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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Colloidal Dispersions of Sterically and Electrostatically Stabilized PbS Quantum Dots: Structure Factors, Second Virial Coefficients, and Film-Forming Properties

Electrostatically stabilized nanocrystals (NCs) and, in particular, quantum dots (QDs) hold promise for forming strongly coupled superlattices due to their compact and electronically conductive surface ligands. However, studies of the colloidal dispersion and interparticle interactions of electrostatically stabilized sub-10 nm NCs have been limited, hindering the optimization of their colloidal stability and self-assembly. In this study, we employed small-angle X-ray scattering (SAXS) experiments to investigate the interparticle interactions and arrangement of PbS QDs with thiostannate ligands (PbS-Sn2S64-) in polar solvents. The study reveals significant deviations from the ideal solution behavior in electrostatically stabilized QD dispersions. Our results demonstrate that PbS-Sn2S64- QDs exhibit long-range interactions within the solvent, in contrast to the short-range steric repulsion characteristic of PbS QDs with oleate ligands (PbS-OA). Introducing highly charged multivalent electrolytes screens electrostatic interactions between charged QDs, reducing the length scale of the repulsive interactions. Furthermore, we calculated the second virial (B2) coefficients from SAXS data, providing insights into how surface chemistry, solvent, and size influence pair potentials. Finally, we explore the influence of long-range interparticle interactions of PbS-Sn2S64- QDs on the morphology of films produced by drying or spin-coating colloidal solutions. The long-range repulsive term of PbS-Sn2S64- QDs promotes the formation of amorphous films, and screening the electrostatic repulsion by the addition of an electrolyte enables the formation of crystalline domains. These findings highlight the critical role of NC-NC interactions in tailoring the properties of functional materials made of colloidal NCs.

SAXS↗
DOE OSTI2026

Ultrafast Nanoimaging of Carrier Funneling in Composition-Graded Semiconductor Nanowires

Recent advances in bandgap engineering of low-dimensional semiconductors have enabled high-efficiency carrier transport in miniaturized electronic and optoelectronic devices. The physical properties and functionalities of these materials are governed by complex carrier dynamics coupled with multiple transport mechanisms in tailored band structures. Here, we report ultrafast nanoimaging of carrier funneling and recombination in composition-grade CdSxSe1-x nanowires using pump-probe near-field nanoscopy. Leveraging the high resolution of this technique in both space and time, we resolve nanoscale local carrier dynamics along composition-graded nanowires, revealing the local variation of composition-dependent carrier mobilities and lifetimes that significantly differ from their uniform composition counterparts. Furthermore, we demonstrate a length-dependent behavior wherein shorter nanowires exhibit enhanced funneling effects, accelerating carrier transport by up to 33%. Our findings provide direct visualization of nanoscale carrier transport while supporting an effective approach for investigating complex carrier interactions in inhomogeneous semiconductor nanostructures, with implications for optimizing next-generation optoelectronic devices.

Yang, Rundi↗
DOE OSTI2020

Revealing High Na-Content P2-Type Layered Oxides as Advanced Sodium-Ion Cathodes

Layered Na-based oxides with the general composition of Na x TMO 2 (TM: transition metal) have attracted significant attention for their high compositional diversity that provides tunable electrochemical performance for electrodes in sodium-ion batteries. The various compositions bring forward complex structural chemistry that is decisive for the layered stacking structure, Na-ion conductivity, and the redox activity, potentially promising new avenues in functional material properties. In this work, we have explored the maximum Na content in P2-type layered oxides and discovered that the high-content Na in the host enhances the structural stability; moreover, it promotes the oxidation of low-valent cations to their high oxidation states (in this case Ni 2+ ). This can be rationalized by the increased hybridization of the O(2p)-TM(3d-e g *) states, affecting both the local TM environment as well as the interactions between the NaO 2 and TMO 2 layers. These properties are highly beneficial for the Na storage capabilities as required for cathode materials in sodium-ion batteries. It leads to excellent Na-ion mobility, a large storage capacity (>100 mAh g –1 between 2.0-4.0 V), yet preventing the detrimental sliding of the TMO 2 layers (P2–O2 structural transition), as reflected by the ultralong cycle life (3000 (dis)charge cycles demonstrated). These findings expand the horizons of high Na-content P2-type materials, providing new insights of the electronic and structural chemistry for advanced cathode materials.

