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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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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Six-Letter DNA Nanotechnology: Incorporation of Z-P Base Pairs into Self-Assembling 3D Crystals

Artificially expanded genetic information systems (AEGIS) were developed to expand the diversity and functionality of biological systems. Recent experiments have shown that these expanded DNA molecular systems are robust platforms for information storage and retrieval as well as useful for basic biotechnologies. In tandem, nucleic acid nanotechnology has seen the use of information-based “semantomorphic” encoding to drive the self-assembly of a vast array of supramolecular devices. To establish the effectiveness of AEGIS toward nanotechnological applications, we investigated the ability of a six-letter alphabet composed of A:T, G:C and synthetic Z:P (Z, 6-amino-3-(1'-β- D-2'-deoxy ribofuranosyl)-5-nitro-(1H)-pyridin-2-one; P, 2-amino-8-(1'- β-D-2'-deoxyribofuranosyl)-imidazo-[1,2a]-1,3,5-triazin-(8H)-4-one) base pairs to engage in 3D self-assembly. We found that crystals could be programmably assembled from AEGIS oligomers. We conclude that unnatural base pairs can be used for the topological self-assembly of crystals. We anticipate the expansion of AEGISbased nucleic acid nanotechnologies to enable the development of novel nanomaterials, high-fidelity signal cascades, and dynamic nanoscale devices.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Deriving Material Properties from Feedback Error Signals in Scanning Tunneling Microscopy

Imperfections in measurements, e.g., deviations and broadening, are not devoid of information; rather, they can reveal valuable physical properties and processes. Scanning tunneling microscopy leverages a negative feedback loop to regulate the tunneling current. Practically, current fluctuates around its set point, and such deviations are considered insignificant and ignored. Here, in this study, we investigate the information embedded in these deviations. In the constant-current mode with an active feedback loop, we observe an unexpected persistent DC current offset from its set point when the tunneling junction is periodically perturbed. We demonstrate both experimentally and theoretically that such error signals encode local tunneling barrier heights and the square of local differential conductance as a consequence of the interplay between rectification and active feedback compensation. We provide evidence on the generalizability of this phenomenology to other negative feedback systems. This new approach has the potential to broadly impact physical sciences by allowing rapid measurements without lock-in amplifiers.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Covert MOF-Based Photoluminescent Tags via Tunable Linker Energetics

We report that the optical anticounterfeiting tags utilize the photoluminescent properties of materials to encode unique patterns, enabling identification and validation of important items and assets. These tags must combine optical complexity with ease of production and authentication to both prevent counterfeiting and to remain practical for widespread use. Metal–organic frameworks (MOFs) based on polynuclear, rare earth clusters are ideal materials platforms for this purpose, combining fine control over structure and composition, with tunable, complex energy transfer mechanisms via both linker and metal components. Here we report the design and synthesis of a set of heterometallic MOFs based on combinations of Eu, Nd, and Yb with the tetratopic linker 1,3,6,8-tetrakis(4-carboxyphenyl)pyrene. The energetics of this linker facilitate the intentional concealment of the visible emissions from Eu while retaining the infrared emissions of Nd and Yb, creating an optical tag with multiple covert elements. Unique to the materials system reported herein, we document the occurrence of a previously not observed 11-metal cluster correlated with the presence of Yb in the MOFs, coexisting with a commonly encountered 9-metal cluster. We demonstrate the utility of these materials as intricate optical tags with both rapid and in-depth screening techniques, utilizing orthogonal identifiers across composition, emission spectra, and emission decay dynamics. This work highlights the important effect of linker selection in controlling the resulting photoluminescent properties in MOFs and opens an avenue for the targeted design of highly complex, multifunctional optical tags.

