Numerical experiments suggest that the proposed method is capable of generating realistic grains with morphology, shapes and sizes consistent with the training data inferred from an F50 sand database. We then use a rigid contact dynamic simulator to pour the synthetic sand in a confined volume to form granular assemblies in a static equilibrium state with targeted distribution properties. To ensure third‐party validation, 50,000 synthetic sand grains and the 1542 real synchrotron microcomputed tomography (SMT) scans of the F50 sand, as well as the granular assemblies composed of synthetic sand grains are made available in an open‐source repository.

Vlassis, Nikolaos N.↗

DOE OSTI2026

Tuning Anisotropic Optical Properties of Inorganic and Hybrid Organic–Inorganic MXenes via Topochemical Surface Modification

Surface groups are central to the properties of MXenes, yet their role in optical anisotropy remains largely unexplored. Here, we use a topochemical route to synthesize single crystals of stacked Ti 3 C 2 Cl 2 and hybrid organic–inorganic MXenes (h-MXenes) with lateral sizes of 38–75 μm, rotational registry, and tunable interlayer spacing. Solid-state NMR spectroscopy shows that topochemical substitution generates mixed amido, imido, and hydride surface motifs, which modify the electronic structure of the Ti 3 C 2 inorganic core. Imaging spectroscopic ellipsometry with micron-scale spatial resolution enables reconstruction of the complex dielectric tensor of individual multilayer crystals. Ti 3 C 2 Cl 2 exhibits a type-II hyperbolicity above 930 nm, whereas h-MXenes do not display hyperbolicity within the measured 300–1700 nm window, instead showing reduced in-plane conductivity, suppressed out-of-plane light absorption, and a chain-length-dependent blue shift of a near-infrared absorption feature. These results demonstrate topochemical surface modification as a direct handle for engineering MXenes as surface-programmable optical media.

Hybrid materials↗

DOE OSTI2026

Strain mapping of three-dimensionally structured two-dimensional materials

Strain plays a crucial role in tuning materials’ properties, influencing their optical, electrical, and chemical performances. In two-dimensional (2D) materials, applied stress often induces out-of-plane deformation, resulting in a more intricate three-dimensional (3D) topography, where mapping the strain remains a challenge due to the limitations of conventional characterization techniques. In this work, we introduce BRIGHT (Bragg-Rod Informed, Gradient-based Height-mapping Technique), an integrated method for reconstructing both the topography and planar strain profile of 3D-structured 2D materials using nanobeam four-dimensional scanning transmission electron microscopy (4D-STEM). We apply BRIGHT to a MoS2-MoSe2 transition metal dichalcogenide (TMD) lateral heterojunctions exhibiting built-in strain and out-of-plane ripples and show that varying heterojunction widths lead to distinct surface morphologies and corresponding changes in the planar strain distribution. These results establish a foundation for more effective strain engineering in 2D materials by accounting for out-of-plane structural features, thereby enabling more precise control of strain-dependent properties.

Mireles, Adan [Rice Univ., Houston, TX (United Sta↗

DOE OSTI2025

Recent advances in rational design of defect-engineered photocatalysts toward sustainable NH 3 synthesis as H 2 carrier: From fundamental and development to machine-learning

In this study, we provide a detailed overview of the fundamental mechanisms underpinning photocatalytic N 2 reduction. We also discuss advances in catalyst design for the synthesis of NH 3 . Particular emphasis is placed on the role of surface defect engineering, which includes the creation of surface defects to enhance the performance of semiconducting photocatalysts for efficient N 2 reduction. In addition, the application of a machine learning-based computational modeling approach is discussed as an important driving force for predicting and regulating NH 3 synthesis efficiency based on catalyst features and reaction conditions. Finally, existing challenges and future perspectives for improving the performance of defect-engineered photocatalysts are outlined to contribute to the ongoing discourse on sustainable ammonia generation. This review aims to clarify recent progress in the rational design of defect-containing photocatalysts for the synthesis of NH 3 and encourages innovative approaches to catalyst optimization rather than solely focusing on new materials.

