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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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

microRNAs (miR 9, 124, 155 and 224) transdifferentiate mouse macrophages to neurons

Highlights: • miR 9, miR 124 and miR155 could reprogramme macrophages to mature neurons. • miR-induced neuronal reprogramming involves stem cell-like intermediate phenotype. • miR 9, miR 124 and miR155 could also reprogramme CD133+ stem cells to mature neurons. Development is an irreversible process of differentiating the undifferentiated cells to functional cells. Brain development involves generation of cells with varied phenotype and functions, which is limited during adulthood, stress, damage/degeneration. Cellular reprogramming makes differentiation reversible process with reprogramming somatic/stem cells to alternative fate with/without stem cells. Exogenously expressed transcription factors or small molecule inhibitors have driven reprogramming of stem/somatic cells to neurons providing alternative approach for pre-clinical/clinical testing and therapeutics. Here in, we report a novel approach of microRNA (miR)- induced trans-differentiation of macrophages (CD11b high) to induced neuronal cells (iNCs) (neuronal markershigh- Nestin, Nurr1, Map2, NSE, Tubb3 and Mash1) without exogenous use of transcription factors. miR 9, 124, 155 and 224 successfully transdifferentiated macrophages to neurons with transient stem cell-like phenotype. We report trans differentiation efficacy 18% and 21% with miR 124 and miR 155. in silico(String 10.0, miR gator, mESAdb, TargetScan 7.0) and experimental analysis indicate that the reprogramming involves alteration of pluripotencygenes like Oct4, Sox2, Klf4, Nanog and pluripotency miR, miR 302. iNCs also shifted to G0 phase indicating manipulation of cell cycle by these miRs. Further, CD133+ intermediate cells obtained during current protocol could be differentiated to iNCs using miRs. The syanpsin{sup +} neurons were functionally active and displayed intracellular Ca{sup +2} evoke on activation. miRs could also transdifferentiate bone marrow-derived macrophages and peripheral blood mononuclear cells to neuronal cells. The current protocol could be employed for direct in vivo reprogramming of macrophages to neurons without teratoma formation for transplantation and clinical studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Light-up and FRET aptamer reporters; evaluating their applications for imaging transcription in eukaryotic cells

The regulation of RNA transcription is central to cellular function. Changes in gene expression drive differentiation and cellular responses to events such as injury. RNA trafficking can also have a large impact on protein expression and its localization. Thus, the ability to image RNA transcription and trafficking in real time and in living cells is a worthwhile goal that has been difficult to achieve. The availability of “light-up” aptamers that cause an increase in fluorescence of their ligands when bound by the aptamer have shown promise for reporting on RNA production and localization in vivo. Here we have investigated two light-up aptamers (the malachite green aptamer and the Spinach aptamers) for their suitabilities as reporters of RNA expression in vivo using two eukaryotic cell types, yeast and mammalian. Our analysis focused on the aptamer ligands, their contributions to background noise, and the impact of tandem aptamer strings on signal strength and ligand affinity. Whereas the background fluorescence is very low in vitro, this is not always true for cell imaging. Our results suggest the need for caution in using light-up aptamers as reporters for imaging RNA. In particular, images should be collected and analyzed by operators blinded to the sample identities. The appropriate control condition of ligand with the cells in the absence of aptamer expression must be included in each experiment. This control condition establishes that the specific interaction of ligand with aptamer, rather than nonspecific interactions with unknown cell elements, is responsible for the observed fluorescent signals. As a result, high background signals due to nonspecific interactions of aptamer ligands with cell components can be minimized by using IMAGEtags (Intracellular Multiaptamer GEnetic tags), which signal by FRET and are promising RNA reporters for imaging transcription.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis

