Search NASA⌕ Search

SEARCH · Search NASA

Results for “Comparative Analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Modular Assembly of FTO|Chromophore-Catalyst Hierarchical Films Based on Strong Dipole Interactions

Here, we have designed and characterized modular self-assembled hierarchical films containing a molecular catalyst tethered to an anchoring molecule by means of dipole-induced dipole interactions. In order to do so, two new Co III -based molecular catalyst candidates were designed, namely, [Co III L 1 (pyrr) 2 ]ClO 4 (Co1) and [Co III L 2 (pyrr) 2 ]ClO 4 (Co2), where L 1 and L 2 are the respective deprotonated forms of N,N′-[4,5-bis(dodecyloxy)-1,2-phenylene]dipicolinamide and N,N′-[4,5-bis(methoxyethoxy)-1,2-phenylene]dipicolinamide and were characterized by electrochemical, electronic, and film formation properties. Species Co1 and Co2 were deposited onto an anchor molecule such as octylphosphonic acid (OPA) or the chromophoric [Ru II (bpy PO3H ) 2 (bpy C7 )]Cl 2 (Ru) previously attached onto conductive fluorine-doped tin oxide (FTO). Four hierarchical films of the form substrate|anchor-catalyst were obtained, namely, FTO|OPA-Co1, FTO|OPA-Co2, FTO|Ru-Co1, and FTO|Ru-Co2, and the role of dipole-dipole interactions between anchor and catalyst modules was assessed. These newly synthesized hierarchical films were characterized by a host of surface-specific methods that include X-ray photoelectron spectroscopy, ellipsometry, X-ray fluorescence, and water contact angle, thus enabling an unprecedented level of analysis. Compared to the weak C-H van der Waals interactions exhibited by Co1, the presence of alkoxy chains in Co2 ensures stronger dipole-dipole interactions with the alkyl chain of the anchors due to O···H formation. The persistence of their redox properties, which include metal oxidation, and directionality of electron transport were probed suggesting direct relevance to catalytic processes such as water oxidation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Protein Adhesion on Semi-Fluorinated Polystyrene Surfaces in Static and Dynamic Measurements

Reducing protein adhesion is a critical strategy in fouling-resistant material innovation, with broad applications spanning biomedical and healthcare devices, biosensors, industrial and environmental systems, and other important technological domains. Here, in this study, we elucidated protein adhesion behavior on polystyrene-based thin films by neutron reflectometry (NR) and quartz crystal microbalance with dissipation (QCM-D), using both lysozyme and bovine serum albumin (BSA) as model proteins. To this end, semifluorinated polystyrene thin films with gradient wettability and surface energy were fabricated through dry processing using plasma oxidation and gas-phase deposition. Although it is believed that a fully fluorinated alkyl chain offers extremely low surface energy, thus rejecting foulants, and has been used in many fouling-resistant surface designs, enhanced protein–surface interactions were observed consistently in NR and QCM-D results, due to the combined effects of surface morphology and chemistry. On the contrary, depositing shorter fluorinated silane onto a hydrophilic PS surface contributed to a more homogeneous nanoscale fluorine coating, resulting in less initial protein adsorption and improved surface recovery. Comparative analysis of proteins with different sizes on the nanopatterned semifluorinated surface revealed the influence of molecular characteristics on surface interactions. Lysozyme, being smaller and more compact, showed faster adsorption kinetics and higher surface coverage but largely reversible binding, whereas BSA, with its larger and more flexible structure, formed broader and more stable interfacial layers. This study fills the gap in understanding protein adhesion within the range of hydrophobicity (water contact angle ∼90°), as current strategies often associate with extreme hydrophilic and superhydrophobic surfaces due to hydration or low-surface-energy rejection mechanisms, respectively. It also provides in-depth insights into current combinatorial fouling-resistant surface design.

