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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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17 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Ion Transport in Self-Assembled Peptoid Membranes with Carbon Nanotube Porin Channels

Artificial membranes that combine high ionic selectivity with mechanical robustness remain a key challenge for next-generation separation technologies. Here, we report ion transport measurements in biomimetic membranes composed of crystalline peptoid nanosheets co-assembled with carbon nanotube porins (CNTPs). Pure peptoid sheets formed defect-free, ion-impermeable membranes, which were then suspended over small SiN x nanopore apertures for ion transport measurements. Incorporation of CNTPs into the peptoid sheet matrix made these membranes ion permeable, with ion conductance values consistent with ion transport through individual and multiple carbon nanotube channels. In conclusion, the modularity and molecular order of peptoid membranes, combined with the exceptional conductance properties of CNTPs, position this platform as a versatile framework for assembling programmable, selective, and robust nanofluidic membranes that can bridge the performance gap between biological and synthetic membrane materials.

Biotechnology↗
DOE OSTI2026

Membrane Composition Influences Expression Yield of Plant Cytochrome P450s in E. coli Lysate-Based Cell-Free Systems

Plant cytochrome P450 enzymes are central to natural product biosynthesis, but remain difficult to express in microbial hosts due to their transmembrane nature. Lysate-based, cell-free expression systems allow supplementation with artificial membranes to support the expression and translocation of transmembrane proteins. We developed a framework to systematically test liposomal membrane compositions to enhance the plant P450 expression yield. Adjustments to common phospholipid ratios or the addition of plant galactolipids had minimal impact on expression. In contrast, blended liposomes containing Egg PC, sterol-conjugated phospholipids, and PEGylated lipids produced concentration-dependent increases in expression. Expression of an Escherichia coli mechanosensitive channel and three plant P450s improved more than 2-fold, with some P450s showing up to 14-fold enhancement. Furthermore, these findings highlight membrane composition as a key determinant of the P450 expression yield in cell-free expression systems. While P450 activity was not measured, these findings provide a framework for future workflows toward achieving functional plant transmembrane enzymes for the bioproduction of natural products.

TXTL↗
DOE OSTI2025

Abbr. Final report: Self-assembled molecular containers as artificial water channels: towards biomimetic desalination membranes

Global water scarcity demands advances in desalination technologies that can deliver more fresh water with less energy. Current reverse osmosis membranes are fundamentally limited by a trade-off between how much water they can pass and how well they block salts. To address this challenge, we developed a bottom-up strategy to design and test artificial water channels that mimic the efficiency of biological proteins but are built from robust synthetic molecules. Over two years, we synthesized and evaluated more than twenty molecular channel candidates, including supramolecular macrocycles and nanographene pores with atomically precise structures. We showed that small chemical modifications allow direct control over pore size and chemistry, which in turn govern water permeability and salt rejection. In collaboration with university partners, we reported the first experimental demonstration of water transport through a nanographene pore, bridging a long-standing gap between simulation and experiment. Several of the artificial channels we developed achieved water–salt selectivity beyond conventional polymer membranes, highlighting their potential for next-generation desalination and precision separations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Atomic lift-off of epitaxial membranes for cooling-free infrared detection

Recent breakthroughs in ultrathin, single-crystalline, freestanding complex oxide systems have sparked industry interest in their potential for next-generation commercial devices. However, the mass production of these ultrathin complex oxide membranes has been hindered by the challenging requirement of inserting an artificial release layer between the epilayers and substrates. Here we introduce a technique that achieves atomic precision lift-off of ultrathin membranes without artificial release layers to facilitate the high-throughput production of scalable, ultrathin, freestanding perovskite systems. Leveraging both theoretical insights and empirical evidence, we have identified the pivotal role of lead in weakening the interface. This insight has led to the creation of a universal exfoliation strategy that enables the production of diverse ultrathin perovskite membranes less than 10 nm. Our pyroelectric membranes demonstrate a record-high pyroelectric coefficient of 1.76 × 10 −2 C m −2 K −1 , attributed to their exceptionally low thickness and freestanding nature. Moreover, this method offers an approach to manufacturing cooling-free detectors that can cover the full far-infrared spectrum, marking a notable advancement in detector technology.

nanoscale materials↗
DOE OSTI2024

Tetranuclear Polypyridylruthenium(II) Complexes as Selective Nucleic Acid Stains for Flow Cytometric Analysis of Monocytic and Epithelial Lung Carcinoma Large Extracellular Vesicles

