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Structural features of 11 synthetic bacteriochlorins — comparisons with chlorins and porphyrins

As part of a program to develop the fundamental science of synthetic bacteriochlorins — analogues of Nature’s light-harvesting pigments in anoxygenic photosynthesis — 11 synthetic free base bacteriochlorins have been examined by single-crystal X-ray diffraction analysis. In each bacteriochlorin, each pyrroline ring contains a gem-dimethyl group to block adventitious (aerobic) dehydrogenation. The structural features of the bacteriochlorins (tetrahydroporphyrins) are compared with the less saturated counterparts, chlorins (dihydroporphyrins) and porphyrins. Only slight structural perturbation accrues due to the presence of the gem-dimethyl groups. Furthermore, the results presented herein may aid in understanding metal chelation processes and underpin the development of functional solid-state molecular assemblies.

X-ray diffraction↗

General Entity Model

General Entity Model (GEM) is an extensible, upper-level ontology. An ontology is simply a collection of concepts and their relationships and properties and can be used as a schema or data model to describe some domain. As an upper-level ontology, GEM is intended to be used as a foundation for building domain-specific ontologies (such as energy, manufacturing, etc).

Browning, JerenM↗

BESTEST-GSR (Building Energy Simulation Test - Generation Simulation and Reporting) 2023 [SWR 18-23]

Building Energy Simulation Test (BESTEST) is an NREL-developed method to validate the qualitative performance of different whole building simulations engines relative to each other. https://www.nrel.gov/docs/legosti/old/6231.pdf The purpose of this repository is to generate BESTEST test case models, run simulations, and populate data for ASHRAE Standard 140 reporting spreadsheets for EnergyPlus® based whole building simulation tools. It was originally setup for 2014 version of Standard 140. In May of 2022 it was updated to the 2020 version of Standard 140. This update included updates and additions to existing test suites, the bulk of which was in Section 5.2 (Building Thermal Envelope and Fabric Load Tests). We did not add Section 5.5 Airside HVAC Equipment Performance, but we hope to add that later in 2022. At some point we also hope to add Section 5.2.4 ground modeling, which is currently excluded. Supported Tools The default IDF generation is based on the OpenStudio® CLI, but the workflow supports a 'Bring your own IDF' use case. Additionally, for non-EnergyPlus® based tools the post processing scripts can be used if simulation results are provided as a CSV file.The scripts on this repository should work on Mac, Windows, and Linux. Dependencies Install OpenStudio® 3.4.0 make sure command line can recognize the 'openstudio' command This includes EnergyPlus® 22.1 Install Ruby on your system if it isn't already setup. 2.7 is used for development but other versions may work Since OpenStudio has its own embedded Ruby, which is used for running measures, you don't necessarily have to use a version of Ruby supported by OpenStudio. Install RubyXL Ruby gem This is used to modify Microsoft Excel spreadsheets Install Parallel Ruby gem This allows the CLI to run simulations in parallel

Goldwasser, David↗

pnnl-predictive-phenomics/csc052-gem

Genome-Scale Metabolic Model Continuous Validation with Memote for CarbStor Community Member Bacillus These repositories contain the continuous validation environment for an organism-specific genome-scale metabolic model (GEM) using Memote. Memote is a software tool that provides a suite of tests to ensure the quality and consistency of metabolic models. By integrating Memote into a continuous integration (CI) workflow, we can automatically validate updates to the GEM, ensuring that model modifications improve or maintain the model's integrity.

Torres, Victor E.↗

pnnl-predictive-phenomics/csc031-gem

Genome-Scale Metabolic Model of CarbStor Community member Microbacterium (csc031) Continuous Validation with Memote These repositories contain the continuous validation environment for an organism-specific genome-scale metabolic model (GEM) using Memote. Memote is a software tool that provides a suite of tests to ensure the quality and consistency of metabolic models. By integrating Memote into a continuous integration (CI) workflow, we can automatically validate updates to the GEM, ensuring that model modifications improve or maintain the model's integrity

McNaughton, Andrew [@PNNL]↗

pnnl-predictive-phenomics/csc009-gem

Genome-Scale Metabolic Model of CarbStore Community member Curtobacterium (csc009) Continuous Validation with Memote These repositories contain the continuous validation environment for an organism-specific genome-scale metabolic model (GEM) using Memote. Memote is a software tool that provides a suite of tests to ensure the quality and consistency of metabolic models. By integrating Memote into a continuous integration (CI) workflow, we can automatically validate updates to the GEM, ensuring that model modifications improve or maintain the model's integrity

Lin, Tesia↗

pnnl-predictive-phenomics/csc040-gem

Genome-Scale Metabolic Model Continuous Validation with Memote for CarbStor Community Member Rhodococcus These repositories contain the continuous validation environment for an organism-specific genome-scale metabolic model (GEM) using Memote. Memote is a software tool that provides a suite of tests to ensure the quality and consistency of metabolic models. By integrating Memote into a continuous integration (CI) workflow, we can automatically validate updates to the GEM, ensuring that model modifications improve or maintain the model's integrity.