Yuan, Yue [Oak Ridge National Laboratory (ORNL), O↗
DOE OSTI2025

Flash Communication: Boron K-edge XAS and TDDFT Studies of Covalent Metal–Ligand Bonding in Ni(C 2 B 9 H 11 ) 2

Ligand K-edge X-ray absorption spectroscopy (XAS), a technique that can measure variations in covalent metal–ligand bonding, has rarely been used to assess covalency in complexes containing metal–boron bonds. Here we describe ligand K-edge XAS and TDDFT studies of the Ni dicarbollide complex Ni(C 2 B 9 H 11 ) 2 (1) and the Ni-free salt (HNMe 3 )(C 2 B 9 H 12 ) (L1). The XAS spectrum for 1 reveals a pre-edge feature indicative of covalent Ni–B bonding, which is corroborated by time-dependent density functional theory (TDDFT) calculations and comparative analysis to L1 and inner-shell electron energy loss spectroscopy (ISEELS) collected on the same Ni complex.

Boron↗
DOE OSTI2024

Low Melting Temperature Gallium–Indium Liquid Metal Anode for Solid-State Li-Ion Batteries

Solid-state Li-ion batteries are attracting attention for their enhanced safety features, higher energy density, and broader operational temperature range compared to systems based on liquid electrolytes. However, current solid-state Li-ion batteries face performance challenges, such as suboptimal cycling and poor rate capabilities, often due to inadequate interfacial contact between the solid electrolyte and electrodes. To address this issue, we incorporated a gallium–indium (Ga–In) liquid metal as the anode in a solid-state Li-ion battery setup, employing Li 6 PS 5 Cl as the solid electrolyte. Operating at room temperature, this configuration achieved an initial capacity of 389 mAh g –1 and maintained 88% of this capacity after 30 cycles at a 0.05 C rate. It also demonstrated a capacity retention of 66% after 500 cycles at a 0.5 C rate. In comparison to solid anode materials, such as tin, the Ga–In liquid metal exhibited superior cycling stability and rate capacity, which is due to the self-healing and fluid properties of the alloy that ensure stable interfacial contact with solid electrolytes. In situ X-ray diffraction (XRD) and ex situ scanning electron microscope (SEM) analyses revealed that indium does not directly participate in the lithiation/delithiation process. Instead, it helps maintain the alloy’s low melting point, facilitating its return to a liquid state after delithiation. In a comparative analysis of stack pressure during cycling in cells utilizing Ga–In liquid metal and tin, the Ga–In liquid metal cell demonstrated an ability to buffer pressure increases associated with deformation. In conclusion, these findings suggest a promising approach for enhancing solid-state batteries by integrating liquid metal anodes, which improve interfacial contact and stability.

Alloys↗
DOE OSTI2026

Quantifying Outer- and Inner-Coordination Sphere Effects Using Uranium Redox Chemistry in Molten Salt Solutions

Defining the relative influence of intramolecular and intermolecular forces is a fundamental problem in chemistry that is difficult to quantify. To address this challenge, we developed a method to evaluate the relative impact of direct chemical bonding in the inner-coordination sphere vs effects from cations in the outer-coordination sphere by comparative analysis of uranium redox reactivity in various molten salts. We observed that outer-coordination sphere cations (M 1+ ) and inner-coordination sphere anions (X 1– ) both affected uranium redox reactivity, with more polarizing M 1+ and larger X 1– favoring uranium in low oxidation states. Changing M 1+ (Li, Na, K) shifted the U IV + e 1– ⇌ U III (U IV/III ) and U III + 3e 1– → U 0 metal potentials by +330 and +240 mV, respectively. Changing X 1– (Cl, Br, I) caused larger shifts of +440 mV for the U IV/III redox potential and +1060 mV for the U 0 metal deposition potential. Using Coulomb’s Law, we correlated these potentials with electrostatic interactions between UIII and the molten salt. This model provided a facile way of predicting redox chemistry within molten salts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Ultrafast Formation of Jahn–Teller Polarons Revealed by State-Selective Excitation in Correlated Spinel Co 3 O 4

