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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Integrating Transactive Energy into Reliability Evaluation for a Self-healing Distribution System with Microgrid

Non-utility owned distributed energy resources (DERs) are mostly untapped currently, but they can provide many grid services such as voltage regulation and service restoration, if properly controlled, and can improve the distribution systems reliability when coordinated with utility-owned assets such as self-healing control and microgrids. This paper integrates transactive energy control into the distribution system reliability evaluation to quantitatively assess the impact of non-utility owned DERs on reliability improvement. Here, a transactive reactive power control strategy is designed to incentivize the DERs to provide reactive power support for improving voltage profiles thus enabling additional customer load restoration during an outage. Also, an operational sequence to coordinate the non-utility owned DERs with the utility owned self-healing control and utility owned microgrids is designed and integrated into the service restoration process with the operational constraints guaranteed by checking the three-phase unbalanced power flow for post-fault network reconfiguration. The reliability indices are then calculated through a Monte Carlo simulation. The transactive reactive power control strategy is tested on a four-feeder distribution system operated by Duke Energy in the U.S. Results demonstrate that the non-utility owned DERs with the transactive control improve the reliability of both the system and critical loads by more than 30%.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Importance of appropriate genome information for the design of mating type primers in black and yellow morel populations

Abstract Morels are highly prized edible fungi where sexual reproduction is essential for fruiting-body production. As a result, a comprehensive understanding of their sexual reproduction is of great interest. Central to this is the identification of the reproductive strategies used by morels. Sexual reproduction in fungi is controlled by mating-type ( MAT ) genes and morels are thought to be mainly heterothallic with two idiomorphs, MAT1-1 and MAT1-2. Genomic sequencing of black (Elata clade) and yellow (Esculenta clade) morel species has led to the development of PCR primers designed to amplify genes from the two idiomorphs for rapid genotyping of isolates from these two clades. To evaluate the design and theoretical performance of these primers we performed a thorough bioinformatic investigation, including the detection of the MAT region in publicly available Morchella genomes and in-silico PCR analyses. All examined genomes, including those used for primer design, appeared to be heterothallic. This indicates an inherent fault in the original primer design which utilized a single Morchella genome, as the use of two genomes with complementary mating types would be required to design accurate primers for both idiomorphs. Furthermore, potential off-targets were identified for some of the previously published primer sets, but verification was challenging due to lack of adequate genomic information and detailed methodologies for primer design. Examinations of the black morel specific primer pairs (MAT11L/R and MAT22L/R) indicated the MAT22 primers would correctly target and amplify the MAT1-2 idiomorph, but the MAT11 primers appear to be capable of amplifying incorrect off-targets within the genome. The yellow morel primer pairs (EMAT1-1 L/R and EMAT1-2 L/R) appear to have reporting errors, as the published primer sequences are dissimilar with reported amplicon sequences and the EMAT1-2 primers appear to amplify the RNA polymerase II subunit ( RPB2 ) gene. The lack of the reference genome used in primer design and descriptive methodology made it challenging to fully assess the apparent issues with the primers for this clade. In conclusion, additional work is still required for the generation of reliable primers to investigate mating types in morels and to assess their performance on different clades and across multiple geographical regions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Amino acid interacting network in the receptor-binding domain of SARS-CoV-2 spike protein

The relation between amino acid (AA) sequence and biologically active conformation controls the process of polypeptide chains folding into three-dimensional (3d) protein structures. The recent achievements in the resolution achieved in cryo-electron microscopy coupled with improvements in computational methodologies have accelerated the analysis of structures and properties of proteins. However, the detailed interaction between AAs has not been fully elucidated. Herein, we present a de novo method to evaluate inter-amino acid interactions based on the concept of accurately evaluating the amino acid bond pairs (AABP). The results obtained enabled the identification of complex 3d long-range interconnected AA interacting network in proteins. The method is applied to the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. We show that although nearest-neighbor AAs in the primary sequence have large AABP, other nonlocal AAs make substantial contribution to AABP with significant participation of both covalent and hydrogen bonding. Detailed analysis of AABP in RBD reveals the pivotal role they play in sequence conservation with profound implications on residue mutations and for therapeutic drug design. This approach could be easily applied to many other proteins of biomedical interest in life sciences.