McNaughton, Andrew [@PNNL]↗

pnnl-predictive-phenomics/csc043-gem

Genome-Scale Metabolic Model Continuous Validation with Memote for CarbStor Community Member Paenibacillus These repositories contain the continuous validation environment for an organism-specific genome-scale metabolic model (GEM) using Memote. Memote is a software tool that provides a suite of tests to ensure the quality and consistency of metabolic models. By integrating Memote into a continuous integration (CI) workflow, we can automatically validate updates to the GEM, ensuring that model modifications improve or maintain the model's integrity.

Zucker, Jeremy [Pacific Northwest National Laborat↗

Practical low-temperature gasoline combustion for very high efficiency off-road, medium- and heavy-duty engines

Low-temperature gasoline combustion (LTGC) with additive-mixing fuel injection (AMFI) is a new combustion strategy that has been demonstrated to deliver 9%–25% better brake thermal efficiency than similar-sized market-leading diesel engines over the operating map. Moreover, the LTGC-AMFI engine shows near-zero smoke, and NOx emissions are 4–100 times lower than those of a diesel, sufficiently low that no aftertreatment, or only passive NOx aftertreatment, would be sufficient (diesel exhaust fluid is not required). LTGC-AMFI combustion is based on kinetically controlled compression ignition of a dilute charge with a variable amount of low-to-moderate fuel stratification. Fast combustion control is provided by adding minute amounts of an ignition-enhancing additive into the fuel each engine cycle to control its reactivity. This strategy was used to operate a medium-duty (MD) LTGC-AMFI engine at loads from idle to 16.3 bar BMEP and speeds from 600 to 2400 rpm with regular E10 gasoline, which covers nearly the entire operating map of a typical MD engine. Turbine-out temperatures were sufficient for an oxidation catalyst to control hydrocarbon and CO emissions. Autonomie simulations over the GEM ARB Transient and the GEM 55 mph Cruise driving cycles for class-6 trucks using this technology showed fuel economies of 8.1 and 11.4 mpg-gasoline-equivalent, respectively, corresponding to 18.6% and 13.4% improvements over a similar-size diesel engine. Engine-out NOx emissions were 0.024 and 0.01 g/bhp-h, respectively, well below current U.S. emission standards. These results show that switching from diesel to LTGC-AMFI engines would greatly reduce greenhouse gas (GHG) emissions for off-road, MD and HD applications, which will continue to rely on combustion engines because electrification is not practical in the foreseeable future. Finally, with their reduced fuel consumption, the lower cost of gasoline compared to diesel fuel, and much lower aftertreatment costs, LTGC-AMFI engines also offer a significantly lower total cost of ownership.

33 ADVANCED PROPULSION SYSTEMS↗

Acoustic Observations of the OSIRIS-REx Sample Return Capsule Re-Entry from Wendover Airport

The Origins, Spectral Interpretation, Resource Identification, and Security‐Regolith Explorer sample return capsule (SRC) re‐entered the Earth’s atmosphere at hypersonic speeds from interplanetary space on 24 September 2023. The current work reports on 18 ground‐based acoustic sensors deployed at Wendover Airport, the same location that the Genesis and Stardust SRC re‐entries were recorded. Four different sensors (Chaparral Physics, Gem, Wilson Engineering Research and Development [WERD], and RedVox) were deployed in close proximity to compare their performance. All the sensors captured an N‐wave signal associated with the SRC re‐entry shock wave followed by a broadband coda. The Chaparral Physics array served as the high‐fidelity reference measurement. The N‐wave signal had a peak‐to‐peak amplitude of 4.07 Pa with a fundamental frequency of 4.98 Hz from 167.5° measured clockwise from north, nearly perpendicular to the SRC trajectory. In addition, high coherence in the coda was shown to be associated reflections from the surrounding mountains. In general, the more economical sensors (Gem, WERD, and RedVox) produced results that were consistent with these observations and sensor specifications. Beamforming with these single sensors arranged as an array showed agreement with the high‐fidelity array to within a couple of degrees. Furthermore, the current high‐fidelity results were compared with the measurements during the Genesis and Stardust SRC re‐entries. All three entries produced a broadband fundamental peak at a frequency that was inversely related to the SRC diameter as well as evidence of reflections from the surrounding topography.