Jahn–Teller polarons are quasiparticles that stem from symmetry breaking and strong local electron–phonon coupling. They originate from an excess charge carrier being dressed by a local lattice distortion, caused by the Jahn–Teller effect, and they critically impact electrical, structural, and magnetic properties in transition metal oxides. The observation of the microscopic steps involved in their formation is essential for enabling control over material properties through the targeted activation of local, site-specific modifications with light pulses. While Jahn–Teller distortion associated with polaron formation was predicted to contribute significantly to changes in electronic band gap and optical properties in Co 3 O 4 , its experimental observation remains elusive, requiring signatures of local symmetry reduction. In this work, we demonstrate Jahn–Teller polaron formation in spinel Co 3 O 4 . By exciting electronic transitions at 3.10 eV and 1.55 eV, we target either the Oh Cobalt(III) or the Td Cobalt(II) ions, and drive the subsequent coherent responses of the system through two different pathways. For the former, we demonstrate that ligand-to-metal charge transfer leads to Jahn–Teller polaron formation, which is linked to the deformation potential and magnetoelastic coupling. For the latter, we identify the coherent excitation of a T 2g phonon mode launched by on-site d-d electronic transitions. Key to our observations is the ability to target site-specific electronic excitations in spinel Co 3 O 4 using ultrafast optical pulses, while monitoring the ensuing low-energy collective modes through the coherent time-domain response of the material. Our approach, which combines the comparative analysis of experimental fingerprints with the support from density functional theory calculations, is broadly applicable to systems in which Jahn–Teller polaron physics has been theoretically predicted but remains experimentally unverified, and it underscores the potential of electronic-state targeting as a route to selectively excite and probe quasiparticle dynamics in solids.

Lattices↗
DOE OSTI2026

Comparing Liquid Vortex Capture & the Rapid Droplet Sampling Interface for Single Cell Mass Spectrometry

High-throughput single-cell mass spectrometry is a rapidly evolving field that requires innovative sampling and ionization techniques to balance speed, sensitivity, and reliability for metabolomic and lipidomic analyses. This study provides a comparative analysis of two cutting-edge ionization platforms for single-cell analysis: Liquid Vortex Capture (LVC) and Rapid Droplet Sampling Interface (RDSI). The performance was benchmarked by testing pharmaceuticals, EquiSPLASH, and single-cell experiments. RDSI demonstrated up to 100-fold improvements in sensitivity for drugs and lipids such as propranolol, amiodarone, atorvastatin, and phosphocholines in water and phosphate-buffered solutions. This was attributed to its low-flow rate operation (3 μL/min) and reduced dilution. Conversely, LVC excelled in handling higher liquid volumes with greater reproducibility due to its higher solvent flow rate (200 μL/min), enabling increased dilution, solubility, and cleaning. Single-cell uptake of atorvastatin incubated for 10 min, or amiodarone incubated for 24 h in HepG2 cells, similarly revealed up to 85-fold enhancement in sensitivity by RDSI for drugs and lipids. These findings highlight the potential of RDSI for enhancing sensitivity in single-cell drug monitoring and lipidomics.

Cahill, John [ORNL] (ORCID:0000000298664010)↗
DOE OSTI2025

Deep Learning for Subsurface Flow: A Comparative Study of U‐Net, Fourier Neural Operators, and Transformers in Underground Hydrogen Storage

Subsurface flow research is essential for the sustainable management of natural resources and the environment. Deep learning (DL) has significantly advanced this field by developing efficient and accurate surrogate models to replace computationally expensive physics‐based simulations. These surrogate models are commonly used to predict the spatiotemporal evolution of state variables, such as gas saturation and reservoir pressure, in heterogeneous geological formations. Despite the various DL models applied to this task, there is a lack of studies systematically comparing their performance. This absence of comparative analysis leads to somewhat arbitrary DL model selection in subsurface flow research, resulting in suboptimal performance and potentially inaccurate predictions. To bridge this gap, we conduct a systematic comparison study of three popular DL architectures—U‐Net, Fourier Neural Operators (FNO), and Segmentation Transformer (SETR)—in surrogate modeling of underground hydrogen storage (UHS). We focus on UHS due to its promise of enhancing clean energy resilience and its cyclic operational conditions that represent common scenarios in various subsurface applications. We evaluate the models based on accuracy, training cost, and inference speed. The comparison shows that U‐Net achieves the highest accuracy, followed by SETR and FNO. Despite its lower accuracy, FNO has the highest inference speed. SETR offers competitive accuracy with the least training memory usage, demonstrating the potential of transformers in learning subsurface flow. Our results provide guidance for selecting DL models for surrogate modeling in a wide range of subsurface flow problems.