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

Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of Polyacrylonitrile

Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable for advancing the design principles of final products at reduced processability costs. While ab initio molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers, such as dipole–dipole interactions and hydrogen bonding, and analyzing their influence on the molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIPs) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures and properties at large scales (polymers). NNIP provides critical insight into intra- and interchain hydrogen-bonding and dipolar correlations and accurately predicts the amorphous bulk PAN structure validated by modeling the experimental X-ray structure factor. Furthermore, the NNIP-predicted PAN properties, such as density and elastic modulus, are in good agreement with their experimental values. Overall, the trend in the elastic modulus is found to correlate strongly with the PAN structural orientations encoded in the Hermans orientation factor. In conclusion, this study enables the ability to predict the structure–property relations for PAN and analogues with sustainable ab initio accuracy across scales.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Database of Nonaqueous Proton-Conducting Materials

This work presents the assembly of 48 papers, representing 74 different compounds and blends, into a machine-readable database of nonaqueous proton-conducting materials. SMILES was used to encode the chemical structures of the molecules, and we tabulated the reported proton conductivity, proton diffusion coefficient, and material composition for a total of 3152 data points. The data spans a broad range of temperatures ranging from -70 to 260 °C. To explore this landscape of nonaqueous proton conductors, DFT was used to calculate the proton affinity of 18 unique proton carriers. The results were then compared to the activation energy derived from fitting experimental data to the Arrhenius equation. It was found that while the widely recognized positive correlation between the activation energy and proton affinity may hold among closely related molecules, this correlation does not necessarily apply across a broader range of molecules. This work serves as an example of the potential analyses that can be conducted using literature data combined with emerging research tools in computation and data science to address specific materials design problems.

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

Ripening of Rh Nanoparticle Catalysts in Reverse Water–Gas Shift via a Data-Driven Model Combining Physics, Theory, and Experiment

Degradation via sintering is an ongoing challenge that impedes the broad commercial success of supported metallic nanoparticle catalysts. To mitigate degradation via informed catalyst design and process operations, here we aim to disambiguate the underlying mechanisms of sintering by combining theory and experiment in a quantitative framework. While mechanistic sintering models exist, they only model a single sintering pathway, even though multiple sintering mechanisms can occur simultaneously or dominate at different stages of the process. Data-driven machine learning models have emerged as a means to represent complex processes through data regression. However, machine learning models have very large data needs and lack mechanistic insights due to their black-box encoding. To develop an interpretive model of catalyst degradation via sintering, we constructed a hybrid model combining mechanistic “physics-based” models and data-driven methods to obtain both reliable predictions and mechanistic insights regarding experimentally observed sintering phenomena. Focusing on nanoparticle sintering in the Rh–TiO 2 catalyst for the reverse water–gas shift (RWGS) reaction, the hybrid model couples a mechanistic term for Ostwald ripening with energy values calculated via density functional theory (DFT) with a parametric, data-driven discrepancy function term for unmodeled mechanisms. The hybrid model is trained using Bayesian inference with data collected from small-angle X-ray scattering (SAXS) in situ experiments wherein average nanoparticle diameter versus time was measured at three relevant operating temperatures. The calibrated hybrid model results show that an Ostwald ripening-only model parameterized with fixed DFT energies does not fully capture the time and temperature dependence of the SAXS-observed sintering kinetics, and that an additional functional contribution, or DFT energy calibration, is required to reconcile simulation and experiment. Analysis of the hybrid-model error confirms that the hybrid model outperforms both the purely mechanistic and purely data-driven alternatives in terms of expected predictive accuracy for time-evolving average particle sizes. Furthermore, the results support the hypothesis that the Ostwald ripening mechanism is less important for explaining the sintering phenomena as operating temperature increases under an assumed fixed DFT parameterization. This could be explained in one of two ways: either latent, unmodeled sintering mechanisms dominate at higher temperatures, or the DFT uncertainty increases with temperature. The proposed modeling approach directly links theory to experiments and simulations via a statistical hybrid modeling framework and can be extended to other catalytic systems to improve predictive models and mechanistic understanding.

Bayesian hybrid modeling↗
DOE OSTI2026

Coupling Redox Compensation and Interfacial Stabilization in Low-Ni O3-Type Sodium Layered Oxide Cathodes

Low-Ni O3-type sodium layered oxides are attractive cathodes for cost-robust sodium-ion batteries, yet high-voltage cycling is often limited by Fe-driven degradation, including cation migration/dissolution, irreversible slab gliding with large strain, particle cracking, and accelerated interfacial parasitic reactions. Here, in this study, we introduce a redox-interface codesign strategy using stoichiometric, charge-balanced Cu 2+ /Ti 4+ cosubstitution while preserving full Na stoichiometry, transitioning from NaNi 1/4 Fe 1/2 Mn 1/4 O 2 to NaNi 1/4 Fe 1/5 Mn 1/4 Cu 3/20 Ti 3/20 O 2 . With the cosubstitution, Cu and Ti suppress Fe migration and dissolution and facilitate sustained Fe oxidation at high voltage. Meanwhile, Cu is also shown to be redox-active, providing reversible cationic charge compensation that mitigates the capacity penalty typically associated with reducing Fe participation. Operando diffraction and spectroscopy collectively indicate a more reversible high-voltage structural evolution with suppressed Fe-related irreversibility. Particularly, spontaneous Ti enrichment at surface/grain-boundary regions stabilizes the cathode−electrolyte interface and promotes a more NaF-rich interphase signature. This work establishes a generalizable route to reconcile stability and capacity in low-Ni, Fe-containing O3 sodium layered oxide cathodes via compositionally encoded bulk-interfacial coupling.

