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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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DOE OSTI2020

Bio-inspired gas sensing: boosting performance with sensor optimization guided by “machine learning”

The performance of existing gas sensors often degrades in field conditions because of the loss of measurement accuracy in the presence of interferences. Thus, new sensing approaches are required with improved sensor selectivity. We are developing a new generation of gas sensors, known as multivariable sensors, that have several independent responses for multi-gas detection with a single sensor. In this study, we analyze the capabilities of natural and fabricated photonic three-dimensional (3-D) nanostructures as sensors for the detection of different gaseous species, such as vapors and non-condensable gases. We employed bare Morpho butterfly wing scales to control their gas selectivity with different illumination angles. Next, we chemically functionalized Morpho butterfly wing scales with a fluorinated silane to boost the response of these nanostructures to the vapors of interest and to suppress the response to ambient humidity. Further, we followed our previously developed design rules for sensing nanostructures and fabricated bioinspired inorganic 3-D nanostructures to achieve functionality beyond natural Morpho scales. These fabricated nanostructures have embedded catalytically active gold nanoparticles to operate at high temperatures of ≈300 °C for the detection of gases for solid oxide fuel cell (SOFC) applications. Our performance advances in the detection of multiple gaseous species with specific nanostructure designs were achieved by coupling the spectral responses of these nanostructures with machine learning (a.k.a. multivariate analysis, chemometrics) tools. Our newly acquired knowledge from studies of these natural and fabricated inorganic nanostructures coupled with machine learning data analytics allowed us to advance our design rules for sensing nanostructures toward the required gas selectivity for numerous gas monitoring scenarios at room and high temperatures for industrial, environmental, and other applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

VOLTTRON Modular Framework: Enabling flexible and scalable deployment solutions

VOLTTRON ™ is an open-source platform for distributed sensing and control. The platform provides services for collecting and storing data from buildings and devices and provides an environment for developing applications which interact with that data. The platform allows developers to build out their use cases by utilizing these frameworks and integrating new capabilities. To simplify the deployment of systems built on VOLTTRON, a new way of organizing the codebase is being explored. This document details these efforts through a new code repository layout for the VOLTTRON platform and services, and how this new layout provides targeted deployments using standard python deployment packages (wheels). In addition, this paper will discuss the development of third-party agents and how they can be integrated within the VOLTTRON ecosystem. Finally, we will discuss core platform development and direction for the modularized version of VOLTTRON.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Field and Model Data Associated with the Manuscript “Drivers of Streamflow Intermittency in Humid Regions: 2. Evaluating Controls on Flow Persistence in an Urbanized Catchment”

This package contains field data, modeling files, and scripts supporting the investigation of the drivers of streamflow intermittency in an urbanized catchment. It includes the field data collected from electrical resistivity tomography (ERT) surveys, distributed temperature sensing (DTS), continuous self-potential (SP) monitoring, groundwater and stilling well. In addition, it contains the data and results of the coupled water- and electrical-flow model developed using the COMSOL Multiphysics and Advanced Terrestrial Simulator (ATS), as well as software files and Jupyter notebooks used to process the data and generate figures in the manuscript submitted for peer review. The data archive is organized in the following directories: 1) Climate Includes hourly precipitation and daily evapotranspiration time series (2024 – 2025) provided as CSV files, alongside a text file detailing dataset units. 2) Coupled_model Field_Application subfolder contains the ATS XML input scripts, data files, output data for the SP site. It also contains the Jupyter notebook (Plot_final_calib.ipynb) to visualize the results of the modeled SP, stream-groundwater exchange and moisture content. The flow model simulation is executed using the ATS XML scripts and the included Python script (generate_data_set.py) to convert ATS output to COMSOL-ready input. COMSOL Multiphysics template (.m can only be used with COMSOL with MATLAB) is executed using the ATS output data to simulate the potential field. 3) Discharge Includes the electrical conductivity (EC) time series (provided as CSV files) from salt slug injections. It also includes the Jupyter notebook (Discharge_process.ipynyb) used to estimate discharge. All discharge measurements collated into rating_curve_processed.csv 4) DTS Contains collated DTS data including raw Stokes and anti-Stokes measurement (provided as .h5 file). It also includes DTS processing.ipynb, a Jupyter notebook for calibrating the DTS data using dts_calibration Python package. cooler_calibration.csv is the DTS calibration CSV used in the calibration sequence. 5) ERT Contains raw resistivity data (provided as CSV files), spatial location of each of the electrodes (provided as CSV files), and files used for the resistivity inversion. 6) Slug_test Includes the slug test data at all the groundwater wells provided as CSV files, as well as the Jupyter notebook (Slug_test.ipynb) for calculating hydraulic conductivity. 7) SP Contains the SP data collected in field at the SP sites (provided as CSV files). 8) Well_data Contains two subfolders: 1) Raw, which provides unprocessed pressure, electrical conductivity and temperature timeseries downloaded from the loggers in all the groundwater and stilling wells, and 2) Processed, which contains sorted, QA/QC timeseries data for each well. The data archive also contains data_process.ipynb, a Jupyter notebook used for field data analysis and generating figures (plotting well, SP, climate, and discharge data, as well as calculating head gradient at sites with nested groundwater wells). Note: Code files (.ipynb, .py, .xml) can be opened in any standard code editor, .exo file can be viewed using Paraview, .h5 files can be opened using HDFView software and h5py Python package, and .resipy file can be opened with the open-source ResIPy software.