42 ENGINEERING↗
DOE OSTI2025

Transcription factor binding divergence drives transcriptional and phenotypic variation in maize

Regulatory elements are essential components of plant genomes that have shaped the domestication and improvement of modern crops. However, their identity, function and diversity remain poorly characterized, limiting our ability to harness their full power for agricultural advances using induced or natural variation. Here, in this study, we mapped transcription factor (TF) binding for 200 TFs from 30 families in two distinct maize inbred lines historically used in maize breeding. TF binding comparison revealed widespread differences between inbreds, driven largely by structural variation, that correlated with gene expression changes and explained complex quantitative trait loci such as Vgt1, an important determinant of flowering time, and DICE, an herbivore resistance enhancer. CRISPR–Cas9 editing of TF binding regions validated the function and structure of regulatory regions at various loci controlling plant architecture and biotic resistance. Our maize TF binding catalogue identifies functional regulatory regions and enables collective and comparative analysis, highlighting its value for agricultural improvement.

Galli, Mary [Rutgers Univ., Piscataway, NJ (United↗
DOE OSTI2024

Metabolic complexity drives divergence in microbial communities

Microbial communities are shaped by environmental metabolites, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. Here, in this work, we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but they diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively simpler ones that then participate in cross-feeding between community members, is necessary and sufficient to recapitulate our experimental observations. In addition to helping understand the role of the environment in community assembly, the divergence-complexity effect can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions towards microbiome engineering applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Semi-empirical model for Henry’s law constant of noble gases in molten salts

Henry’s law constant, which describes the proportionality of dissolved gas to partial pressure of free gas in liquid–gas equilibrium systems, can also be applied to mass transport applications. In this work, we investigated an approach for determining the solubility of noble gases in a molten salt liquid utilizing the equilibrium concept of Henry’s gas constant. Henry’s gas constant is described as a mathematical function dependent on the van der Waals radius of the noble gas and the temperature of the molten salt. The alteration in Gibbs free energy encompasses contributions from both surface and volume energies. Enthalpy and entropy are deduced from these surface and volume energies in the Gibbs free energy formulation. A comparative analysis was conducted between the conventional method and our proposed model. Moreover, useful chemical properties can be determined from examination of surface and volume energies. Our findings provide an accurate and general theory of Gibbs free energy that can be validated experimentally based on the model proposed herein. This work unifies the prediction of Henry gas constant and subsequently the entropy and enthalpy calculation for noble gases in a molten salt solution to a single functional form using van der Waals radius of the gas and temperature of the system. This functional form is then used to perform a multiple regression method to find two parameters corresponding to the surface energy and volume energy. These two parameters are consistent between all combinations of noble gas and molten salt.

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

Exploring the effects of molecular beam epitaxy growth characteristics on the temperature performance of state-of-the-art terahertz quantum cascade lasers

This study conducts a comparative analysis, using non-equilibrium Green’s functions (NEGF), of two state-of-the-art two-well (TW) Terahertz Quantum Cascade Lasers (THz QCLs) supporting clean 3-level systems. The devices have nearly identical parameters and the NEGF calculations with an abrupt-interface roughness height of 0.12 nm predict a maximum operating temperature (T max ) of ~ 250 K for both devices. However, experimentally, one device reaches a T max of ~ 250 K and the other a T max of only ~ 134 K. Both devices were fabricated and measured under identical conditions in the same laboratory, with high quality processes as verified by reference devices. The main difference between the two devices is that they were grown in different MBE reactors. Our NEGF-based analysis considered all parameters related to MBE growth, including the maximum estimated variation in aluminum content, growth rate, doping density, background doping, and abrupt-interface roughness height. From our NEGF calculations it is evident that the sole parameter to which a drastic drop in T max could be attributed is the abrupt-interface roughness height. We can also learn from the simulations that both devices exhibit high-quality interfaces, with one having an abrupt-interface roughness height of approximately an atomic layer and the other approximately a monolayer. However, these small differences in interface sharpness are the cause of the large performance discrepancy. This underscores the sensitivity of device performance to interface roughness and emphasizes its strategic role in achieving higher operating temperatures for THz QCLs. We suggest Atom Probe Tomography (APT) as a path to analyze and measure the (graded)-interfaces roughness (IFR) parameters for THz QCLs, and subsequently as a design tool for higher performance THz QCLs, as was done for mid-IR QCLs. Our study not only addresses challenges faced by other groups in reproducing the record T max of ~ 250 K and ~ 261 K but also proposes a systematic pathway for further improving the temperature performance of THz QCLs beyond the state-of-the-art.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗
DOE OSTI2026