KC, Real J. [Oklahoma State University, Stillwater↗

Optimization of Energy Flow through Synthetic Metabolic Modules and Regulatory Networks in a Model Photosynthetic Eukaryotic Microbe

Photosynthetic organisms have recently gained considerable attention for a role in development of renewable energy sources. Genome-enabled systems biology methods, coupled with functional and synthetic genomics, present opportunities to develop sustainable and economical applications such as fuel production within the next 10 to 15 years. However, optimization of light-driven metabolism for biomass or biofuel production will require a detailed systems biology understanding of photosynthetic processes and cellular metabolism. Genome-scale metabolic models (GEMs) are at the core of systems analysis of cellular processes and form a common organizational framework for analyses of data resulting from functional genomics experimental work and computational studies. Therefore, there is a clear demand for high quality photosynthetic model organisms and the appropriate computational tools that enable systems analysis of light-driven metabolism. Through research conducted we expanded the currently available repertoire of photosynthetic GEMs to include the commercially valuable model diatom Phaeoctylum tricornutum. Diatoms have a peculiar and distinct evolutionary footprint and represent a major eukaryotic lineage that is taxonomically and functionally distinct from green and red algae and vascular plants. Therefore, the true potential for light-driven metabolism aimed at biofuel production remains poorly understood at a systems level for a large subset of the global diversity of photosynthetic organisms. The metabolic capabilities of P. tricornutum were comparatively modeled with those from other photosynthetic groups in order to elucidate the occurrence of metabolic traits within and between phototrophs. Additionally, this research resulted in significant extension of the COnstraints Based Reconstruction and Analysis (COBRA) Toolbox to accommodate the crucial need for infrastructure required for ‘omics data integration and analysis in the context of genome-scale models. Therefore, the proposed research achieved two important goals. First, within the broad scope of photosynthetic organisms, we functionally compared and, as a result, identified cellular processes that require optimization in order to enable deployment as biofuel feedstock. Second, the proposed research resulted in development of key computational infrastructure, which can be further extended to other biological systems, that is currently lacking but necessary for multiple ‘omics data integration.

59 BASIC BIOLOGICAL SCIENCES↗

Petronius Offshore Oil Field Case Study

This document is one of three offshore post-primary CO 2 enhanced oil recovery project case studies that compares the modeling results from an industry standard compositional simulator (“GEM”) and a variant of the FE/NETL CO 2 Prophet Model (CO 2 Prophet Model), which determined the CO 2 Prophet Model was able to reasonably represent the performance of the CO 2 flood modeled using the more sophisticated GEM. This case study uses the Petronius oil field J-2 Sand using a quarter of a five-spot pattern design (one CO 2 injector well and five production wells), for a 40-year injection scenario. A version of the CO 2 Prophet Model is available on NETL’s website under the Collection Name: FE/NETL CO 2 Prophet Model.

02 PETROLEUM↗

Cognac Offshore Oil Field Case Study

This document is one of three offshore post-primary CO 2 enhanced oil recovery project case studies that compares the modeling results from an industry standard compositional simulator (“GEM”) and a variant of the FE/NETL CO 2 Prophet Model (CO 2 Prophet Model), which determined the CO 2 Prophet Model was able to reasonably represent the performance of the CO 2 flood modeled using the more sophisticated GEM. This case study uses the Cognac oil field J Sand, in the field’s Northeast fault block, using a line drive pattern design (one CO 2 injector and two production wells), for 10-year and 20-year injection scenarios. A version of the CO 2 Prophet Model is available on NETL’s website under the Collection Name: FE/NETL CO 2 Prophet Model.

02 PETROLEUM↗

Horn Mountain Offshore Oil Field Case Study

This document is one of three offshore post-primary CO 2 enhanced oil recovery project case studies that compares the modeling results from an industry standard compositional simulator (“GEM”) and a variant of the FE/NETL CO 2 Prophet Model (CO 2 Prophet Model), which determined the CO 2 Prophet Model was able to reasonably represent the performance of the CO 2 flood modeled using the more sophisticated GEM. This case study uses the Horn Mountain oil field NFB M Sand, in the field's North fault block, using three pairs of wells. Each pair of wells is a line drive pattern design (one CO 2 injector and one production well). Four 20-year injection scenarios were assessed, varying CO 2 injector well placements. A version of the CO 2 Prophet Model is available on NETL’s website under the Collection Name: FE/NETL CO 2 Prophet Model.

