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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery

Non-Ribosomal Peptides (NRPs) represent a biomedically important class of natural products that include a multitude of antibiotics and other clinically used drugs. NRPs are not directly encoded in the genome but are instead produced by metabolic pathways encoded by biosynthetic gene clusters (BGCs). Since the existing genome mining tools predict many putative NRPs synthesized by a given BGC, it remains unclear which of these putative NRPs are correct and how to identify post-assembly modifications of amino acids in these NRPs in a blind mode, without knowing which modifications exist in the sample. To address this challenge, here we report NRPminer, a modification-tolerant tool for NRP discovery from large (meta)genomic and mass spectrometry datasets. We show that NRPminer is able to identify many NRPs from different environments, including four previously unreported NRP families from soil-associated microbes and NRPs from human microbiota. Furthermore, in this work we demonstrate the anti-parasitic activities and the structure of two of these NRP families using direct bioactivity screening and nuclear magnetic resonance spectrometry, illustrating the power of NRPminer for discovering bioactive NRPs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling

Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Scalable 3D reconstruction for X-ray single particle imaging with online machine learning

X-ray free-electron lasers offer unique capabilities for measuring the structure and dynamics of biomolecules, helping us understand the basic building blocks of life. Notably, high-repetition-rate free-electron lasers enable single particle imaging, where individual, weakly scattering biomolecules are imaged under near-physiological conditions with the opportunity to access fleeting states that cannot be captured in cryogenic or crystallized conditions. Existing X-ray single particle reconstruction algorithms, which estimate the particle orientation for each image independently, are slow and memory-intensive when handling the massive datasets generated by emerging free-electron lasers. Here, we introduce X-RAI (X-Ray single particle imaging with Amortized Inference), an online reconstruction framework that estimates the structure of 3D macromolecules from large X-ray single particle datasets. X-RAI consists of a convolutional encoder, which amortizes pose estimation over large datasets, as well as a physics-based decoder, which employs an implicit neural representation to enable high-quality 3D reconstruction in an end-to-end, self-supervised manner. We demonstrate that X-RAI achieves state-of-the-art performance for small-scale datasets in simulation and challenging experimental settings and demonstrate its unprecedented ability to process large datasets containing millions of diffraction images in an online fashion. These abilities signify a paradigm shift in X-ray single particle imaging towards real-time reconstruction.

Computer science↗
DOE OSTI2026

Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations

Understanding the anharmonic phonon properties of crystal compounds—such as phonon lifetimes and thermal conductivities—is essential for investigating and optimizing their thermal transport behaviors. These properties also impact optical, electronic, and magnetic characteristics through interactions between phonons and other quasiparticles and fields. In this study, we develop an automated first-principles workflow to calculate anharmonic phonon properties and build a comprehensive database encompassing more than 6500 inorganic compounds. Utilizing this dataset, we train a graph neural network model to predict thermal conductivity values and spectra from structural parameters, demonstrating a scaling law in which prediction accuracy improves with increasing training data size. High-throughput screening with the model enables the identification of materials exhibiting extreme thermal conductivities—both high and low. The resulting database offers valuable insights into the anharmonic behavior of phonons, thereby accelerating the design and development of advanced functional materials.

Ohnishi, Masato [University of Tokyo (Japan); Inst↗
DOE OSTI2023

Error statistics and scalability of quantum error mitigation formulas

Quantum computing promises advantages over classical computing in many problems. Nevertheless, noise in quantum devices prevents most quantum algorithms from achieving the quantum advantage. Quantum error mitigation provides a variety of protocols to handle such noise using minimal qubit resources. While some of those protocols have been implemented in experiments for a few qubits, it remains unclear whether error mitigation will be effective in quantum circuits with tens to hundreds of qubits. In this paper, we apply statistics principles to quantum error mitigation and analyse the scaling behaviour of its intrinsic error. We find that the error increases linearly O(ϵN) with the gate number N before mitigation and sublinearly O(ϵ'N γ ) after mitigation, where γ ≈ 0.5, ϵ is the error rate of a quantum gate, and ϵ' is a protocol-dependent factor. The $\sqrt{N}$ scaling is a consequence of the law of large numbers, and it indicates that error mitigation can suppress the error by a larger factor in larger circuits. We propose the importance Clifford sampling as a key technique for error mitigation in large circuits to obtain this result.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