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

Decision-Making Under Uncertainty for a Digital Thread-Enabled Design Process

Abstract Digital thread is a data-driven architecture that links together information from all stages of the product lifecycle. Despite increasing application in manufacturing, maintenance/operations, and design related tasks, a principled formulation of analyzing the decision-making problem under uncertainty for the digital thread remains absent. The contribution of this article is to present a formulation using Bayesian statistics and decision theory. First, we address how uncertainty propagates in the product lifecycle and how the digital thread evolves based on the decisions we make and the data we collect. By using these mechanics, we explore designing over multiple product generations or iterations and provide an algorithm to solve the underlying multistage decision problem. We illustrate our method on an example structural design problem where our method can quantify and optimize different types and sequences of decisions, ranging from experimentation, manufacturing, and sensor placement/selection, to minimize total accrued costs.

Engineering↗
DOE OSTI2025

Signal sequences target enzymes and structural proteins to bacterial microcompartments and are critical for microcompartment formation

ABSTRACT Spatial organization of pathway enzymes has emerged as a promising tool to address several challenges in metabolic engineering, such as flux imbalances and off-target product formation. Bacterial microcompartments (MCPs) are a spatial organization strategy used natively by many bacteria to encapsulate metabolic pathways that produce toxic, volatile intermediates. Several recent studies have focused on engineering MCPs to encapsulate heterologous pathways of interest, but how this engineering affects MCP assembly and function is poorly understood. In this study, we investigated the role of signal sequences, short domains that target proteins to the MCP core, in the assembly of 1,2-propanediol utilization (Pdu) MCPs. We characterized two novel Pdu signal sequences on the structural proteins PduM and PduB, which constitute the first report of metabolosome signal sequences on structural proteins rather than enzymes. We then explored the role of enzymatic and structural Pdu signal sequences on MCP assembly by deleting their encoding sequences from the genome alone and in combination. Deleting enzymatic signal sequences decreased the MCP formation, but this defect could be recovered in some cases by overexpressing genes encoding the knocked-out signal sequence fused to a heterologous protein. By contrast, deleting structural signal sequences caused similar defects to knocking out the genes encoding the full-length PduM and PduB proteins. Our results contribute to a growing understanding of how MCPs form and function in bacteria and provide strategies to mitigate assembly disruption when encapsulating heterologous pathways in MCPs. IMPORTANCE Spatially organizing biosynthetic pathway enzymes is a promising strategy to increase pathway throughput and yield. Bacterial microcompartments (MCPs) are proteinaceous organelles that many bacteria natively use as a spatial organization strategy to encapsulate niche metabolic pathways, providing significant metabolic benefits. Encapsulating heterologous pathways of interest in MCPs could confer these benefits to industrially relevant pathways. Here, we investigate the role of signal sequences, short domains that target proteins for encapsulation in MCPs, in the assembly of 1,2-propanediol utilization (Pdu) MCPs. We characterize two novel signal sequences on structural proteins, constituting the first Pdu signal sequences found on structural proteins rather than enzymes, and perform knockout studies to compare the impacts of enzymatic and structural signal sequences on MCP assembly. Our results demonstrate that enzymatic and structural signal sequences play critical but distinct roles in Pdu MCP assembly and provide design rules for engineering MCPs while minimizing disruption to MCP assembly.

Johnson, Elizabeth R. (ORCID:0000000179236881)↗
DOE OSTI2026

torch-einshard v1.0

torch-einshard is a Python library for describing local and distributed PyTorch tensor computations with compact, einsum-like notation. Its expressions name logical axes, specify how they are sharded across a PyTorch DeviceMesh, and represent partial reductions. The library automatically performs contractions, permutations, reshaping, splitting, gathering, reduction, reduce-scatter, and repartitioning while preserving autograd. Additional features include sharding-aware FFTs, tensor rolls, halo exchange, sliding windows, 1D–3D convolutions, uneven-shard handling, parameter initialization and gradient management, and cost-based execution planning. It is designed for scientific machine learning and large-model workloads, including tensor-, sequence-, and spatial-parallel MLPs, attention, convolutions, and spectral operations. Compared with manually combining torch.einsum and distributed collectives, torch-einshard expresses both the mathematical operation and data placement in one readable formula. This reduces boilerplate and synchronization errors, keeps forward and backward communication consistent, and allows the library to select optimized collective strategies without changing model code.