08 HYDROGEN↗

DOE OSTI2024

The putative center in NGC 1052

Context. Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood.Aims. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052.Methods. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. Our study is focused on the size and continuum spectrum of the innermost region containing the central engine and the footpoints of both jets. We employed a synchrotron-self absorption model to fit the continuum radio spectrum and we combined the size measurements from close to the central engine out to ∼1 pc to study the jet collimation.Results. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 43 μas perpendicular to the jet axes, corresponding to just around 250 RS (Schwarzschild radii). This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 3 × 103 RS and 1 × 104 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 μas from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at ∼130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 2.6 × 104 Gauss, which is consistent with previous measurements.Conclusions. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength.Key words: methods: observational / techniques: high angular resolution / techniques: interferometric / galaxies: active / galaxies: jets / galaxies: Seyfert⋆⋆ NASA Hubble Fellowship Program, Einstein Fellow.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Human Factors Considerations for Hydrogen Rail Refueling and Maintenance

The Federal Railroad Administration (FRA) contracted Sandia National Laboratories to review the risks introduced specifically through human interactions within hydrogen rail refueling and maintenance activities and present recommendations for future system development and implementation. Due to the lack of domestic hydrogen-powered trains at the time of review, observational data was collected on analogous existing systems to consider various ways hydrogen might be implemented within future designs. The team developed 50 human factors design guidelines through analysis of human risk factors. Because hydrogen rail systems are still in the early design stage, the resulting human factors guidelines represent general best practices and do not include specific design features. The research team recommends design and regulatory agencies include a Human Factors Engineer in the design and review process to ensure the guidelines are appropriately implemented and verified.

08 HYDROGEN↗

DOE OSTI2025

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) Software for Depletion and Fuel Management

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) software is being developed in the Research and Test Reactor (RTR) Program at Argonne National Laboratory to meet the reactor design and analysis needs of the Conversion Program. ADDER is a flexible tool that (1) provides a depletion capability through coupling external neutronics codes with a built-in CRAM solver or external depletion code and (2) provides a user-friendly interface to perform fuel management and criticality search operations. The ADDER software is a Python 3 application written using modern software development practices subject to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance standards. This report is the user guide for the software release referred to as ADDER v1.1.0. The motivation for a software to have flexible capabilities that ADDER possesses is the need to support a wide variety of geometries that are commonly required in analysis of research and test reactors. These reactors can have complex fuel, experiment, or control material shuffling patterns that persist over several years with many fuel management and partial refueling intervals. The scale of fuel management analysis can require tracking of an inventory that is multiple times the core loading. Many reactors, both power and non-power reactors of various types, will find the features of ADDER useful to facilitate key tasks that a fuel or core design engineer must perform with the convenience of concise input and validated functionality.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) Software for Depletion and Fuel Management

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Physics-Informed Machine Learning Model for Ceramic Matrix Composite Creep

A physics-informed recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear, time-dependent constitutive behavior of ceramic matrix composites (CMCs) driven by matrix damage and constituent creep at the microscale. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the high-fidelity generalized method of cells (HFGMC) approach which calls appropriate creep and damage models for each of the constituents. This coupling permits simulating the nonlinear behavior of CMCs based on constituent response at the microscale along with microstructural features such as fiber and porosity volume fraction and fiber radius. The microscale repeating unit cell is loaded under creep fatigue conditions to replicate the material loading experienced in a turbine engine. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input stress sequence, temperature, and microstructural features, the resulting strain history response while satisfying physical constraints related to creep rate, isochoric inelastic deformation, and strain energy density. The trained surrogate model is shown to effectively match the strain history over quantified distributions of microstructural features and relevant loading regimes and temperatures. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore, the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex microstructures, nonlinear time-dependent material response, and under non-monotonic loading conditions.

ceramic matrix composites↗

DOE OSTI2026

Machine learning insights into microstructural origins of transport and mechanical properties in porous microstructures

Multifunctional porous materials are increasingly needed across various fields, but their complex microstructures create significant challenges due to the intricate microstructure-property relationships. This complexity, combined with limitations of traditional analysis methods, hinders efforts to understand and optimize microstructure–property relationships. Here, to address this, we integrate physics-based mesoscale modeling with interpretable machine learning (ML) to uncover how microstructural features govern effective diffusivity and elastic modulus. At constant porosity, we show diffusivity varies by over 150 × and modulus by ∼50 ×, highlighting the power of microstructure engineering. Statistical analysis reveals bimodal behavior in diffusivity and unimodal in modulus. ML identifies connectivity as the dominant factor, while modulus is also sensitive to domain size and feature interactions. Controlled simulations further highlight domain shape as a critical feature for modulus. This framework enables efficient exploration of microstructure-property correlations, offering new insights to guide the design of advanced porous materials.