High-fidelity trapped-ion qubit operations with scalable photonic modulators

Abstract Experiments with trapped ions and neutral atoms typically employ optical modulators in order to control the phase, frequency, and amplitude of light directed to individual atoms. These elements are expensive, bulky, consume substantial power, and often rely on free-space I/O channels, all of which pose scaling challenges. To support many-ion systems like trapped-ion quantum computers or miniaturized deployable devices like clocks and sensors, these elements must ultimately be microfabricated, ideally monolithically with the trap to avoid losses associated with optical coupling between physically separate components. In this work we design, fabricate, and test an optical modulator capable of monolithic integration with a surface-electrode ion trap. These devices consist of piezo-optomechanical photonic integrated circuits configured as multi-stage Mach-Zehnder modulators that are used to control the intensity of light delivered to a single trapped ion on a separate chip. We use quantum tomography employing hundreds of multi-gate sequences to enhance the sensitivity of the fidelity to the types and magnitudes of gate errors relevant to quantum computing and better characterize the performance of the modulators, ultimately measuring single qubit gate fidelities that exceed 99.7%.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Accelerated, scalable and reproducible AI-driven gravitational wave detection

The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics. Here we develop a workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Hardware-Accelerated Learning (HAL) cluster, using funcX as a universal distributed computing service. Using this workflow, an ensemble of four openly available AI models can be run on HAL to process an entire month's worth (August 2017) of advanced Laser Interferometer Gravitational-Wave Observatory data in just seven minutes, identifying all four binary black hole mergers previously identified in this dataset and reporting no misclassifications. This approach combines advances in AI, distributed computing and scientific data infrastructure to open new pathways to conduct reproducible, accelerated, data-driven discovery. By combining a repository for artificial intelligence models and a supercomputing cluster, an entire month's worth of advanced LIGO data is analysed in just 7 min, finding all binary black hole mergers previously identified in this dataset and reporting no misclassifications.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Standardized excitable elements for scalable engineering of far-from-equilibrium chemical networks

Engineered far-from-equilibrium synthetic chemical networks that pulse or switch states in response to environmental signals could precisely regulate the kinetics of chemical synthesis or self-assembly. Currently, such networks must be extensively tuned to compensate for the different activities of and unintended reactions between a network’s various chemical components. Modular elements with standardized performance could be used to rapidly construct networks with designed functions. Here we develop standardized excitable chemical regulatory elements, termed genelets, and use them to construct complex in vitro transcriptional networks. We develop a protocol for identifying >15 interchangeable genelet elements with uniform performance and minimal crosstalk. Furthermore, these elements can be combined to engineer feedforward and feedback modules whose dynamics match those predicted by a simple kinetic model. Modules can then be rationally integrated and organized into networks that produce tunable temporal pulses and act as multistate switchable memories. Standardized genelet elements, and the workflow to identify more, should make engineering complex far-from-equilibrium chemical dynamics routine.

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

Exploring the cost and emissions impacts, feasibility and scalability of battery electric ships

The United States’ greenhouse gas (GHG) emissions reduction goals, along with targets set by the International Maritime Organization, create an opportunity for battery electric shipping. In this study, we model life-cycle costs and GHG emissions from shipping electrification, leveraging ship activity datasets from across the United States in 2021. We estimate that retrofitting 6,323 domestic ships under 1,000 gross tonnage to battery electric vessels would reduce US domestic shipping GHG emissions by up to 73% by 2035 from 2022 levels. By 2035, electrifying up to 85% of these ships could become cost effective versus internal combustion engine ships if they cover 99% of annual trips and charge from a deeply decarbonized grid. We find that charging demands from electrifying these ships could be concentrated at just 20 of 150 major ports nationwide. This study demonstrates that retrofitting to battery electric vessels has economic potential and could significantly accelerate GHG emission reductions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

A scalable, biopolymer-based microenvironment for electrochemical CO 2 conversion to multicarbon products with current densities over 2 A cm −2

The electrochemical CO 2 reduction reaction (CO 2 RR) relies heavily on the surrounding microenvironment to promote formation of desirable multicarbon (C 2+ ) products. However, microenvironment control to achieve high C 2+ yields at industrially relevant current densities remains a crucial challenge. We report that chitosan, cellulose and chitin biopolymer coatings on CO 2 RR electrocatalysts enhance the microenvironment by increasing local CO 2 /CO concentration, reducing local water activity and providing suitable ion conductivity and local pH. This facile approach achieves C 2+ Faradaic efficiencies of 90 ± 1.7% at 1.6 A cm −2 and C 2+ Faradaic efficiency = 83 ± 3.2% at 2.2 A cm −2 with a formation rate of 5,926 μmol h −1 cm −2 . Importantly, within the cathode, these ion-conductive hydrophilic biopolymers can fully substitute traditional hydrophobic ionomers/binders, such as Nafion, challenging previous assumptions about the non-viability of hydrophilic materials for selective CO 2 RR due to excess interfacial H 2 O. These findings unveil key insights into microenvironment design to enhance C–C coupling through a simple method.

