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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data for The Stem Cell-Type Transcriptome of Bioenergy Sorghum Reveals the Spatial Regulation of Secondary Cell Wall Networks

Bioenergy sorghum is a low-input, drought-resilient, deep-rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum’s 4-5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about stem cell-type gene expression and regulation was available to enable engineering. To obtain this information, Laser Capture Microdissection (LCM) was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell-type specific and cell-preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell-type specific gene regulatory networks (GRNs) revealed that unique TF families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell-specific transcriptome data was combined with a stem developmental transcriptome dataset to identify the GRN that differentially activates the secondary cell wall (SCW) formation in stem xylem sclerenchyma and epidermal cells. The cell-type transcriptomic dataset provides a valuable source of information about the function of sorghum stem cell types and GRNs that will enable the engineering of bioenergy sorghum stems.

Software↗
DOE OSTI2024

Cell‐type‐specific transcriptomics uncovers spatial regulatory networks in bioenergy sorghum stems

SUMMARY Bioenergy sorghum is a low‐input, drought‐resilient, deep‐rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum's 4–5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about cell‐type gene expression and regulation in stems was available to enable engineering. To obtain this information, laser capture microdissection was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell‐type‐specific and cell‐preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell‐type‐specific gene regulatory networks (GRNs) revealed that unique transcription factor families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell‐specific transcriptome data was combined with known secondary cell wall (SCW) networks to identify the GRNs that differentially activate SCW formation in vascular sclerenchyma and epidermal cells. The spatial transcriptomic dataset provides a valuable source of information about the function of different sorghum cell types and GRNs that will enable the engineering of bioenergy sorghum stems, and an interactive web application developed during this project will allow easy access and exploration of the data ( https://mc‐lab.shinyapps.io/lcm‐dataset/ ).

09 BIOMASS FUELS↗
DOE OSTI2020

High‐Performance Fluorinated Fused‐Ring Electron Acceptor with 3D Stacking and Exciton/Charge Transport

Abstract A new fluorinated electron acceptor (FINIC) based on 6,6,12,12‐tetrakis(3‐fluoro‐4‐hexylphenyl)‐indacenobis(dithieno[3,2‐ b ;2 ′ ,3 ′ ‐ d ]thiophene) as the electron‐donating central core and 5,6‐difluoro‐3‐(1,1‐dicyanomethylene)‐1‐indanone as the electron‐deficient end groups is rationally designed and synthesized. FINIC shows similar absorption profile in dilute solution to the nonfluorinated analogue INIC. However, compared with INIC, FINIC film shows red‐shifted absorption, down‐shifted frontier molecular orbital energy levels, enhanced crystallinity, and more ordered molecular packing. Single‐crystal structure data show that FINIC molecules pack into closer 3D “network” motif through H‐bonding and π–π interaction, while INIC molecules pack into incompact “honeycomb” motif through only π–π stacking. Theoretical calculations reveal that FINIC has stronger electronic coupling and more molecular interactions than INIC. FINIC has higher electron mobilities in both horizontal and vertical directions than INIC. Moreover, FINIC and INIC support efficient 3D exciton transport. PBD‐SF/FINIC blend has a larger driving force for exciton splitting, more efficient charge transfer and photoinduced charge generation. Finally, the organic solar cells based on PBD‐SF/FINIC blend yield power conversion efficiency of 14.0%, far exceeding that of the PBD‐SF/INIC‐based devices (5.1%).

Dai, Shuixing↗
DOE OSTI2020

A Guide to Conquer the Biological Network Era Using Graph Theory

Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Network analysis of memristive device circuits: dynamics, stability and correlations