25 ENERGY STORAGE↗
DOE OSTI2025

Advanced Polymer Characterization: Modular Operations for Spectral Alignment by Iterative Compression (MOSAIC)

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry encodes structural information across diverse homo- and copolymer ensembles, yet decrypting these spectra requires a systematic analytical approach. We introduce Modular Operations for Spectral Alignment by Iterative Compression (MOSAIC)─a general cipher algorithm that applies modular arithmetic to filter monomer-derived mass contributions and cluster MALDI peaks by nonconstitutional repeating units (non-CRUs). MOSAIC performs sequential modular operations using monomer mass differences as base units to compress complex spectral data, revealing end-group distributions and comonomer incorporation. As a demonstration, we applied MOSAIC to five copolymers formed by two different polymerization mechanisms. Furthermore, the resulting remainder–mass plots clearly resolve polymer homologs with distinct non-CRUs into visually apparent clusters, enabling intuitive assignment of mass spectral features.

Wang, Hanlin M. [University of Illinois at Urbana−↗
DOE OSTI2021

Disentangling Rotational Dynamics and Ordering Transitions in a System of Self-Organizing Protein Nanorods via Rotationally Invariant Latent Representations

The dynamics of complex ordering systems with active rotational degrees of freedom exemplified by protein self-assembly is explored using a machine learning workflow that combines deep learning-based semantic segmentation and rotationally invariant variational autoencoder-based analysis of orientation and shape evolution. The latter allows for disentanglement of the particle orientation from other degrees of freedom and compensates for lateral shifts. The disentangled representations in the latent space encode the rich spectrum of local transitions that can now be visualized and explored via continuous variables. The time dependence of ensemble averages allows insight into the time dynamics of the system and, in particular, illustrates the presence of the potential ordering transition. Finally, analysis of the latent variables along the single-particle trajectory allows tracing these parameters on a single-particle level. The proposed approach is expected to be universally applicable for the description of the imaging data in optical, scanning probe, and electron microscopy seeking to understand the dynamics of complex systems where rotations are a significant part of the process.

representation learning↗
DOE OSTI2021

Bayesian Learning of Adatom Interactions from Atomically Resolved Imaging Data

Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be captured by a generative physical model. In this work, we develop a workflow based on a machine-learning-based analysis of scanning tunneling microscopy images to reconstruct the atomic and adatom positions, and a Bayesian optimization procedure to minimize statistical distance between the chosen physical models and experimental observations. We optimize the parameters of a 2- and 3-parameter Ising model describing surface ordering and use the derived generative model to make predictions across the parameter space. For concentration dependence, we compare the predicted morphologies at different adatom concentrations with the dissimilar regions on the sample surfaces that serendipitously had different adatom concentrations. The proposed workflow can be used to reconstruct the thermodynamic models and associated uncertainties from the experimental observations of materials microstructures. The code used in the manuscript is available at https://github.com/saimani5/Adatom_interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.

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

Water, Solute, and Ion Transport in De Novo-Designed Membrane Protein Channels

Biological organisms engineer peptide sequences to fold into membrane pore proteins capable of performing a wide variety of transport functions. Synthetic de novo-designed membrane pores can mimic this approach to achieve a potentially even larger set of functions. Here, in this work, we explore water, solute, and ion transport in three de novo designed β-barrel membrane channels in the 5–10 Å pore size range. We show that these proteins form passive membrane pores with high water transport efficiencies and size rejection characteristics consistent with the pore size encoded in the protein structure. Ion conductance and ion selectivity measurements also show trends consistent with the pore size, with the two larger pores showing weak cation selectivity. MD simulations of water and ion transport and solute size exclusion are consistent with the experimental trends and provide further insights into structure–function correlations in these membrane pores.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Rethinking Suicide Thi4 Thiazole Synthases: Comparative Genomic Insights and Pilot Functional Evidence