Decentralised Reinforcement Learning for Dynamic Cyberattack Response in Microgrid Networks

Microgrids rely on communication networks for reliable operation, which makes them inherently vulnerable to cyberattacks. Such attacks can destabilise system dynamics and drive states away from their nominal operating trajectories. Although several physics-informed and machine learning-based strategies have been developed to counter these threats, the rapidly evolving cyber landscape enables adversaries to bypass static defences or rules-based mitigation approaches. This paper proposes a dynamic, online-trained and fully decentralised reinforcement learning (RL)-based cyberattack response framework to protect microgrids from evolving cyberattacks. The proposed framework deploys multiple deep Q-networks (DQNs), each associated with a distributed energy resource (DER), to enable localised and adaptive attack mitigation. In this framework, each DQN processes local voltage and frequency measurements—combined with intrusion detection system (IDS) alerts—as observations and rewards to guide decision-making. Extensive simulation studies demonstrate the robustness of the proposed framework under diverse attack scenarios and varying IDS-induced detection delays. Comparative analysis highlights its superiority over existing static or preexisting rules-based mitigation approaches. Finally, we present an analysis that shows the framework's scalability to real-life microgrids with more interacting agents.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Wavelength modulation laser-induced fluorescence for plasma characterization

Laser-Induced Fluorescence (LIF) spectroscopy is an essential tool for probing ion and atom velocity distribution functions (VDFs) in complex plasmas. VDFs carry information about the kinetic properties of species that is critical for plasma characterization. Accurate interpretation of these functions is challenging due to factors such as multicomponent distributions, broadening effects, and background emissions. Our research investigates the use of Wavelength Modulation (WM) LIF to enhance the sensitivity of VDF measurements. Unlike standard Amplitude Modulation (AM) methods, WM–LIF measures the derivative of the LIF signal. This approach makes variations in VDF shape more pronounced. VDF measurements with WM–LIF were investigated with both numerical modeling and experimental measurements. The developed model enables the generation of both WM and AM signals, facilitating comparative analysis of fitting outcomes. Experiments were conducted in a weakly collisional argon plasma with magnetized electrons and non-magnetized ions. Measurements of the argon ion VDFs employed a narrow-band tunable diode laser, which scanned the 4p 4 D 7/2 –3d 4 F 9/2 transition centered at 664.553 nm in vacuum. A lock-in amplifier detected the second harmonic WM signal, which was generated by modulating the laser wavelength with an externally controlled piezo-driven mirror of the diode laser. Finally, our findings indicate that the WM–LIF signal is more sensitive to fitting parameters, allowing for better identification of VDF parameters such as the number of distribution components, their temperatures, and velocities. In addition, WM–LIF can serve as an independent method to verify AM measurements and is particularly beneficial in environments with substantial light noise or background emissions, such as those involving thermionic cathodes and reflective surfaces.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Insights into the year-round vertical distribution of chlorophyll concentration in high-latitude Arctic Ocean: implications for primary production