Abstract Networks with memristive devices are a potential basis for the next generation of computing devices. They are also an important model system for basic science, from modeling nanoscale conductivity to providing insight into the information-processing of neurons. The resistance in a memristive device depends on the history of the applied bias and thus displays a type of memory. The interplay of this memory with the dynamic properties of the network can give rise to new behavior, offering many fascinating theoretical challenges. But methods to analyze general memristive circuits are not well described in the literature. In this paper we develop a general circuit analysis for networks that combine memristive devices alongside resistors, capacitors and inductors and under various types of control. We derive equations of motion for the memory parameters of these circuits and describe the conditions for which a network should display properties characteristic of a resonator system. For the case of a purely memresistive network, we derive Lyapunov functions, which can be used to study the stability of the network dynamics. Surprisingly, analysis of the Lyapunov functions show that these circuits do not always have a stable equilibrium in the case of nonlinear resistance and window functions. The Lyapunov function allows us to study circuit invariances, wherein different circuits give rise to similar equations of motion, which manifest through a gauge freedom and node permutations. Finally, we identify the relation between the graph Laplacian and the operators governing the dynamics of memristor networks operators, and we use these tools to study the correlations between distant memristive devices through the effective resistance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Effect of static local distortions vs. dynamic motions on the stability and band gaps of cubic oxide and halide perovskites

Ternary ABX 3 perovskites made of corner-sharing BX 6 octahedra have long featured prominently in solid-state chemistry and condensed matter physics. Still, the joint understanding of their two main subgroups—halides and oxides—has not been fully developed. Indeed, unlike the case in simpler compounds having a single, robust repeated motif (“monomorphous”), certain cubic perovskites can manifest a non-thermal (= intrinsic) distribution of local motifs (“polymorphous networks”). Such static deformations can include positional degrees of freedom (e.g., atomic displacements and octahedral tilting) or magnetic moment degrees of freedom in paramagnets. Unlike thermal motion, such static distortions do not time-average to zero, being an expression of the intrinsic symmetry breaking preference of the chemical bonding. Here, the present study compares electronic structure features of oxide and halide perovskites starting from the static polymorphous distribution of motifs described by Density Functional Theory (DFT) minimization of the internal energy, continuing to finite temperature thermal disorder modeled via finite temperature DFT molecular dynamics. We find that (i) different oxide vs. halide ABX 3 compounds adopt different energy-lowering symmetry-breaking modes. The calculated pair distribution function (PDF) of SrTiO 3 from the first-principles agrees with recently measured PDF. (ii) In both oxides and halides, such static distortions lead to band gap blueshifts with respect to undistorted cubic Pm-3m structure. (iii) For oxide perovskites, high-temperature molecular dynamics simulations initiated from the statically distorted polymorphous structures reveal that the thermally-induced distortions can lead to a band gap redshift. (iv) In contrast, for cubic halide perovskite CsPbI 3 , both the intrinsic distortions and the thermal distortions contribute in tandem to band gap blueshift, the former, intrinsic effect being dominant. (v) In the oxide SrTiO 3 and CaTiO 3 (but not in halide) perovskites, octahedral tilting leads to the emergence of a distinct Γ–Γ direct band gap component as a secondary valley minimum to the well-known indirect R–Γ gap. Understanding such intrinsic vs. thermal effects on oxide vs. halide perovskites holds the potential for designing target electronic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

ITeM: Independent temporal motifs to summarize and compare temporal networks

We report networks are a fundamental and flexible way of representing various complex systems. Many domains such as communication, citation, procurement, biology, social media, and transportation can be modeled as a set of entities and their relationships. Temporal networks are a specialization of general networks where every relationship occurs at a discrete time. The temporal evolution of such networks is as important to understand as the structure of the entities and relationships. We present the Independent Temporal Motif (ITeM) to characterize temporal graphs from different domains. ITeMs can be used to model the structure and the evolution of the graph. In contrast to existing work, ITeMs are edge-disjoint directed motifs that measure the temporal evolution of ordered edges within the motif. For a given temporal graph, we produce a feature vector of ITeM frequencies and the time it takes to form the ITeM instances. We apply this distribution to measure the similarity of temporal graphs. We show that ITeM has higher accuracy than other motif frequency-based approaches. We define various ITeM-based metrics that reveal salient properties of a temporal network. We also present importance sampling as a method to efficiently estimate the ITeM counts. We present a distributed implementation of the ITeM discovery algorithm using Apache Spark and GraphFrame. We evaluate our approach on both synthetic and real temporal networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Adaptive Recovery Model: Designing Systems for Testing Tracing and Vaccination to Support COVID-19 Recovery Planning.