Molecular design is a critical aspect of various scientific and industrial fields, where the properties of molecules hold significant importance. In this study, a 3-fold methodology design is presented that leverages the power of generative artificial intelligence (AI), predictive modeling, and reinforcement learning to create tailored molecules with desired properties. This model synergistically combines deep learning techniques with Self-Referencing Embedded Strings (SELFIES) molecular representation to build a generative model that generates valid molecules and a graphical neural network model that accurately forecasts molecular properties. The Variational Autoencoder (VAE) coupled with reinforcement learning helps refine molecule generation based on targeted attributes. Data from an experimental study involving surfactants were used to test the framework. A validation of the structural integrity of the molecules generated was conducted, and Tanimoto similarities were used to quantify the similarity and diversity between the original and generated molecular structures. Also, saliency maps for the generated surfactants were produced to identify the features explaining the property values. Lastly, molecular dynamics simulations were used to validate the stability of the generated molecules. The results showed that the proposed framework can effectively produce valid molecules within the set property threshold value.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Low-Frequency Phonon Driven Negative Thermal Expansion in Cubic GaFe(CN) 6 Prussian Blue Analogues

Here, the understanding of the negative thermal expansion (NTE) mechanism is vital not only for the development of new NTE compounds but also for effectively controlling thermal expansion. Here, we report an interesting isotropic NTE property in cubic GaFe(CN) 6 Prussian blue analogues (α l = –3.95 × 10 –6 K –1 , 100–475 K), which is a new example to understand the complex NTE mechanism. A combined study of synchrotron X-ray diffraction, X-ray total scattering, X-ray absorption fine structure, neutron powder diffraction, and density functional theory calculations shows that the NTE of GaFe(CN)6 originates from the low-frequency phonons (< ~100 cm –1 ), which are directly related to the transverse vibrations of the atomic –Ga–N≡C–Fe– chains. Both the Ga–N and Fe–C chemical bonds are much softer to bend than to stretch. The direct evidence that transverse vibrational contribution to the NTE of GaFe(CN) 6 is dominated by N, instead of C atoms, is illustrated. It is interesting to find that the polyhedra of GaFe(CN) 6 are not rigid, which is a starting assumption in some models describing the NTE properties of other systems. The NTE mechanism can be vividly described by the “guitar-string” effect, which would be the common feature for the NTE property of many open-framework functional materials, such as Prussian blue analogues, oxides, cyanides, metal–organic frameworks, and zeolites.

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

Creation of Polymer Datasets with Targeted Backbones for Screening of High-Performance Membranes for Gas Separation

A simple approach was developed to computationally construct a polymer dataset by combining simplified molecular-input line-entry system (SMILES) strings of a targeted polymer backbone and a variety of molecular fragments. This method was used to create 14 polymer datasets by combining seven polymer backbones and molecules from two large molecular datasets (MOSES and QM9). Polymer backbones that were studied include four polydimethylsiloxane (PDMS) based backbones, poly(ethylene oxide) (PEO), poly(allyl glycidyl ether) (PAGE), and polyphosphazene (PPZ). The generated polymer datasets can be used for various cheminformatics tasks, including high-throughput screening for gas permeability and selectivity. This study utilized machine learning (ML) models to screen the polymers for CO2/CH4 and CO2/N2 gas separation using membranes. Several polymers of interest were identified. Here the results highlight that employing an ML model fitted to polymer selectivities leads to higher accuracy in predicting polymer selectivity compared to using the ratio of predicted permeabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Graph-Based Approaches for Predicting Solvation Energy in Multiple Solvents: Open Datasets and Machine Learning Models

The solvation properties of molecules, often estimated using quantum chemical simulations, are important in the synthesis of energy storage materials, drugs, and industrial chemicals. Here, we develop machine learning models of solvation energies to replace expensive quantum chemistry calculations with inexpensive-to-compute message-passing neural network models that require only the molecular graph as inputs. Our models are trained on a new database of solvation energies for 130,258 molecules taken from the QM9 dataset computed in five solvents (acetone, ethanol, acetonitrile, dimethyl sulfoxide, and water) via an implicit solvent model. Our best model achieves a mean absolute error of 0.5 kcal/mol for molecules with nine or fewer non-hydrogen atoms and 1 kcal/mol for molecules with between 10 and 14 non-hydrogen atoms. We make the entire dataset of 651,290 computed entries openly available and provide simple web and programmatic interfaces to enable others to run our solvation energy model on new molecules. This model calculates the solvation energies for molecules using only the SMILES string and also provides an estimate of whether each molecule is within the domain of applicability of our model. We envision that the dataset and models will provide the functionality needed for the rapid screening of large chemical spaces to discover improved molecules for many applications.