Morozov, Dmitriy [Lawrence Berkeley National Labor↗
DOE OSTI2020

Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide–MHC Binding Predictions

The reliable prediction of the affinity of candidate peptides for the MHC is important for predicting their potential antigenicity and thus influences medical applications, such as decisions on their inclusion in T cell–based vaccines. In this study, we present a rapid, predictive computational approach that combines a popular, sequence-based artificial neural network method, NetMHCpan 4.0, with three-dimensional structural modeling. We find that the ensembles of bound peptide conformations generated by the programs MODELLER and Rosetta FlexPepDock are less variable in geometry for strong binders than for low-affinity peptides. In tests on 1271 peptide sequences for which the experimental dissociation constants of binding to the well-characterized murine MHC allele H-2D b are known, by applying thresholds for geometric fluctuations the structure-based approach in a standalone manner drastically improves the statistical specificity, reducing the number of false positives. Furthermore, filtering candidates generated with NetMHCpan 4.0 with the structure-based predictor led to an increase in the positive predictive value (PPV) of the peptides correctly predicted to bind very strongly (i.e., Kd < 100 nM) from 40 to 52% (p = 0.027). The combined method also significantly improved the PPV when tested on five human alleles, including some with limited data for training. Overall, an average increase of 10% in the PPV was found over the standalone sequence-based method. The combined method should be useful in the rapid design of effective T cell–based vaccines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Three-Dimensional Patterning of Nanoparticles by Molecular Stamping

Directing the formation of nanoscale architectures from nanoparticles is one of the key challenges in designing nanomaterials with prescribed functions. Atomic systems, given their ability to form molecules and crystals via directional chemical bonds, provide an inspiration for establishing approaches where nanoparticles with designed anisotropic binding modalities can be assembled into nanoscale architectures. However, fabricating such nanoparticles has been challenging due to their small dimensions and limited ways for site-specific control of their surface. To this end, we present a Molecular Stamping (MOST) approach to pattern DNA-coated nanoparticles with molecules at the predefined positions on a nanoparticle surface. This patterning is realized by use of a rigid and coordinative DNA frame as a molecular stamping apparatus (MOST App). The MOST App transfers multiple types of molecular “inks”, DNA sequences, onto nanoparticle surface and fixes these molecular inks into place to form a designed pattern. After a nanoparticle release from MOST App, it possesses single-molecule patches that can provide anisotropic bonds with distinctive affinities. We further use these stamped nanoparticles to assemble prescribed clusters, whose structure is determined by the locations of patches. Using electron microscopy and tomographic methods, we investigate the efficiency of cluster formation and the resulting spatial arrangements of nanoparticles. The presented approach provides a single-molecule and spatiallydetermined control over nanoparticle functionalization for creating nanoparticles with designed placement of different molecules and for realizing a rational fabrication of nanomaterial architectures.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Machine learning overcomes human bias in the discovery of self-assembling peptides

Peptide materials have a wide array of functions, from tissue engineering and surface coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the peptide modulates peptide functionality, but a small increase in sequence length leads to a dramatic increase in the number of peptide candidates. Traditionally, peptide design is guided by human expertise and intuition and typically yields fewer than ten peptides per study, but these approaches are not easily scalable and are susceptible to human bias. Here, in this work, we introduce a machine learning workflow—AI-expert—that combines Monte Carlo tree search and random forest with molecular dynamics simulations to develop a fully autonomous computational search engine to discover peptide sequences with high potential for self-assembly. We demonstrate the efficacy of the AI-expert to efficiently search large spaces of tripeptides and pentapeptides. The predictability of AI-expert performs on par or better than our human experts and suggests several non-intuitive sequences with high self-assembly propensity, outlining its potential to overcome human bias and accelerate peptide discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Develop a Fast Analysis Solver for Welding Sequence Optimization

During the shipbuilding manufacturing process, materials are exposed to significant stresses, as induced both thermally and mechanically, that alter the intended design and significantly affect the production schedule, labor hours (fitting, welding, rework, etc.), and material structural performance. The type and magnitude of deformation of a given structure depends on many factors such as the material, thickness and quality of components, the process heat input, preheat and inter-pass temperatures, type and size of welds, welding sequence and direction, location, sequence, and degree of fixturing. Numerical simulations using finite element analysis (FEA) have long been used to analyze welding-induced structural distortion. For large assemblies, transient thermal elastic-plastic analysis (TEPA) can take days or weeks to run, and optimization of welding sequence is not feasible. Simplified analysis methods were developed to reduce computational time. However, it is challenging to use these techniques to fully optimize welding sequencing because of their applied simplifications in modeling weld details. A fast analysis solver that could be used by the shipbuilding industry is being developed for optimizing welding sequences by taking full advantage of modern GPU-based HPC hardware and incorporating patented acceleration schemes. The accelerated processing factors are up to 2200 times greater for large, multi-pass welded structures.