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

Scalable fabrication and coating methods for perovskite solar cells and solar modules

Since the report in 2012 of a solid-state perovskite solar cell (PSC) with a power-conversion efficiency (PCE) of 9.7% and a stability of 500 h, intensive efforts have been made to increase the certified PCE, reaching 25.2% in 2019. The PCE of PSCs now exceeds that of conventional thin-film solar-cell technologies, and the rate at which this increase has been achieved is unprecedented in the history of photovoltaics. Moreover, the development of moisture-stable and heat-stable materials has increased the stability of PSCs. Small-area devices (<1 cm2) are typically fabricated using a spin-coating method; however, this approach may not be suitable for the preparation of the large-area (>100 cm2) substrates required for commercialization. Thus, materials and methods need to be developed for coating large-area PSCs. In this Review, we discuss solution-based and vapour-phase coating methods for the fabrication of large-area perovskite films, examine the progress in performance and the parameters affecting the properties of large-area coatings, and provide an overview of the methodologies for achieving high-efficiency perovskite solar modules.

14 SOLAR ENERGY↗
DOE OSTI2022

Scalable single-mode surface-emitting laser via open-Dirac singularities

Single-aperture cavities are a key component of lasers that are instrumental for the amplification and emission of a single light mode. However, the appearance of high-order transverse modes as the size of the cavities increases has frustrated efforts to scale-up cavities while preserving single-mode operation since the invention of the laser six decades ago. A suitable physical mechanism that allows single-mode lasing irrespective of the cavity size-a 'scale invariant' cavity or laser-has not been identified yet. Here we propose and demonstrate experimentally that open-Dirac electromagnetic cavities with linear dispersion-which in our devices are realized by a truncated photonic crystal arranged in a hexagonal pattern-exhibit unconventional scaling of losses in reciprocal space, leading to single-mode lasing that is maintained as the cavity is scaled up in size. The physical origin of this phenomenon lies in the convergence of the complex part of the free spectral range in open-Dirac cavities towards a constant governed by the loss rates of distinct Bloch bands, whereas for common cavities it converges to zero as the size grows, leading to inevitable multimode emission. An unconventional flat-envelope fundamental mode locks all unit cells in the cavity in phase, leading to single-mode lasing. We name such sources Berkeley surface-emitting lasers (BerkSELs) and demonstrate that their far-field corresponds to a topological singularity of charge two, in agreement with our theory. Open-Dirac cavities unlock avenues for light-matter interaction and cavity quantum electrodynamics.

42 ENGINEERING↗
DOE OSTI2020

Scalable ultrafast epitaxy of large-grain and single-crystal II-VI semiconductors

A general problem for semiconductor applications is that very slow deposition on expensive single-crystal substrates yields high crystalline quality with excellent electro-optical properties, but at prohibitive costs and throughput for many applications. In contrast, rapid deposition on inexpensive substrates or nanocrystalline films yields low costs, but comparatively inferior crystallinity, carrier transport, and recombination. Here, we present methods to deposit single-crystal material at rates 2-3 orders of magnitude faster than state-of-the-art epitaxy with low-cost methods without compromising crystalline or electro-optical quality. For example, single-crystal CdTe and CdZnTe films that would take several days to grow by molecular-beam epitaxy are deposited in 8 minutes by close-spaced sublimation, yet retain the same crystalline quality measured by X-ray diffraction rocking curves. The fast deposition is coupled with effective n- and p-type in-situ doping by In, P, and As. The epitaxy can be extended to nanocrystalline substrates. For example, we recrystallize thin CdTe films on glass to deposit large grains with low defect density. The results provide new research paths for photovoltaics, detectors, infrared imaging, flexible electronics, and other applications.

14 SOLAR ENERGY↗
DOE OSTI2025

Scalable fabrication of an array-type fixed-target device for automated room temperature X-ray protein crystallography

X-ray crystallography is one of the leading tools to analyze the 3-D structure, and therefore, function of proteins and other biological macromolecules. Traditional methods of mounting individual crystals for X-ray diffraction analysis can be tedious and result in damage to fragile protein crystals. Furthermore, the advent of multi-crystal and serial crystallography methods explicitly require the mounting of larger numbers of crystals. To address this need, we have developed a device that facilitates the straightforward mounting of protein crystals for diffraction analysis, and that can be easily manufactured at scale. Inspired by grid-style devices that have been reported in the literature, we have developed an X-ray compatible microfluidic device that can be used to trap protein crystals in an array configuration, while also providing excellent optical transparency, a low X-ray background, and compatibility with the robotic sample handling and environmental controls used at synchrotron macromolecular crystallography beamlines. At the Stanford Synchrotron Radiation Lightsource (SSRL), these capabilities allow for fully remote-access data collection at controlled humidity conditions. Furthermore, we have demonstrated continuous manufacturing of these devices via roll-to-roll fabrication to enable cost-effective and efficient large-scale production.

chemical engineering↗