25 ENERGY STORAGE↗
DOE OSTI2023

Efficient 3D Molecular Design with an E(3) Invariant Transformer VAE

This work introduces a three-dimensional (3D) invariant graph-to-string transformer variational autoencoders (VAE) (Vagrant) for generating molecules with accurate density functional theory (DFT)-level properties. Vagrant learns to model the joint probability distribution of a 3D molecular structure and its properties by encoding molecular structures into a 3D-aware latent space. Directed navigation through this latent space implicitly optimizes the 3D structure of a molecule, and the latent embedding can be used to condition a generative transformer to predict the candidate structure as a one-dimensional (1D) sequence. Additionally, we introduce two novel sampling methods that exploit the latent characteristics of a VAE to improve performance. Here, we show that our method outperforms comparable 3D autoregressive and diffusion methods for predicting quantum chemical property values of novel molecules in terms of both sample quality and computational efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Fundamental Link between β Relaxation, Excess Wings, and Cage-Breaking in Metallic Glasses

In glassy materials, the Johari–Goldstein secondary (β) relaxation is crucial to many properties as it is directly related to local atomic motions. However, a long-standing puzzle remains elusive: why some glasses exhibit β relaxations as pronounced peaks while others present as unobvious excess wings? Using microsecond atomistic simulation of two model metallic glasses (MGs), we demonstrate that such a difference is associated with the number of string-like collective atomic jumps. Relative to that of excess wings, we find that MGs having pronounced β relaxations contain larger numbers of such jumps. Structurally, they are promoted by the higher tendency of cage-breaking events of their neighbors. Furthermore, our results provide atomistic insights for different signatures of the β relaxation that could be helpful for understanding the low-temperature dynamics and properties of MGs.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Thermodynamic Route to Efficient Prediction of Gas Diffusivity in Nanoporous Materials

We report an efficient computational procedure for rapid and accurate prediction of the self-diffusivity of gas molecules in nanoporous materials by implementing the transition state theory for intercage hopping at infinite dilution with the string method in conjunction with the excess-entropy scaling for predicting gas diffusion coefficients at finite loadings. The theoretical procedure has been calibrated with molecular dynamics simulations for the diffusion coefficients of methane and hydrogen gases in representative nanoporous materials including metal organic frameworks and zeolites. Combined with the classical density functional theory for calculating the excess entropy of gas molecules in micropores, the theoretical procedure enables efficient computation of both thermodynamic and transport properties important for design and screening of nanostructured materials for gas storage and separation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Binary Superlattice Design by Controlling DNA-Mediated Interactions

Most binary superlattices created using DNA functionalization rely on particle size differences to achieve compositional order and structural diversity. Here we study two-dimensional (2D) assembly of DNA-functionalized micron-sized particles (DFPs), and employ a strategy that leverages the tunable disparity in interparticle interactions, and thus enthalpic driving forces, to open new avenues for design of binary superlattices that do not rely on the ability to tune particle size (i.e., entropic driving forces). Our strategy employs tailored blends of complementary strands of ssDNA to control interparticle interactions between micron-sized silica particles in a binary mixture to create compositionally diverse 2D lattices. We show that the particle arrangement can be further controlled by changing the stoichiometry of the binary mixture in certain cases. With this approach, we demonstrate the ability to program the particle assembly into square, pentagonal, and hexagonal lattices. In addition, different particle types can be compositionally ordered in square checkerboard and hexagonal–alternating string, honeycomb, and Kagome arrangements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Grain Growth and Coarsening Dynamics in a Compositionally Asymmetric Block Copolymer Revealed by X-ray Photon Correlation Spectroscopy