Suicide thiazole synthases (Thi4) are mononuclear metal enzymes that form the thiazole moiety of thiamin from NAD + , glycine, and a sulfur atom that is stripped from an active-site cysteine residue, causing enzyme inactivation. Comparative genomic analysis shows that prokaryotic Thi4 genes often cluster on the chromosomal regions encoding ThiS, ThiF, and other proteins that can produce, relay, or use persulfide or thiocarboxylate sulfur. These recurring genomic associations raise the possibility that, in some microorganisms, suicide Thi4s may interact with sulfur-relay systems, i.e., they can possibly operate in a nonsuicide mode. This proof-of-concept study explores this possibility via complementation assays using Escherichia coli as a heterologous platform. A representative bacterial Thi4 that clustered with thiS and thiF complemented an E. coli ΔthiG (thiazole auxotroph) single mutant better than a ΔthiG ΔthiF ΔthiS triple mutant. Although (in)direct sulfur transfer could not be assessed in the scope of our investigation, the initial results suggest a dependence on host sulfur relay components, consistent with predicted interactions with the host sulfide transfer chain. Collectively, this new perspective provides a useful guide for future biochemical studies on alternative modes of action for “suicide Thi4s” and accessory proteins.

Bacteria↗
DOE OSTI2026

A Chimeric LBT-GFP Biosensor Exhibits Antithetical Fluorescence Responses to Ca 2+ and Dy 3+ Binding

Rare earth elements (REEs) are critical components in emerging technologies, but their mining and refining processes are often laborious, costly, and environmentally damaging. Developing green and efficient separation methods for REEs is crucial. Biomolecular approaches using lanthanide-binding proteins and peptides show promise for selective REE extraction and separation. In this study, we present the design and characterization of a genetically encoded fluorescence indicator (GEFI) construct that combines a superfolder green fluorescent protein (sfGFP) with a dual lanthanide-binding tag (2×dLBT). The 2×dLBT insert induces conformational changes in sfGFP upon lanthanide binding, modulating the fluorescence intensity. The sfGFP-2×dLBT biosensor exhibited distinct fluorescence responses to different lanthanide ions, with the highest dynamic range observed for heavy REEs like dysprosium (Dy 3+ ). Interestingly, the sensor displayed an antithetical response, where low concentrations of lanthanides initially quenched the fluorescence, but higher concentrations led to a significant fluorescence increase (1.5-fold). The Ca 2+ ion on the other hand showed only a dose-dependent quenching of the fluorescence response. Based on these observations, the biphasic response of the biosensor to lanthanides was eliminated by pretreating the sensor with calcium, which further expanded the dynamic range up to 3-fold for Dy 3+ . The lanthanide-selective and concentration-dependent fluorescence changes of the sfGFP-2×dLBT biosensor demonstrate its potential as a platform for developing specific sensors for various REEs. These sensors could enable rapid and cost-effective determination of REE composition in complex mixtures, facilitating the separation and recovery of critical REEs from electronic waste and other REE-containing sources.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Polarization-Insensitive Medium-Switchable Holographic Metasurfaces

The adoption of metasurfaces has led to revolutionary advances in holography due to improved compactness, integrability, and performance. Switchable meta-holograms projecting different replay field images in a controllable manner are highly desirable. Still, existing technologies generally rely on the use of polarized light and additional optics to facilitate switching. Consequently, the potential benefits afforded through the use of metasurfaces are limited both by the system complexity and a fixed relationship between the optical input and output. In this manuscript, we demonstrate polarization-insensitive metasurfaces encoding arbitrary and independent holograms, which can be switched between by changing the refractive index of the infiltration medium while maintaining identical illumination conditions. Finally, by sidestepping the requirements for high-performance light sources, switching optics, or delicate alignment, this approach points toward ultracompact and cost-effective switchable meta-holograms for various practical applications, such as holographic image projection, eye-perceptible sensors, optical information storage, processing, and security.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quorum-driven microbial consortium for Bioplastic production from agro-waste