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

Uncertainty-aware mixed-variable machine learning for materials design

Abstract Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models’ predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Impact of CuInP 2 S 6 –metal interfaces on the stabilization of polar phases and polarization switching

The multifunctionality of two-dimensional ferroelectric CuInP 2 S 6 (CIPS) arises from the existence of multiple polar phases combined with a high ionic conductivity that facilitates polarization switching in unusual ways. The van der Waals (vdW) layered structure provides ultrathin flakes and ideal interfaces to integrate with other materials for microelectronics and neuromorphic elements. However, device integration necessitates metal contacts to read, write, or transmit signals. In this work, we find that different types of metal–CIPS interfaces strongly impact the stabilization of specific polar phases and the field-induced transitions between the polarization states. Cu electrodes initially suppress the piezoresponse, whereas, at CIPS–Ag interfaces, the electromechanical signal is increased. Under electric fields, the Cu electrodes, Ag electrodes and surrounding CIPS surfaces can show distinct switching behavior as different phases and polarization orientations are stabilized. These findings highlight that metal–CIPS interfaces provide the opportunity to optimize functional material properties.

Ferroelectrics and multiferroics↗
DOE OSTI2021

Decorated networks of native proteins: nanomaterials with tunable mesoscopic domain size

Natural and artificial proteins with designer properties and functionalities offer unparalleled opportunity for functional nanoarchitectures formed through self-assembly. However, to exploit this potential we need to design the system such that assembly results in desired architecture forms while avoiding denaturation and therefore retaining protein functionality. Here we address this challenge with a model system of fluorescent proteins. By manipulating self-assembly using techniques inspired by soft matter where interactions between the components are controlled to yield the desired structure, we have developed a methodology to assemble networks of proteins of one species which we can decorate with another, whose coverage we can tune. Consequently, the interfaces between domains of each component can also be tuned, with potential applications for example in energy – or electron – transfer. Our model system of eGFP and mCherry with tuneable interactions reveals control over domain sizes in the resulting networks.

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

Generative AI for design of nanoporous materials: review and future prospects

Generative artificial intelligence (AI) is emerging as a powerful tool for advancing the design of nanoporous materials such as metal–organic frameworks, covalent–organic frameworks, and zeolites. These materials have potential application in important areas such as carbon capture, catalysis, gas storage, chemical separation, and drug delivery due to their modular, tunable structures, and their performance in these areas depends on precise control over their structure, chemical functionalities, and properties. Herein, we provide a review of generative AI algorithms that are emerging as powerful tools for the design of nanoporous materials, namely generative adversarial networks, variational autoencoders, diffusion models, genetic algorithms, reinforcement learning, and large language models. Some models are particularly good at generating diverse and high-quality designs, while others excel at exploring large design spaces or optimizing materials with desired properties. Certain algorithms also allow for efficient transitions between different designs, and some offer versatility in generating materials based on textual input. We discuss the advantages, limitations, and applications of these algorithms in porous material design and emphasize the future potential of integrating AI with experimental workflows to accelerate the development and validation of AI-generated materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Tailoring magnetization reversal of a single-domain bar nanomagnet via its end geometry