The dynamics of nanostructured soft materials crucially impact their associated macroscopic material properties, yet they are often difficult to measure due to spatiotemporal limitations of conventional instrumentation. Herein, we use X-ray photon correlation spectroscopy (XPCS) to directly observe particle-scale dynamics during grain growth and coarsening in a BCC- forming diblock polymer melt, with specific attention to the distribution of structural relaxation times associated with the interplanar (110) distance. Following sample quenching from the disordered state, these dynamical phenomena surprisingly exhibit little dependence on time and thermal quench depth. We posit that these relaxations stem from collective particle motions during grain rotation. We also observe unusual internally-referenced heterodyne correlations, which enable measurements of speed distributions within the sample. In this work, these speeds are significantly slower and appear at much longer annealing times than those previously reported during grain nucleation and growth in microphase separated block polymer melts. Drawing on analogies between polycrystalline hard and soft materials, we ascribe these speed distributions to misorientation-dependent grain boundary migration during ordered domain coarsening and anomalously fast, cooperative string-like particle motion along the grain boundaries. Thus, these coherent X-ray measurements provide new opportunities to interrogate grain boundary structure and dynamics in polycrystalline soft materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Mechanisms of Directed Self-Assembly in Cylindrical Hole Confinements

We report the directed self-assembly of block copolymers in cylindrical holes is a promising technology for lithographic patterning, particularly in the context of vertical interconnect accesses. While the hole-shrink process for single cylinders has been extensively explored, the proliferation of morphological defects remains a significant technological barrier. We use a coarse grained model to explore morphologies that form within cylindrical confinements for combinations of template surface energies. We identify metastable defect morphologies, in addition to the desired cylindrical morphology, in majority-wetting sidewall templates. We use our coarse-grained model and the string method to identify transition pathways between defective morphologies and the cylindrical morphology to elucidate the mechanism of defect annihilation within the confinements; the transition pathway from a disordered state is also identified. Lastly, this work demonstrates that the minimum free energy path for the formation of a cylinder goes through defective morphologies and that designing confinements can eliminate these undesirable transition states.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mechanisms of Skyrmion and Skyrmion Crystal Formation from the Conical Phase

Real-space topological magnetic structures such as skyrmions and merons are promising candidates for information storage and transport. However, the microscopic mechanisms that control their formation and evolution are still unclear. In this work, using in situ Lorentz transmission electron microscopy, we demonstrate that skyrmion crystals (SkXs) can nucleate, grow, and evolve from the conical phase in the same ways that real nanocrystals form from vapors or solutions. More intriguingly, individual skyrmions can also “reproduce” by division in a mitosis-like process that allows them to annihilate SkX lattice imperfections, which is not available to crystals made of mass-conserving particles. Combined string method and micromagnetic calculations show that competition between repulsive and attractive interactions between skyrmions governs particle-like SkX growth, but nonconservative SkX growth appears to be defect mediated. Our results provide insights toward manipulating magnetic topological states by applying established crystal growth theory, adapted to account for the new process of skyrmion mitosis.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Electron Dose-Controlled Formation, Growth, and Assembly of Nanoclusters and Nanoparticles from Aurophilic Au(I)–Thiolate Ensemble on Surfaces

The ability to precisely control electron irradiationinduced formation, growth, and assembly of nanoclusters or nanoparticles on a solid surface is important for design and creation of catalytically or chemically active surface sites and interfaces free from chemical reducing agents. Here, we show the results of an investigation of the electron dose-controlled formation, growth, and assembly of nanoclusters and nanoparticles in a molecularly assembled thin film of Au(I)–thiolate motifs on a substrate, highlighting an in situ monitoring of the evolution of morphology under controlled electron dose. With aurophilic motifs of Au(I)–thiolate being confined by electrostatic interactions, the sizes of Au nanoclusters and nanoparticles were shown to increase with electron dose, revealing a propensity of a string alignment of the grown nanoclusters and nanoparticles. This growth preference to onedimensional assembly is supported by the analysis of the surface reaction kinetics in terms of the surface density of electron dose for the growth of the nanoclusters and nanoparticles. Here, the electron dose-controlled size-focusing and directional assembly of nanoclusters and nanoparticles may be exploited as new strategy for the precise control of nanoclusters or nanoparticles and their assemblies on solid surfaces not only free from chemical reducing agent but also with the ability of visual monitoring of the morphological evolution during growth.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Finding Chemical Reaction Paths with a Multilevel Preconditioning Protocol