Selective staining of extracellular vesicles (EVs) is a major challenge for diagnostic and therapeutic applications. Herein, the EV labeling properties of a new class of tetranuclear polypyridylruthenium(II) complexes, Rubb7-TNL and Rubb7-TL, as phosphorescent stains are described. These new stains have many advantages over standard stains to detect and characterize EVs, including: high specificity for EV staining versus cell staining; high phosphorescence yields; photostability; and a lack of leaching from EVs until incorporation with target cells. As an example of their utility, large EVs released from control (basal) or lipopolysaccharide (LPS)-stimulated THP-1 monocytic leukemia cells were studied as a model of immune system EVs released during bacterial infection. Key findings from EV staining combined with flow cytometry were as follows: (i) LPS-stimulated THP-1 cells generated significantly larger and more numerous large EVs, as compared with those from unstimulated cells; (ii) EVs retained native EV physical properties after staining; and (iii) the new stains selectively differentiated intact large EVs from artificial liposomes, which are models of cell membrane fragments or other lipid-containing debris, as well as distinguished two distinct subpopulations of monocytic EVs within the same experiment, as a result of biochemical differences between unstimulated and LPS-stimulated monocytes. Comparatively, the staining patterns of A549 epithelial lung carcinoma-derived EVs closely resembled those of THP-1 cell line-derived EVs, which highlighted similarities in their selective staining despite their distinct cellular origins. This is consistent with the hypothesis that these new phosphorescent stains target RNA within the EVs.

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

Aberration corrected RF flipper for high resolution neutron spectroscopy

Project Summary Company: Adelphi Technology, Inc. Title: Aberration-corrected High Frequency RF Flipper for High-Resolution Neutron Spectroscopy PI: Dr. Jay Theodore Cremer Topic: C55-11 Enhancement of Scattering Instrumentation Technology Used at Pulsed and Continuous Sources Subtopic: d. Other Statement of the problem or situation that is being addressed. The quest to understand heterogeneous and hierarchical materials is gathering momentum, as described in a 2015 report by the Basic Energy Sciences Advisory Committee on Challenges at the Frontiers of Matter and Energy. For the past 40 years a technique called neutron spin echo (NSE) has been used to probe molecular motions in such non-crystalline materials over time scales from 10’s of picoseconds to 100’s of nanoseconds. The method has provided unique information about the dynamics of soft heterogeneous materials, including confirmation of the de Gennes model of polymer reptation and quantitative measurement of bending constants of biologically relevant lipid membranes. However, scientists continue to clamor for even higher resolution than NSE can provide. Biomaterials, polymers, glasses, and artificially nanostructured materials all manifest slow molecular motions because of weak or competing interactions between subunits and are amenable to study with neutrons, provided sufficiently long dynamical correlation times can be achieved. All these materials have important applications to advanced technologies so understanding them is key to technological progress. General statement of how this problem is being addressed. We will address the need for high-resolution neutron spectroscopy by using a technique called Neutron Resonance Spin Echo (NRSE). While similar to NSE in many respects, this method has the potential to exceed the NSE capabilities, if 2 technical hurdles can be overcome. The major impediments to successful high-resolution NRSE are the availability of two technologies: a very high frequency, efficient, radiofrequency (rf) flipper for neutrons and a method to correct certain magnetic aberrations. Based on previous STTR support and follow-on research we have developed a suitable rf flipper and we have invented a method to correct the magnetic aberrations. Both technologies need refinement to make them suitable for implementation at a neutron source such as the Oak Ridge National Laboratory nuclear reactor. In this proposal we seek to perfect the two technologies and to combine them into a single, operationally convenient device. Commercial Applications and Other Benefits In view of the increasing demand for the unique scientific information that high resolution neutron spectroscopy can provide, we expect several major instrumentation upgrades at both U.S. and foreign neutron centers will require make use of the NRSE method over the coming decade, creating a market for the devices we will design. These components will enhance scientists’ abilities to probe the time dependence of density fluctuations in a wide range of hierarchical and heterogeneous materials many of which are vital to existing and future technologies. Key Words – Polarized Neutrons, Neutron Spin Echo, Neutron Scattering, advanced materials. Summary for Members of Congress Neutron beams are a powerful materials-science probe that provide unique information about the structure of matter. The proposed devices will accelerate scientific discoveries required to achieve national goals for new technological materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Solar PV