Nanoscale single-domain bar magnets are building blocks for a variety of fundamental and applied mesoscopic magnetic systems, such as artificial spin ices, magnetic shape-morphing microbots, and magnetic majority logic gates. The magnetization reversal switching field of the bar nanomagnets is a crucial parameter that determines the physical properties and functionalities of their constituted artificial systems. Previous methods on tuning the magnetization reversal switching field of a bar nanomagnet usually relied on modifying its aspect ratio, such as its length, width, and/or thickness. Here, we show that the switching field of a bar nanomagnet saturates when extending its length beyond a certain value, preventing further tailoring of the magnetization reversal via aspect ratios. We showcase a highly tunable switching field of a bar nanomagnet by tailoring its end geometry without altering its size. This provides an easy method to control the magnetization reversal of a single-domain bar nanomagnet. It would enable new research and/or applications, such as designing artificial spin ices with additional tuning parameters, engineering magnetic microbots with more flexibility, and developing magnetic quantum-dot cellular automata systems for low power computing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Reference Correlations for the Density and Viscosity of Molten Alkali and Alkaline Earth Fluoride Salts

While there is a significant body of literature pertaining to thermophysical property measurements of molten salts, there is often a wide degree of variability among independent measurements of the same compounds. As such, the scientific community benefits greatly from an unbiased, independent assessment of duplicate datasets, so that reference correlations which describe these thermophysical properties as functions of temperature can be determined and then commonly used by researchers, scientists, and engineers. With regard to molten fluoride compounds, a significant time has elapsed since density and viscosity reference correlations have been determined; Janz conducted the most recent effort, in 1988, to provide reference correlations for the densities and viscosities of molten fluoride compounds via the National Standard Reference Data System coordinated by the National Bureau of Standards. Since then, new data have been published for molten fluoride compounds, and a new precedent has surfaced for putting forth reference correlations that involve fitting to multiple primary datasets. In this work, reference correlations are put forth for molten alkali and alkaline earth fluoride compounds in an effort to provide updated, improved correlations for general use. For molten alkali fluoride densities, estimated uncertainties with a 95% confidence interval are summarized as follows: LiF (0.63%), NaF (0.48%), KF (0.76%), RbF (0.93%), and CsF (0.75%). For molten alkaline earth fluoride densities, an estimated uncertainty was not able to be quantified for BeF 2 because of limited data; however, estimated uncertainties with a 95% confidence interval are summarized as follows for the remaining alkaline earth fluorides: MgF 2 (1.5%), CaF 2 (0.92%), SrF 2 (1.6%), and BaF 2 (0.23%). For molten alkali fluoride viscosities, uncertainty was not able to be quantified for RbF and CsF because of limited data; however, estimated uncertainties with a 95% confidence interval are summarized as follows for the remaining alkali fluorides: LiF (4.4%), NaF (3.0%), and KF (4.0%). For molten alkaline earth fluoride viscosities, limited consistent data resulted in the recommendation of single datasets (from literature) that are deemed to be the most trustworthy based on the quality of the underlying experimental studies.

Birri, A. [Oak Ridge National Laboratory (ORNL), O↗
DOE OSTI2022

Disturbances in North American boreal forest and Arctic tundra: impacts, interactions, and responses

Ecosystems in the North American Arctic-Boreal Zone (ABZ) experience a diverse set of disturbances associated with wildfire, permafrost dynamics, geomorphic processes, insect outbreaks and pathogens, extreme weather events, and human activity. Climate warming in the ABZ is occurring at over twice the rate of the global average, and as a result the extent, frequency, and severity of these disturbances are increasing rapidly. Disturbances in the ABZ span a wide gradient of spatiotemporal scales and have varying impacts on ecosystem properties and function. However, many ABZ disturbances are relatively understudied and have different sensitivities to climate and trajectories of recovery, resulting in considerable uncertainty in the impacts of climate warming and human land use on ABZ vegetation dynamics and in the interactions between disturbance types. Here we review the current knowledge of ABZ disturbances and their precursors, ecosystem impacts, temporal frequencies, spatial extents, and severity. We also summarize current knowledge of interactions and feedbacks among ABZ disturbances and characterize typical trajectories of vegetation loss and recovery in response to ecosystem disturbance using satellite time-series. We conclude with a summary of critical data and knowledge gaps and identify priorities for future study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