Bicontinuous microstructure↗

DOE OSTI2024

Dynamic Bubbling Balanced Proactive CO 2 Capture and Reduction on a Triple-Phase Interface Nanoporous Electrocatalyst

The formation and preservation of the active phase of the catalysts at the triple-phase interface during CO 2 capture and reduction is essential for improving the conversion efficiency of CO 2 electroreduction toward value-added chemicals and fuels under operational conditions. Designing such ideal catalysts that can mitigate parasitic hydrogen generation and prevent active phase degradation during the CO 2 reduction reaction (CO 2 RR), however, remains a significant challenge. Herein, we developed an interfacial engineering strategy to build a new SnO x catalyst by invoking multiscale approaches. This catalyst features a hierarchically nanoporous structure coated with an organic F-monolayer that modifies the triple-phase interface in aqueous electrolytes, substantially reducing competing hydrogen generation (less than 5%) and enhancing CO 2 RR selectivity (~90%). This rationally designed triple-phase interface overcomes the issue of limited CO 2 solubility in aqueous electrolytes via proactive CO 2 capture and reduction. Concurrently, we utilized pulsed square-wave potentials to dynamically recover the active phase for the CO 2 RR to regulate the production of C1 products such as formate and carbon monoxide (CO). This protocol ensures profoundly enhanced CO 2 RR selectivity (~90%) compared with constant potential (~70%) applied at -0.8 V (V vs RHE). We further achieved a mechanistic understanding of the CO 2 capture and reduction processes under pulsed square-wave potentials via in situ Raman spectroscopy, thereby observing the potential-dependent intensity of Raman vibrational modes of the active phase and CO 2 RR intermediates. Finally, this work will inspire material design strategies by leveraging triple-phase interface engineering for emerging electrochemical processes, as technology moves toward electrification and decarbonization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Power-Law Entanglement and Hilbert Space Fragmentation in Nonreciprocal Quantum Circuits

Quantum circuits utilizing measurement to evolve a quantum wave function offer a new and rich playground to engineer unconventional entanglement dynamics. Here, in this work, we introduce a hybrid, nonreciprocal setup featuring a quantum circuit, whose updates are conditioned on the state of a classical dynamical agent. In our example the circuit is represented by a Majorana quantum chain controlled by a classical N-state Potts chain undergoing pair flips. The local orientation of the classical spins controls whether randomly drawn local measurements on the quantum chain are allowed or not. This imposes a dynamical kinetic constraint on the entanglement growth, described by the transfer matrix of an N-colored loop model. It yields an equivalent description of the circuit by an SU(N)-symmetric Temperley-Lieb Hamiltonian or by a kinetically constrained surface growth model for an N-component height field. For N = 2, we find a diffusive growth of the half-chain entanglement toward a stationary profile S(L) ~ L 1/2 for L sites. For N ≥ 3, the kinetic constraints impose Hilbert space fragmentation, yielding subdiffusive growth toward S(L) ~ L 0.57 . This showcases how the control by a classical dynamical agent can enrich the entanglement dynamics in quantum circuits, paving a route toward novel entanglement dynamics in nonreciprocal hybrid circuit architectures.

1-dimensional spin chains↗

DOE OSTI1992

Nuclear Safety [Vol. 33, No. 4, October-December 1992]