This report documents a new approach to designing disease control policies that allocate scarce testing, contact tracing, and vaccination resources to better control community transmission of COVID19 or similar diseases. The Adaptive Recovery Model (ARM) combines a deterministic compartmental disease model with a stochastic network disease propagation model to enable us to simulate COVID-19 community spread through the lens of two complementary modeling motifs. ARM contact networks are derived from cell-phone location data that have been anonymized and interpreted as individual arrivals to specic public locations. Modeling disease spread over these networks allows us to identify locations within communities conducive to rapid disease spread. ARM applies this model- and data-derived abstractions of community transmission to evaluate the effectiveness of disease control measures including targeted social distancing, contact tracing, testing and vaccination. The architecture of ARM provides a unique capacity to help decision makers understand how best to deploy scarce testing, tracing and vaccination resources to minimize disease-spread potential in a community. This document details the novel mathematical formulations underlying ARM, presents a dynamical stability analysis of the deterministic model components, a sensitivity analysis of control parameters and network structure, and summarizes a process for deriving contact networks from cell-phone location data. An example use case steps through applying ARM to evaluate three targeted social distancing policies using Bernalillo County, New Mexico as an exemplar test locale. This step-by-step analysis demonstrates how ARM can be used to measure the relative performance of competing public health policies. Initial scenario tests of ARM shows that ARMs design focus on resource utilization rather than simple incidence prediction can provide decision makers with additional quantitative guidance for managing ongoing public health emergencies and planning future responses.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Changes in short- and medium-range order in metallic liquids during undercooling

It has been widely speculated that dominant motifs, such as short-range icosahedral order, can influence glass formation and the properties of glasses. Experimental data on both fragile and strong undercooled liquids demonstrate corresponding changes in their thermophysical properties consistent with increasing development of a network of interconnect motifs based on molecular dynamics. Describing these regions of local order, how they connect, and how they are related to property changes have been challenging issues, both computationally and experimentally. Yet the consensus is that metallic liquids develop interconnected medium-range order consisting of some regions with lower mobility with deeper undercooling. Less well understood is how these motifs (or “crystal genes”) in the liquid can inhibit nucleation in the deeply undercooled liquid or influence phase selection upon devitrification. These motifs tend to have local packing unlike stable compounds with icosahedral order tending to dominate the best glass formers. The underlying kinetic and thermodynamic forces that guide the formation of these motifs and how they interconnect during undercooling remain open questions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Sulfur Conversion to Donor‐Acceptor Ladder Polymer Networks through Mechanochemical Nucleophilic Aromatic Substitution for Efficient CO 2 Photoreduction

The development of synthetic methods capable of converting elemental sulfur into conjugated porous sulfur‐rich polymers remains a great challenge, although direct utilization of this readily available feedstock can significantly enrich its uses and circumvent environmental problems during sulfur storage. Here, we report herein mechanochemical (MC) nucleophilic aromatic substitution (S N Ar) that enables sulfur conversion into thianthrene‐bridged porous ladder polymer networks with dense donor‐acceptor (D−A) molecular junctions. We demonstrate that the key lies in the generation of bent thianthrene units through a solid‐state ball‐milling condensation reaction between 1,2‐dihaloarenes and elemental sulfur. We also show that the assembling of D−A structural motifs into porous networks affords efficient visible‐light‐driven photocatalytic reduction of carbon dioxide (CO 2 ) with water (H 2 O) vapor, in the absence of any additional photosensitizer, sacrificial agents or cocatalysts. Exceptional photoinduced charge separation along with boosted exciton dissociation results in a high‐performance of carbon monoxide (CO) production rate of 306.1 μmol g −1 h −1 with near 100 % CO selectivity, which is accompanied by H 2 O oxidation to O 2 , as confirmed by both experimental and theoretical results. We anticipate this novel MC S N Ar approach will advance processing techniques for direct sulfur utilization and facilitate new possibilities for the synthesis of D−A ladder polymer networks with promising potential in photocatalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