Yang, Yu-Ping↗
DOE OSTI2021

Optical Biosensor Platforms Display Varying Sensitivity for the Direct Detection of Influenza RNA

Detection methods that do not require nucleic acid amplification are advantageous for viral diagnostics due to their rapid results. These platforms could provide information for both accurate diagnoses and pandemic surveillance. Influenza virus is prone to pandemic-inducing genetic mutations, so there is a need to apply these detection platforms to influenza diagnostics. Here, we analyzed the Fast Evaluation of Viral Emerging Risks (FEVER) pipeline on ultrasensitive detection platforms, including a waveguide-based optical biosensor and a flow cytometry bead-based assay. The pipeline was also evaluated in silico for sequence coverage in comparison to the U.S. Centers for Disease Control and Prevention’s (CDC) influenza A and B diagnostic assays. The influenza FEVER probe design had a higher tolerance for mismatched bases than the CDC’s probes, and the FEVER probes altogether had a higher detection rate for influenza isolate sequences from GenBank. When formatted for use as molecular beacons, the FEVER probes detected influenza RNA as low as 50 nM on the waveguide-based optical biosensor and 1 nM on the flow cytometer. In addition to molecular beacons, which have an inherently high background signal we also developed an exonuclease selection method that could detect 500 pM of RNA. The combination of high-coverage probes developed using the FEVER pipeline coupled with ultrasensitive optical biosensors is a promising approach for future influenza diagnostic and biosurveillance applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Plant defensin MtDef4–derived antifungal peptide with multiple modes of action and potential as a bio–inspired fungicide

Chemical fungicides have been instrumental in protecting crops from fungal diseases. However, increasing fungal resistance to many of the single-site chemical fungicides calls for the development of new antifungal agents with novel modes of action (MoA). The sequence-divergent cysteine-rich antifungal defensins with multisite MoA are promising starting templates for design of novel peptide-based fungicides. Here, we experimentally tested such a set of 17-amino-acid peptides containing the γ-core motif of the antifungal plant defensin MtDef4. These designed peptides exhibited antifungal properties different from those of MtDef4. Focused analysis of a lead peptide, GMA4CG_V6, showed that it was a random coil in solution with little or no secondary structure elements. Additionally, it exhibited potent cation-tolerant antifungal activity against the plant fungal pathogen Botrytis cinerea, the causal agent of grey mould disease in fruits and vegetables. Its multisite MoA involved localization predominantly to the plasma membrane, permeabilization of the plasma membrane, rapid internalization into the vacuole and cytoplasm, and affinity for the bioactive phosphoinositides phosphatidylinositol 3-phosphate (PI3P), PI4P, and PI5P. The sequence motif RRRW was identified as a major determinant of the antifungal activity of this peptide. While topical spray application of GMA4CG_V6 on Nicotiana benthamiana and tomato plants provided preventive and curative suppression of grey mould disease symptoms, the peptide was not internalized into plant cells. Our findings open the possibility that truncated and modified defensin-derived peptides containing the γ-core sequence could serve as promising candidates for further development of bio-inspired fungicides.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

When Machine Learning Meets 2D Materials: A Review

The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper – yet more efficient – alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.

2D materials↗
DOE OSTI2022

Programmable soft valves for digital and analog control

In soft devices, complex actuation sequences and precise force control typically require hard electronic valves and microcontrollers. Existing designs for entirely soft pneumatic control systems are capable of either digital or analog operation, but not both, and are limited by speed of actuation, range of pressure, time required for fabrication, or loss of power through pull-down resistors. Using the nonlinear mechanics intrinsic to structures composed of soft materials—in this case, by leveraging membrane inversion and tube kinking—two modular soft components are developed: a piston actuator and a bistable pneumatic switch. These two components combine to create valves capable of analog pressure regulation, simplified digital logic, controlled oscillation, nonvolatile memory storage, linear actuation, and interfacing with human users in both digital and analog formats. Three demonstrations showcase the capabilities of systems constructed from these valves: 1) a wearable glove capable of analog control of a soft artificial robotic hand based on input from a human user’s fingers, 2) a human-controlled cushion matrix designed for use in medical care, and 3) an untethered robot which travels a distance dynamically programmed at the time of operation to retrieve an object. This work illustrates pathways for complementary digital and analog control of soft robots using a unified valve design.