Finding transition paths for chemical reactions can be computationally costly owing to the level of quantum-chemical theory needed for accuracy. Here, we show that a multilevel preconditioning scheme that was recently introduced (Tempkin et al. J. Chem. Phys. 2014, 140, 184114) can be used to accelerate quantum-chemical string calculations. We demonstrate the method by finding minimum-energy paths for two well-characterized reactions: tautomerization of malonaldehyde and Claissen rearrangement of chorismate to prephanate. For these reactions, we show that preconditioning density functional theory (DFT) with a semiempirical method reduces the computational cost for reaching a converged path that is an optimum under DFT by several fold. In conclusion, the approach also shows promise for free energy calculations when thermal noise can be controlled.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Modes of Disorder in Poly(carbon monofluoride)

Poly(carbon monofluoride), or (CF) n , is a layered fluorinated graphite material consisting of nanosized platelets. In this work, we present experimental multidimensional solid-state NMR spectra of (CF)n, supported by density functional theory (DFT) calculations of NMR parameters, which overhauls our understanding of structure and bonding in the material by elucidating many ways in which disorder manifests. We observe strong 19 F NMR signals conventionally assigned to elongated or “semi-ionic” C–F bonds and find that these signals are in fact due to domains where the framework locally adopts boat-like cyclohexane conformations. We calculate that C–F bonds are weakened but are not elongated by this conformational disorder. Exchange NMR suggests that conformational disorder avoids platelet edges. We also use a new J-resolved NMR method for disordered solids which provides molecular-level resolution of highly fluorinated edge states. Strings of consecutive difluoromethylene groups at edges are relatively mobile. Topologically distinct edge features, including zigzag edges, crenellated edges, and coves are resolved in our samples by solid-state NMR. Disorder should be controllable in a manner dependent on synthesis, affording new opportunities for tuning the properties of graphite fluorides.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

De Novo Sequencing of Peptides from Top-Down Tandem Mass Spectra

De novo sequencing of proteins and peptides is one of the most important problems in mass spectrometry-driven proteomics. A variety of methods have been developed to accomplish this task from a set of bottom-up tandem (MS/MS) mass spectra. However, a more recently emerged top-down technology, now gaining more and more popularity, opens new perspectives for protein analysis and characterization, implying a need in efficient algorithms for processing this kind of MS/MS data. Here we describe a method that allows to retrieve from a set of top-down MS/MS spectra long and accurate sequence fragments of the proteins contained in a sample. To this end, we outline a strategy for generating high-quality sequence tags from top-down spectra, and introduce the concept of a T-Bruijn graph by adapting to the case of tags the notion of an A-Bruijn graph widely used in genomics. The output of the proposed approach represents the set of amino acid strings spelled out by optimal paths in the connected components of a T-Bruijn graph. We illustrate its performance on top-down datasets acquired from carbonic anhydrase 2 (CAH2) and the Fab region of alemtuzumab.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2021

Creation and observation of Hopfions in magnetic multilayer systems

Among topological solitons, magnetic skyrmions are two-dimensional particle-like objects with a continuous winding of the magnetization, and magnetic Hopfions are three-dimensional objects that can be formed from a closed loop of twisted skyrmion strings. Theoretical models suggest that magnetic Hopfions can be stabilized in frustrated or chiral magnetic systems, and target skymions can be transformed into Hopfions by adapting their perpendicular magnetic anisotropy, but their experimental verification has been elusive so far. Here, we present an experimental study of magnetic Hopfions that are created in Ir/Co/Pt multilayers shaped into nanoscale disks, known to host target skyrmions. To characterize three-dimensional spin textures that distinguish Hopfions from target skyrmions magnetic images are recorded with surface-sensitive X-ray photoemission electron microscopy and bulk-sensitive soft X-ray transmission microscopy using element-specific X-ray magnetic circular dichroism effects as magnetic contrast. These results could stimulate further investigations of Hopfions and their potential application in three-dimensional spintronics devices.

36 MATERIALS SCIENCE↗