pKPDB: a protein data bank extension database of p Ka and pI theoretical values

Abstract Summary pKa values of ionizable residues and isoelectric points of proteins provide valuable local and global insights about their structure and function. These properties can be estimated with reasonably good accuracy using Poisson–Boltzmann and Monte Carlo calculations at a considerable computational cost (from some minutes to several hours). pKPDB is a database of over 12 M theoretical pKa values calculated over 120k protein structures deposited in the Protein Data Bank. By providing precomputed pKa and pI values, users can retrieve results instantaneously for their protein(s) of interest while also saving countless hours and resources that would be spent on repeated calculations. Furthermore, there is an ever-growing imbalance between experimental pKa and pI values and the number of resolved structures. This database will complement the experimental and computational data already available and can also provide crucial information regarding buried residues that are under-represented in experimental measurements. Availability and implementation Gzipped csv files containing p Ka and isoelectric point values can be downloaded from https://pypka.org/pKPDB. To query a single PDB code please use the PypKa free server at https://pypka.org. The pKPDB source code can be found at https://github.com/mms-fcul/pKPDB. Supplementary information Supplementary data are available at Bioinformatics online.

Reis, Pedro B. P. S. (ORCID:0000000335636239)↗
DOE OSTI2023

Theoretical Analysis of Resonant Tunneling Enhanced Field Emission

In this paper, we develop an exact analytical quantum theory for field emission from surfaces with a nearby quantum well, by solving the one-dimensional time-independent Schrödinger equation. The quantum well, which may be introduced by ions, atoms, nanoparticles, etc., is simplified as a square potential well with depth H, width d, and distance to the surface L. The theory is used to analyze the effects of the quantum well (d, H, and L), the cathode properties (work function W and Fermi energy E F ), and dc field F. It is found that the quantum well can lead to resonant tunneling enhanced field emission up to several orders of magnitude larger than that from bare cathode surfaces. In the meantime, the electron-emission-energy spectrum is significantly narrowed. The strong enhancement region is bounded by the conditions eFL + H ≥ W + C and eFL ≤ W, with e being the elementary charge (positive) and C a constant dependent on dc field F. It is also found that the linear shift of resonance peaks in the electron-emission-energy spectrum with dc field F follows ε p =ε p⁢0 –e⁢FL, with ε p⁢0 being approximately the eigenenergies for electrons confined in a square potential well without a dc field. Finally, the theory provides insights for the design of high-efficiency field emitters, which can produce a high current and highly collimated electron beams.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Topological descendants of a multicritical Dirac semimetal with magnetism and strain

The past decades have witnessed a transformation in characterizing condensed matter systems with topology. Aided by a refined understanding of topological band structures with crystalline symmetries that has emerged recently, many electronic phases have been identified, and a plethora of materials have been predicted to host properties and functionalities. A key underlying question, also with respect to future application, is to what extent the related physical features can be manipulated, especially in the context of magnetic order. Here, we describe a paradigmatic semimetal that simultaneously incorporates multiple and sometimes conflicting topology which guarantees gaplessness and leads to an exceptionally rich family of descendent phases on lowering symmetry. We predict that this multicritical phase is realized in EuTl 2 . Starting from the parent semimetallic state, which already separates two topological insulating regimes, the interplay of inherent magnetism and strain allows for an exceptionally rich phase diagram of topological descendant states.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Crystallizing Kagome Artificial Spin Ice

Artificial spin ices are engineered arrays of dipolarly coupled nanobar magnets. They enable direct investigations of fascinating collective phenomena from their diverse microstates. However, experimental access to ground states in the geometrically frustrated systems has proven difficult, limiting studies and applications of novel properties and functionalities from the low energy states. In this work, we introduce a convenient approach to control the competing diploar interactions between the neighboring nanomagnets, allowing us to tailor the vertex degeneracy of the ground states. We achieve this by tuning the length of selected nanobar magnets in the spin ice lattice. We demonstrate the effectiveness of our method by realizing multiple low energy microstates in a kagome artificial spin ice, particularly the hardly accessible long range ordered ground state—the spin crystal state. Our strategy can be directly applied to other artificial spin systems to achieve exotic phases and explore new emergent collective behaviors.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Physics-informed machine learning analysis for nanoscale grain mapping by synchrotron Laue microdiffraction