42 ENGINEERING↗
DOE OSTI2022

Extension of the Generic Multi-Frequency Modelling Method for Type 3 Wind Turbines

Reflecting potential non-linearities of converter-based systems, especially frequency and sequence couplings, is an ongoing challenge for linearized multi-frequency models. Besides, design details are required to develop such models, which either are the intellectual property of manufacturers or require experimental tests. The generic multi-frequency modelling method has been proposed to fill this gap; however, it is only developed for converter-connected systems, e.g., Type 4 Wind Turbines (WT). This paper proposes to extend the application of the generic multi-frequency modelling method for Type 3 WTs. In this way, a theory for patterns of the couplings in Type 3 WTs is proposed. Accordingly, a group of emissions and couplings are Rotor-Speed-Dependent (RSD). The RSD emissions and couplings are particular characteristics of Type 3 WTs, which should be addressed in the generic multi-frequency models. Additionally, the proposed theory is verified by unique-worldwide experimental perturbation tests on a 2 MVA Type 3 WT using a 7 MVA grid emulator. Accordingly, a limited number of RSD couplings and emissions are observed in the test results, mainly in low frequencies (below 1 kHz). Therefore, addressing the RSD couplings is practical and important to extend the generic multi-frequency modelling for Type 3 WTs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Identification Uncertainty in Inverse Material Model Parameter Determination: A Sensitivity‐Based Decision Process for Load Path Selection

This research proposes a sensitivity-based framework for selecting the optimal prescribed loading path for a biaxial cruciform specimen. Optimality here is determined by the direction and magnitude of the prescribed displacement that minimizes the influence of random noise on the material model parameter identification. Using simulated experimental data based on finite element simulation, in this work, we identify the material model parameters of a Ludwik hardening model and plane stress implementation of the Hill-48 yield criterion using finite element model updating (FEMU). Our analysis reveals that the identification (or estimator) uncertainty of model parameters depends on the displacement boundary conditions (i.e., loading sequence) and the ground-truth value of the individual parameters. Optimal experimental design (OED) criteria based on the Fisher information matrix were investigated to mitigate indecision in the choice of optimal load path when the identification uncertainty of different material model parameters optimized at different load paths. The determinant of the Fisher information matrix was chosen here as the more useful metric due to its ability to capture uncertainty of the most influential material model parameters. The proposed framework demonstrates potential for real-time automated load step selection using scalar criteria derived prior to mechanical loading. The framework can be generalized to other geometries, boundary conditions and material models, allowing this procedure to be utilized for different experimental configurations and materials.

Fayad, Samuel S. [University of Illinois at Urbana↗
DOE OSTI2023

Eukaryotic genomes from a global metagenomic data set illuminate trophic modes and biogeography of ocean plankton

ABSTRACT Metagenomics is a powerful method for interpreting the ecological roles and physiological capabilities of mixed microbial communities. Yet, many tools for processing metagenomic data are neither designed to consider eukaryotes nor are they built for an increasing amount of sequence data. EukHeist is an automated pipeline to retrieve eukaryotic and prokaryotic metagenome-assembled genomes (MAGs) from large-scale metagenomic sequence data sets. We developed the EukHeist workflow to specifically process large amounts of both metagenomic and/or metatranscriptomic sequence data in an automated and reproducible fashion. Here, we applied EukHeist to the large-size fraction data (0.8–2,000 µm) from Tara Oceans to recover both eukaryotic and prokaryotic MAGs, which we refer to as TOPAZ (Tara Oceans Particle-Associated MAGs). The TOPAZ MAGs consisted of >900 environmentally relevant eukaryotic MAGs and >4,000 bacterial and archaeal MAGs. The bacterial and archaeal TOPAZ MAGs expand upon the phylogenetic diversity of likely particle- and host-associated taxa. We use these MAGs to demonstrate an approach to infer the putative trophic mode of the recovered eukaryotic MAGs. We also identify ecological cohorts of co-occurring MAGs, which are driven by specific environmental factors and putative host-microbe associations. These data together add to a number of growing resources of environmentally relevant eukaryotic genomic information. Complementary and expanded databases of MAGs, such as those provided through scalable pipelines like EukHeist, stand to advance our understanding of eukaryotic diversity through increased coverage of genomic representatives across the tree of life. IMPORTANCE Single-celled eukaryotes play ecologically significant roles in the marine environment, yet fundamental questions about their biodiversity, ecological function, and interactions remain. Environmental sequencing enables researchers to document naturally occurring protistan communities, without culturing bias, yet metagenomic and metatranscriptomic sequencing approaches cannot separate individual species from communities. To more completely capture the genomic content of mixed protistan populations, we can create bins of sequences that represent the same organism (metagenome-assembled genomes [MAGs]). We developed the EukHeist pipeline, which automates the binning of population-level eukaryotic and prokaryotic genomes from metagenomic reads. We show exciting insight into what protistan communities are present and their trophic roles in the ocean. Scalable computational tools, like EukHeist, may accelerate the identification of meaningful genetic signatures from large data sets and complement researchers’ efforts to leverage MAG databases for addressing ecological questions, resolving evolutionary relationships, and discovering potentially novel biodiversity.

59 BASIC BIOLOGICAL SCIENCES↗