Climate-induced rapid changes in the Arctic Ocean, such as decreasing sea ice extent and increasing water temperature, are altering nutrient and light availability, profoundly impacting primary producer growth. However, access to the high-latitude Arctic Ocean is limited, and satellite data are primarily available only during summer, making continuous in-situ data collection challenging. We collected year-round chlorophyll-a (Chl-a) concentration data in high-latitude regions using a mooring system and performed a comparative analysis with reanalysis data. Unlike previous satellite-based studies, which typically rely on surface measurements, we used the annual vertical distribution of Chl-a. These data were applied to the vertically generalized production model to accurately estimate annual primary production. The moored Chl-a concentration data showed that phytoplankton exhibited a typical subsurface chlorophyll maximum (SCM) layer as sea ice retreated in June. Contrary to the gradually deepening SCM distribution predicted by model-based reanalysis data, the SCM layer persisted for approximately 4 months. This indicates that light and nutrient conditions within the SCM layer remained stable, sustaining continuous phytoplankton growth. Annual primary production, reflecting this vertical distribution of Chl-a concentration, was 6.85 gC m −2 yr −1 . This exceeded satellite-based estimates by at least two-fold, highlighting the significant underestimation of primary production by satellite approaches. Estimating primary production while accounting for the vertical distribution of phytoplankton and light is essential for improving ecological models to better understand carbon cycle and food web changes in the Arctic Ocean, with important implications for climate change predictions.

Arctic Ocean↗
DOE OSTI2025

The genome of the polyextremophilic yeast, Naganishia friedmannii, reveals adaptations involved in stress response pathways, carbohydrate metabolism expansion, and a limited DNA repair repertoire

Here we report the draft genome sequence of Naganishia friedmannii (formerly Cryptococcus friedmannii) isolate, a Basidiomycota yeast commonly found in some of the most extreme environments of the Earth's cryosphere. We isolated N. friedmannii strain Llullensis from soils at 6000 m above sea level on Volcán Llullaillaco, Argentina. The genome was 22.2 Mb with 6251 identified protein coding genes. Proteins known to be associated with thermal, osmotic, and radiation stress were identified in the genome. Comparative analysis with seven other Naganishia genomes revealed unique features underlying its polyextremophilic lifestyle. Naganishia friedmannii showed an expansion of genes involved in breaking down plant-derived carbohydrates, supporting the hypothesis that it survives at high elevations by metabolizing wind-deposited organic matter. Surprisingly, many genes involved in cell-cycle checkpoints and DNA repair were missing, as in several other Naganishia species. This extensive loss may be adaptive in extreme environments prone to abiotic stress, where a high mutation rate could generate advantageous traits, and reduced cell-cycle control may allow for faster reproduction that would be advantageous for rapid growth during brief periods of soil wetting following rare snow events.

Vimercati, Lara↗
DOE OSTI2024

Corn stover variability drives differences in bisabolene production by engineered Rhodotorula toruloides

Microbial conversion of lignocellulosic biomass represents an alternative route for production of biofuels and bioproducts. While researchers have mostly focused on engineering strains such as Rhodotorula toruloides for better bisabolene production as a sustainable aviation fuel, less is known about the impact of the feedstock heterogeneity on bisabolene production. Critical material attributes like feedstock composition, nutritional content, and inhibitory compounds can all influence bioconversion. Further, the given feedstocks can have a marked influence on selection of suitable pretreatment and hydrolysis technologies, optimizing the fermentation conditions, and possibly even modifying the microorganism's metabolic pathways, to better utilize the available feedstock. Here, this work aimed to examine and understand how variations in corn stover batches, anatomical fractions, and storage conditions impact the efficiency of bisabolene production by R. toruloides. All of these represent different facets of feedstock heterogeneity. Deacetylation, mechanical refining, and enzymatic hydrolysis of these variable feedstocks served as the basis of this research. The resulting hydrolysates were converted to bisabolene via fermentation, a sustainable aviation fuel precursor, using an engineered R. toruloides strain. This study showed that different sources of feedstock heterogeneity can influence microbial growth and product titer in counterintuitive ways, as revealed through global analysis of protein expression. The maximum bisabolene produced by R. toruloides was on the stalk fraction of corn stover hydrolysate (8.89 ± 0.47 g/L). Further, proteomics analysis comparing the protein expression between the anatomic fractions showed that proteins relating to carbohydrate metabolism, energy production, and conversion as well as inorganic ion transport metabolism were either significantly upregulated or downregulated. Specifically, downregulation of proteins related to the iron–sulfur cluster in stalk fraction suggests a coordinated response by R. toruloides to maintain overall metabolic balance, and this was corroborated by the concentration of iron in the feedstocks.

09 BIOMASS FUELS↗