Understanding the grain morphology, orientation distribution and crystal structure of nanocrystals is essential for optimizing the mechanical and physical properties of functional materials. Synchrotron X-ray Laue microdiffraction is a powerful technique for characterizing crystal structures and orientation mapping using focused X-rays. However, when the grain sizes are smaller than the beam size, mixed peaks in the Laue pattern from neighboring grains limit the resolution of grain morphology mapping. We propose a physics-informed machine learning (PIML) approach that combines a convolutional neural network feature extractor with a physics-informed filtering algorithm to overcome the spatial resolution limits of X-rays, achieving nanoscale resolution for grain mapping. Our PIML method successfully resolves the grain size, orientation distribution and morphology of Au nanocrystals through synchrotron microdiffraction scans, showing good agreement with electron backscatter diffraction results. This PIML-assisted synchrotron microdiffraction analysis can be generalized to other diffraction-based probes, enabling the characterization of nanosized structures with micrometre-sized probes.

X-ray crystallography↗
DOE OSTI2025

Symmetries of all lines in monolayer crystals

As `2D' materials (i.e. materials just a few atoms thick) continue to gain prominence, understanding their symmetries is critical for unlocking their full potential. In this work, we present comprehensive tables that tabulate the rod group symmetries of all crystallographic lines in all 80 layer groups, which describe the symmetries of 2D materials. These tables are analogous to the scanning tables for space groups found in Volume E of the International Tables for Crystallography, but are specifically tailored for layer groups and their applications to 2D materials. This resource will aid in the analysis of line defects, such as domain walls, which play a crucial role in determining the properties and functionality of 2D materials.

2D materials↗
DOE OSTI2020

Microbial macroecology: In search of mechanisms governing microbial biogeographic patterns

Abstract Introduction Rapidly advancing technologies and accumulating information about microbial communities across the globe allow the quantification of microbial properties and functions at a macro‐scale. These emerging microbial biogeographic patterns call for a practical macroecological approach to investigate their underlying mechanisms. Aims The primary aims of this paper are to review the advancements of microbial macroecology in seeking mechanisms governing microbial biogeographic patterns, and to further lay out a roadmap for microbial macroecology in 10 years. Methods We reviewed the progress of microbial macroecology and demonstrated the application of the microbial macroecological approach to microbial biogeographic patterns with three case studies. Results Microbial macroecology provides a platform for understanding microbial abundance, community structure, and functioning across space, time, and taxonomic hierarchy. It emphasizes the integral effects of environmental filtering, microbial responses, diversification, dispersal, and local extinction that drive the microbial biogeographic patterns. The microbial macroecological approach emphasizes the last two stages of the four‐stage scientific method applied to microbial ecology: (a) describing microbial traits across scales to reveal patterns, (b) mathematically representing these patterns, (c) developing and testing conceptual models to build a mechanistic understanding of these patterns from a macroecological perspective, (d) plugging the new knowledge into the theoretical advancements. Three case studies were used to demonstrate the microbial macroecological approach for understanding the global patterns of microbial biomass carbon, microbial composition (fungi : bacteria ratio), and microbial carbon use efficiency. Conclusions Microbial macroecology offers a platform for understanding the mechanisms that drive biogeographic patterns of microbial abundance, diversity and functions. It is likely that these patterns and mechanisms will be increasingly incorporated into predictive models that link climate, carbon dynamics, and biogeochemical processes. A roadmap is outlined for the growing microbial macroecology field; we expect significant progress will be made in five research directions over the next 10 years.

Xu, Xiaofeng↗