Microbial consortia have high relevance in natural environments. Here we present the production of polyhydroxyalkanoates (PHA) from agro-industrial residues by a synthetic interkingdom consortium formed by the saprotrophic fungus Ophiostoma piceae CECT 20146, which encodes a wide range of lignocellulolytic enzymes, and a natural PHA producer, Pseudomonas putida KT2440. Two agro-industrial residues were utilized: Brewer's Spent Grain (BSG) as a carbon/nitrogen source and biofilm scaffold and waste cooking oil (WCO) as a carbon source for PHA synthesis. Through biochemistry, microscopy, and omics analyses, it is shown that P. putida accumulates up to 40.2% of intracellular PHA when the quorum sensing molecule, farnesol (naturally produced by O. piceae) is added, thanks to the increased proliferation of P. putida cells. An interactive Shiny application has also been developed for an easy visualization and comprehension of all the transcriptomics and metabolomics data: https://jgf-bioinformatics.shinyapps.io/Visualization_app/. These results support the increased PHA production of the consortium by an induction of gene phaG, which redirects intermediaries of the fatty acid biosynthesis to PHA precursors, and the repression of the PHA depolymerase phaZ in P. putida. The trophic interaction between microorganisms seems to rely on the citric acid produced by O. piceae and the glycerol liberated from WCO, which can both be consumed by P. putida. Bioreactor scale-up experiments allowed a 3.3-fold increase in the PHA concentration in the consortium (6.7 g·L-1) without pretreatment or sterilization of the substrates, laying the groundwork for the implementation of an industrial consolidated bioprocess (CBP).

Bacteria↗
DOE OSTI2025

Engineered Accumulation of Protocatechuate in Corn Biomass to Enhance Biomanufacturing

The in-planta accumulation of coproducts in crops can enhance the value of lignocellulosic biomass and facilitate a sustainable bioeconomy. Corn stover represents a major renewable source of lignocellulose for the production of advanced biofuels and bioproducts. In this study, we engineered corn with a bacterial gene encoding a dehydroshikimate dehydratase (QsuB) to overproduce protocatechuate (DHBA). Transgenic corn lines accumulate up to 2.9% DHBA on a dry weight basis in leaf and stem biomass. DHBA occurs in the form of glucosides that are extractable from biomass using aqueous methanol as the solvent. The analysis of lignin did not show any evidence for the incorporation of DHBA; however, an increase in the lignin syringyl to guaiacyl ratio and a higher relative abundance of p-coumarate groups compared with total lignin units were observed in QsuB-modified corn. Alkaline hydrolysates prepared from QsuB corn were enriched in DHBA compared to the hydrolysates obtained from wild-type biomass, which contained mostly p-coumarate and ferulate. Using engineered Novosphingobium aromaticivorans as a production host, a 375% improvement in 2-pyrone-4,6-dicarboxylate titers was achieved through biological upgrading of alkaline hydrolysates derived from QsuB corn compared to unmodified biomass. Our data demonstrate an engineering strategy to overproduce DHBA in corn that can facilitate sustainable manufacturing of other valuable bioproducts using stover as a feedstock.

2-pyrone-4,6-dicarboxylate↗
DOE OSTI2024

Exploring the Structural, Biochemical, and Functional Diversity of Glycoside Hydrolase Family 12 from Penicillium subrubescens

Glycoside hydrolases (GHs) play an essential role in plant biomass degradation and modification for the sustainable production of biochemicals. The filamentous Ascomycete fungus Penicillium subrubescens contains a higher number of GH12 candidates compared to related species. Therefore, we aimed to compare P. subrubescens GH12s for their ability and substrate specificity for plant cell wall polysaccharide degradation and species’ potential as a source of novel enzymes for plant biomass valorization. Our re-evaluated phylogenetic analysis of fungal GH12 members showed that the P. subrubescens GH12s were located in different (new) clades. Biochemical characterization marked PsEglA as an endoglucanase and four other P. subrubescens GH12s (i.e., PsXegA–D) as xyloglucanases. Interestingly, structural features of PsXegD and PsXegE were more comparable to those of Basidiomycete GH12 xyloglucanases with a unique open substrate-binding cleft. PsUegA displayed dual xyloglucanase and endoglucanase activity and also showed distinct structural features. Comparative transcriptome analysis supported the functional diversity of P. subrubescens GH12s in plant biomass degradation. The gene encoding PsUegA was expressed under diverse conditions, suggesting a scouting role for this enzyme.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