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) research platform. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence data centers and other variable loads. This dataset entry describes the behavior of a 1.25-MW proton exchange membrane MC250 electrolyzer system, manufactured by Nel Hydrogen , [1] when fed historical data generated by the 430-kW, fixed-axis solar photovoltaic (PV) array located at NLR’s Flatirons Campus. (While the electrolyzer balance of plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack.) Solar PV power output data for the 2020 calendar year were categorized on a daily basis by total energy generation and standard deviation. Each day was then ranked by these metrics, and the 25th, 50th, and 100th percentiles were selected. The 75th percentile day did not exhibit sufficient variability to make for a valuable experiment. A similar process was used for the related historical wind dataset . [2] The historical days in 2020 that represented these percentiles are Dec. 19, March 29, and May 4, respectively. The entire solar day’s power profile was then fed through the MC250 electrolyzer. Due to its length, the 100th percentile day experiment was split into two parts, and the final 3 hours of the solar day were not captured. These final 3 hours contained no spikes or dips of interest and simply represented a slow decay of input solar power. Also, a single timestamp (13:13:47 on Jan. 14, 2026) was lost in the hydrogen system supervisory control and data acquisition. Finally, during the 25th percentile experiment (solar day Dec. 19, 2020) data recording was lost from 11:00:13 to 11:14:45. The roughly 15 minutes of the solar profile were rerun at the end of the experiment and spliced into this time slot during post-processing. The electrolysis system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operation of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical solar profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. For more details on the statistical analysis process, see the slide deck “Public Reference Data for Megawatt-Scale Hydrogen Electrolysis: NLR Historical Solar PV Analysis and Profile Generation” accessible with this data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single solar PV electrolysis experiment and is formatted as: {technology}_{percentile}_{scaling factor} For instance, “solarPV-430kW_25_2x.zip” reports the experiment using the 25th percentile solar data from the historical 2020 solar PV dataset, scaled to 200%. Scaling factors were applied to the generated solar PV power output files to more closely match the 1.25-MW capacity of the electrolyzer. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and solar power input. A PDF file detailing the historical solar data statistical analysis used to generate the solar profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all experiments combined into one dataset labeled "combined_solarPV_experiments.csv". [1] nelhydrogen.com/product/mc-series-electrolyser . [2] data.nlr.gov/submissions/316 .

08 HYDROGEN↗
DOE OSTI2025

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis – Simulated Wind

The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence (AI) data centers and other variable loads. This dataset entry describes hydrogen production using a single, simulated wind turbine. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel Hydrogen . While the unit supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. For the simulated wind energy profiles, NLR used OpenFAST to simulate a 3.4-MW International Energy Agency (IEA) reference wind turbine. The hour-long wind energy profiles varied over wind turbulence intensity (Class A or Class C) and average wind speed (5, 7, or 9 m/s). To match the power limits of the 1.25-MW electrolyzer and 3.4-MW IEA wind turbine most effectively and to maximize the efficiency of hydrogen production at a given average wind speed, the profiles were sometimes scaled by two times. This means that, in some cases, the experimental setup assumed two 1.25-MW electrolyzers were coupled with the wind turbine, representing a total maximum electrolysis load of 2.5 MW. Finally, NLR experimented with two settings for the electrolyzer power supply minimum and maximum current ramp rates (gain and slew): 200 and 400 amperes per second. The simulated profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single wind turbine electrolysis experiment and is formatted as follows: {technology}-{average wind speed}-{turbulence class}_{number of 1.25 MW electrolyzers connected}-{electrolyzer ramp rate in amperes/second} For instance, “windIEA3.4-5ms-C_2-400.zip” represents the hour-long experiment using the IEA 3.4-MW turbine, subjected to an average wind speed of 5 m/s and Class C wind turbulence, and connected to two 1.25-MW electrolyzers with the power supply set to a maximum current ramp rate (gain and slew) of 400 A/s. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production in kilograms per hour, electrolysis power consumption, and input wind turbine power. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30 minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all simulated wind experiments combined into one dataset labeled "combined_wind_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis .

08 HYDROGEN↗
DOE OSTI2025

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - Simulated Wave

The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence (AI) data centers and other variable loads. This dataset entry describes hydrogen production using a single, simulated wave energy conversion device. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel Hydrogen. While the unit supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. For the wave energy, NLR used a wave energy converter model from PacWave. These devices can be equipped with accumulators and pressure relief values to smooth the power output by storing and releasing hydraulic energy. Using a peak power output of 10 MW, the model created two 25-minute profiles: one with and one without the accumulators and pressure relief valves. To down select the profile data from the native resolution of 20 Hz to 1 Hz, NLR took the mean of every 20 data points. NLR experimented with two simulated wave energy power plants: one that peaks at 10 MW, and one that peaks at 5 MW. These profiles were scaled for the physical 1.25 MW electrolyzer by multiplying the original profiles by one eighth and one quarter, respectively. The first profile matches the capacity rating of eight of the 1.25 MW electrolyzers, while the second matches four electrolyzers. Finally, NLR experimented with two settings for the electrolyzer power supply minimum and maximum current ramp rates (gain and slew): 200 and 400 amperes per second. The simulated profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single wave electrolysis experiment and is formatted as follows: {technology}-{accumulator?}_{number of 1.25 MW electrolyzers connected}-{electrolyzer ramp rate in amperes/second} For instance, “wavePacWave-Noacc_4-400.zip” represents the 25 minute-long experiment using the PacWave’s wave energy converter model, equipped with no accumulator, connected to four 1.25-MW electrolyzers with their power supplies set to a maximum current ramp rate (gain and slew) of 400 A/s. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production in kilograms per hour, electrolysis power consumption, and input wave power. An experiment, labeled “characterization_200.zip”, demonstrates the MC250 electrolyzer steady-state response with 30 minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all wave profiles combined into one dataset labeled "combined_wave_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis.

08 HYDROGEN↗
DOE OSTI2026

Nanoarchitectonic Semiconductor–Bio Hybrid Systems with Enhanced Charge Transfer for Hydrogen Peroxide Production

Nanoarchitectonics offers a systematic approach to creating an artificial framework by integrating different multiscale components such as semiconductor lattices and biological substances. However, most nanoarchitectonic abiotic-biotic hybrid systems have intrinsic limitations in imparting nonequilibrium biological features into semiconductor lattices at the nanoscale. Here, in this study, we report a new nanoarchitectonic system integrating bismuth oxychloride (BiOCl) nanosheets with purple membrane (PM) patches. PM is an archaeal subcellular fraction capable of unidirectionally transferring photogenerated charge carriers to its surroundings at the nanoscale independent of archaeal metabolism and retaining this dynamic functionality after isolation from living archaea. Microscopy, spectroscopy, electrochemical, and synchrotron X-ray scattering analyses verify that the nanoarchitectonic hybridization between BiOCl and PM generates a vertical heterostructure, thereby enhancing photogenerated charge-carrier dynamics and enabling the associated photocatalytic capacity. The resulting PM-BiOCl hybrid nanosheets efficiently convert dioxygen into hydrogen peroxide through a two-electron and two-proton transfer process under ambient conditions while simultaneously converting ethylene glycol into value-added chemicals. This study presents a nanoarchitectonic approach that leverages the photogenerated charge-carrier dynamics of the archaeal subcellular fractions to modulate the optoelectronic and catalytic capacity limitations of semiconductors.

catalysis↗
DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Wind

The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence (AI) data centers and other variable loads. This dataset entry describes hydrogen production by conducting a statistical analysis of historical wind data over a five-year period (2020-2025) from a single 1.5MW turbine manufactured by General Electric (GE) located at NLR’s Flatirons Campus, to generate an experimental test profile that was deployed on a 1.25-MW proton exchange membrane type MC250 electrolyzer system manufactured by Nel Hydrogen . [1] While the electrolyzer balance-of-plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. The historical wind data provided several metrics, however, the analysis particularly focused on the measured power output by the wind turbine. The power output time series of data for each day was categorized by total energy generation and standard deviation, and the day that represented the highest combination of these two metrics was chosen – December 25th, 2022. This process was then repeated for a moving four-hour window within this day to identify the most statistically variable period. Finally, this four-hour period was scaled by 65% to match the 1.25 MW electrolyzer. The electrolysis system controls hydrogen production by varying DC current applied to the stack, from a maximum of 3000 A to a minimum safe operation of 300 A, or 10%. Because the current – voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical wind profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1 Hz frequency. For more details on the statistical analysis process, see the presentation labeled “ Public Reference Data for Megawatt-Scale Hydrogen Electrolysis” provided with each data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single wind turbine electrolysis experiment and is formatted as follows: {technology}_{scaling factor}-{electrolyzer ramp rate in amperes/second} For instance, “wind-GE1.5MW_0.65-400.zip” represents the hour-long experiment using historical data from the wind-GE1.5MW turbine, scaled to 65%, with the electrolyzer power supply set to a maximum ramp rate (gain and slew) of 400 A/s. Each .zip folder contains the following files: A .csv file containing raw data An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and wind power input. A PDF file detailing the historical wind data statistical analysis used to generate the wind profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all simulated wind experiments combined into one dataset labeled "combined_historical_wind_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis [1] nelhydrogen.com/product/mc-series-electrolyser .