02 PETROLEUM↗

Beyond Generalized Eigenvalues

Two analysis techniques, the generalized eigenvalue method (GEM) or Prony's method (PM), are commonly used to analyze statistical estimates of correlation functions produced in lattice quantum field theory calculations. GEM takes full advantage of the matrix structure of correlation functions but only considers individual pairs of time separations when much more data exists. PM can be applied to many time separations and many individual matrix elements simultaneously but does not fully exploit the matrix structure of the correlation function. We combine both these methods into a single framework based on matrix polynomials which we call block Prony method (BPM).

Fleming, George T.↗

ThunderDOEME: A Seismoacoustic Deployment at Langmuir Observatory

This report documents a two-month deployment of seismoacoustic instrumentation at the Langmuir Observatory, located outside Socorro, New Mexico. A total of eleven Fairfield Nodal Seismometers and fifteen GEM infrasound loggers were deployed from June 2 to August 5, 2025. The nodal seismometers recorded data for an average of 34 days, while the GEMs had variable operational durations throughout the deployment period. The primary objective was to capture thunder signals using seismoacoustic instrumentation, contributing to a better understanding of the acoustic and seismic phenomena associated with thunder. Coinciding with this deployment, optical and electric field sensors were present, providing information regarding the timing and location of lightning strikes in the region. Additionally, triggered lightning strikes were conducted, serving as a ground truth for validating the captured thunder signals. Preliminary analysis of the data reveals clear thunder signals in both the seismic and infrasound recordings, with peak frequencies observed across a range of 4 to 40 Hz, depending on the event.

47 OTHER INSTRUMENTATION↗

Partnership Center for High-fidelity Boundary Plasma Simulation

The University of Colorado Boulder contributed to the DOE Partnership Center for High‑fidelity Boundary Plasma Simulation (award DE‑SC0018271) by advancing gyrokinetic modeling of core–edge plasma interactions in tokamaks. Using the GEM and XGC codes, the project investigated turbulence spreading from the plasma edge to the core, finding that while edge‑driven trapped electron mode turbulence enhances core ion heat flux in DIII‑D L‑mode plasmas, it does not fully explain experimentally observed transport shortfalls, indicating a need for flux‑driven models. The team also demonstrated that edge micro‑instabilities are highly sensitive to small variations in magnetic equilibrium, particularly near the pedestal and edge, highlighting the importance of accurately characterizing equilibrium uncertainties. Significant progress was made in spatially coupling the core GEM and edge XGC simulations, with successful validation against standalone XGC results in realistic tokamak geometries, laying the groundwork for future extensions to kinetic electrons and electromagnetic effects.

42 ENGINEERING↗

Integrative Modeling and Analysis of Fungal Central Carbon Metabolism

Over a thousand fungal genomes have been sequenced, yet manually curated genome-scale metabolic models (GEMs) are available for only a limited number of species. Moreover, these models have often been developed independently, leading to inconsistencies in namespaces, compartment definitions, and pathway representations that hinder comparative analysis, the systematic reuse of prior curation efforts, and the integration of consolidated metabolic knowledge. Here, we present the Consolidated Fungal Core Metabolism Model (CFCMM), constructed by integrating thirteen published fungal models spanning Ascomycota, Mucoromycota, and both Crabtree-positive and Crabtree-negative yeasts. We harmonized metabolites and reactions into a non-redundant shared ModelSEED ontological space, standardized compartmentalization, and refined gene–protein–reaction (GPR) rules. Using pathway-level visualization and systematic gap detection, we further improved the integrated network through literature-guided curation to correct stoichiometry, stereospecificity, and pathway architecture. Orthologous protein family reconstruction and functional annotation workflows were used to validate and inform GPR associations, with particular emphasis on ambiguous enzyme superfamilies and membrane-associated components. Using the resulting CFCMM, we built high-quality central carbon core models for each fungus and performed flux balance analysis to quantify ATP-yield variation under aerobic and anaerobic conditions, explicitly evaluating scenarios driven by differences in electron transport chain (ETC) composition. Simulations reproduced the expected fermentative yield of approximately 2 mmol ATP per mmol glucose under anaerobic conditions and separated the thirteen fungi into two bioenergetic groups under aerobic respiration based on Complex I status, with predicted yields of approximately 30 versus 22 mmol ATP per mmol glucose. Forcing flux through the alternative oxidase bypass further reduced ATP yields to approximately 12 and 4 mmol ATP per mmol glucose in Complex I-containing and Complex I-lacking fungi, respectively. Collectively, this work provides a manually curated, ModelSEED-consistent, and extensible fungal core metabolic template, deployed in DOE KBase as a resource for automated reconstruction of central carbon core models from any sequenced fungal genome. In addition, the CFCMM provides modular components for developing GEMs with more accurate energy predictions and enables robust comparative analyses of fungal bioenergetics and core metabolic diversity

59 BASIC BIOLOGICAL SCIENCES↗