Nuclear Safety is a review journal that covers significant developments in the field of nuclear safety. Its scope includes the analysis and control of hazards associated with nuclear energy, operations involving fissionable materials, and the products of nuclear fission and their effects on the environment. Primary emphasis is on safety in reactor design, construction, and operation; however, the safety aspects of the entire fuel cycle, including fuel fabrication, spent-fuel processing, nuclear waste disposal, handling of radioisotopes, and environmental effects of these operations, are also treated. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 499 Technical Note: Nuclear Regulatory Commission’s Examination Question Bank System, C. L Jensen and N. L. Skinner; 506 Report on the Specialists’ Meeting on Passive and Active Safety Features of Liquid-Metal Fast Breeder Reactors Organized by the International Atomic Energy Agency at Oarai Engineering Centre of Power Reactor and Nuclear Development Corporation, Japan, November 5-7,1991, S. R. Paranjpe; ACCIDENT ANALYSIS: 514 A Review of Hydrogen Production During Melt/Water Interaction in LWRs, D. F. Fletcher, B. D. Turland, and S. P. A. Lawrence; 535 A Review of the U 3 0 8 -Al Reaction as a Potential Heat Source in Research and Test Reactor Accidents, J. L. Snelgrove; CONTROL AND INSTRUMENTATION: 543 Multilevel Interfaces for Power Plant Control Rooms II: A Preliminary Design Space, K. J. Vicente; DESIGN FEATURES: 549 Twenty-Second DOE/NRC Nuclear Air Cleaning and Treatment Conference, R. R. Bellamy, D. W. Moeller, and M. W. First; 562 Book Review: Safety Assessment of Unit 5 (WWER-440/W-213) of the Greifswald Nuclear Power Station, M. H. Fontana; WASTE AND SPENT FUEL MANAGEMENT: 576 Activities Related to Waste and Spent Fuel Management, M. D. Muhlheim and E. G. Silver; OPERATING EXPERIENCES: 586 Analysis of Corrective Actions Applied to Nuclear Power Plant Operations, A. E. Cross; 593 Reactor Shutdown Experience, Compiled by J. W. Cletcher; 596 Operating U.S. Power Reactors, Compiled by M. D. Muhlheim and E. G. Silver; 609 Non-Commercial Power Operating Experience; 614 World Nuclear Performance, Compiled by M. D. Muhlheim; RECENT DEVELOPMENTS: 623 General Administrative Activities, Compiled by M. D. Muhlheim and E. G. Silver; 635 Reports, Standards, and Safety Guides, D. S. Queener; 639 Proposed Rule Changes as of June 30, 1992; ANNOUNCEMENTS: 513 Fourth International Conference on Nuclear Fuel Reprocessing and Waste Management RECOD '94; 585 International Topical Meeting on Advanced Reactors Safety; 622 International Workshop on Implementation of ALARA at Nuclear Power Plants; 648 Short Course on Nuclear Criticality Safety; 648 8th Topical Meeting on Problems in Reactor Physics MEPhi-VOLGA-93; 643 The Authors; 646 Reviewers of Nuclear Safety, Vol. 33

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Overview and Status of the Long-Baseline Neutrino Facility Far Detectors Cryogenics System

The Sanford Underground Research Facility (SURF) will host the Far Detectors of the Deep Underground Neutrino Experiment (DUNE), an international multi-kiloton Long-Baseline neutrino experiment that will be installed about a mile underground in Lead, SD. Detectors will be located inside four cryostats filled with almost 70,000 metric tons of ultrapure liquid argon, with a level of impurities lower than 100 parts per trillion of oxygen equivalent. The cryogenics infrastructure supporting this experiment is provided by the Long- Baseline Neutrino Facility (LBNF). This contribution presents modes of operation, layout and main features of the LBNF Far Detectors cryogenics system, which is composed of the following subsystems: argon receiving facilities, nitrogen system, argon distribution system, argon purification and regeneration systems, argon circulation system, argon condensers system, internal cryogenics, miscellaneous items, and process controls. An international engin eering tea m is designing these systems and will manufacture, install, test, commission, and qualify them. This contribution describes the main features, performance, functional requirements, and modes of operation of the LBNF Far Detectors cryogenics system. It also presents the status of the design, along with present and future needs to support the DUNE experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Final Technical Report for DE-SC0022206

This project developed foundational genetic, genomic, and epigenetic tools for anaerobic fungi (Neocallimastigomycota), a group of microorganisms with exceptional natural abilities to deconstruct lignocellulosic biomass. Efficient biomass deconstruction remains a major barrier to economical production of renewable fuels, chemicals, and materials from agricultural and forestry residues. The project sought to enable mechanistic studies and future engineering of anaerobic fungi by improving genomic resources, establishing methods for gene expression, and investigating epigenetic regulation of biomass-degrading pathways. Major accomplishments included generation of the first chromosome-scale genome assemblies for multiple anaerobic fungal species, providing publicly available genomic resources that support both engineering and fundamental biological research. The project established the first reproducible system for heterologous gene expression in anaerobic fungi and identified genomic features and mobile genetic elements that may support future development of stable transformation technologies. In parallel, the project demonstrated direct conversion of untreated lignocellulosic biomass into fuels and specialty chemicals through a fungal-yeast bioprocess and identified anaerobic fungal enzymes with utility for metabolic engineering. The research also revealed that epigenetic regulation plays an important role in controlling fungal gene expression and enzyme production, identifying potential strategies for enhancing biomass degradation. Collectively, this work established anaerobic fungi as a tractable emerging platform for bioenergy and biomanufacturing research, generated valuable public resources, trained the next generation of researchers, and advanced DOE-BER goals related to predictive biology, sustainable bioprocessing, and the circular bioeconomy.

Solomon, Kevin [University of Delaware] (ORCID:000↗