08 HYDROGEN↗
DOE OSTI2024

Photosynthetic biohybrid systems for solar fuels catalysis

Photosynthetic reaction center (RC) proteins are finely tuned molecular systems optimized for solar energy conversion. RCs effectively capture and convert sunlight with near unity quantum efficiency utilizing light-induced directional electron transfer through a series of molecular cofactors embedded within the protein core to generate a long-lived charge separated state with a useable electrochemical potential. Of current interest are new strategies that couple RC chemistry to the direct synthesis of energy-rich compounds. This Feature Article highlights recent work from our lab on RC and RC-inspired hybrid systems that capture the Sun's energy and convert it to chemical energy in the form of H2, a carbon-neutral energy source derived from water. Further, biohybrids made from the Photosystem I (PSI) RC are among the best photocatalytic H2-producing protein hybrids to date. Targeted self-assembly strategies that couple abiotic catalysts to PSI translate to catalyst incorporation at intrinsic PSI sites within thylakoid membranes to achieve complete solar water-splitting systems. RC-inspired biohybrids interface synthetic photosensitizers and molecular catalysts with small proteins to create photocatalytic systems and enable the spectroscopic discernment of the structural features and electron transfer processes that underpin solar-driven proton reduction. In total, these studies showcase the incredible scientific opportunities photosynthetic biohybrid research provides for harnessing the optimal qualities of both artificial and natural photosynthetic systems and developing materials that capture, convert, and store solar energy as a fuel.

14 SOLAR ENERGY↗
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 OSTI2026

Assembly and Repair of the Photosystem II Reaction Center

This project investigated the biochemical and biophysical mechanisms governing assembly and repair of Photosystem II (PSII), the membrane protein complex responsible for solar-driven water oxidation in oxygenic photosynthesis. The work focused on three integrated areas: (1) protein–protein interactions that facilitate PSII assembly in cyanobacterial biogenesis centers, (2) the chemical mechanism of photo-assembly of the Mn 4 CaO 5 oxygen-evolving complex (OEC), and (3) mechanisms that target PSII reaction centers for degradation and repair in photosynthetic organisms. Using electron paramagnetic resonance spectroscopy, protein biochemistry, molecular genetics, quantitative mass spectrometry, and computational modeling, the project demonstrated that proton release events limit early steps of OEC assembly and that chloride and calcium ions facilitate Mn oxidation and intermediate stabilization. Complementary studies identified chaperone recruitment mechanisms in cyanobacterial PSII biogenesis centers and translation and protease factors involved in PSII turnover in Chlamydomonas. Together, these results establish proton management and coordinated protein quality control as central design principles in PSII assembly and repair and provide mechanistic insight relevant to biological and artificial photosynthetic systems.

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

Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures

This repository hosts an AI-assisted image segmentation and analysis pipeline for Pantoea sp. YR343 cryo-electron microscopy (cryoEM) datasets. The workflow automates membrane thickness measurements, flagella detection, and field-of-view (FOV) screening from low-dose, high-resolution cryoEM micrographs eliminating the need for slow manual annotation. By integrating deep-learning based segmentation (YOLOv11) with quantitative post-processing, this toolkit provides a scalable and reproducible way to study bacterial morphology under hydrated, near-native conditions. The GitHub repository for AI-based tools for cryoEM bacteria ultrastructures can be found here: https://github.com/Sireesiru/Cryo-EM-Ultrastructures/tree/main

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Dataset for Top Model Decision Tree: Selecting Segmentation Models for Reliable Quantitative Analysis in Low- and Ultralow-Dose CryoEM

Motivation Multiple deep learning model architectures can be used to segment bacterial membranes in cryoEM images. However, an AI-based tool advancement is often presented with only a single segmentation model for broad use, and this single model may show inconsistent results across datasets from different users. Here, we present the Top Model Decision Tree, a model screening framework to screen for the best model to generate bacterial inner and outer membrane masks based on user priorities. We use pre-trained segmentation models from YOLOv11, YOLO26, U-Net, Detectron2 and SAM3 fine-tuned on bacterial inner and outer membranes imaged with cryoEM. Run the Framework This notebook must be opened in Google Colab. Mount Google Drive and run with a GPU-based runtime. Open the notebook and follow steps to git clone in folders and files within this repository. There will be a repeating top_model_decision_tree.ipynb (notebook clone) that will not be used. Save your .png binary mask files and .csv table outputs within your Google Drive or download before closing the notebook. The models and all analysis/training scripts are available at [GitHub: https://github.com/Lynnicia/CryoEM_membranes_top_model_decision_tree and https://github.com/Sireesiru/Semantic-Segmentation-of-bacterial-cell-envelope-using-U-Nets.

59 BASIC BIOLOGICAL SCIENCES↗