ATS↗

DOE OSTI2023

Editorial: Edge computation and digital distribution networks

The development of digital technologies is penetrating all areas of energy revolution. Based on the in-depth integration of advanced digital technologies, distribution networks are gradually transforming into digital distribution networks (DDNs) with tremendous changes from the structure to the operation mode. DDNs is the digitalized appearance of the physical distribution network, in which ubiquitous connections and massive data are the basic characteristics (Huo et al., 2022). It is an important task to utilize the massive data and propose novel operation modes to construct more efficient and intelligent distribution networks (Jian et al., 2022). Among the advanced digital technologies in DDNs, edge computing has received wide attention (Zhao et al., 2022). It has superior performance in local sensing and intelligent computation, which can effectively relieve huge communication pressure. However, the limited computing resources and the complex computing tasks at the edge side significantly challenge the collaboration of distribution network regulation and advanced digital technologies (Hu et al., 2022). It is necessary to find out proper methods to utilize advanced digital technology to construct DDNs. This Research Topic is organized to introduce the recent progress in the construction, operation and advanced computational methods for DDNs. Finally, seven papers have been accepted, which can be sorted into the following three categories: 1) Evolution and technical features of DDNs, 2) Intelligent operation control of DDNs, 3) Advanced simulation for large-scale DDNs. The three sections below respectively introduce the major research and contributions of the papers covered in each category.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

NO 2 Interactions with MoO 3 and CuO at Atmospherically Relevant Pressures

NO x concentrations in some geographic regions are harmful to human health. Gas filters to trap NO x and other toxic chemicals contain metal oxides, including MoO 3 and CuO. These materials are also being investigated for NO x gas sensors. In a step to understand the fundamental adsorption mechanism in sensors and the effect on binding site availability in gas filters, ambient-pressure X-ray photoelectron spectroscopy (APXPS) was used to study the interaction of NO 2 with polycrystalline MoO 3 and CuO surfaces under pressures up to 0.01 Torr (14 parts per million volume (ppmv)). Density functional theory-based computational modeling was performed to reveal the mechanisms of NO 2 interactions with the MoO 3 (010) and CuO(111) surfaces to aid interpretation of the experimental results. With pressure dependence, NO 2 interacts with reduced Mo 5+ atoms generated by oxygen vacancies and abstracts hydrogen atoms from hydroxyl groups on MoO 3 without accumulating N-containing species on the surface; vacancy-induced electronic states in the band gap are also removed, hinting toward an increase in the resistivity of the material. N-containing species begin accumulating on the CuO surface at atmospherically relevant pressures of 140 ppbv. NO 2 only decomposes at oxygen vacancy sites of CuO. The nitrogen species leave the CuO surface upon evacuation, highlighting the importance of in situ surface characterization when studying gas sensing and adsorption mechanisms. Finally, these results imply that NO 2 removes hydroxyl and O vac binding sties on these materials when used in gas filtration and sensing applications. Furthermore, the results show the key role of O vac sites in the gas sensing mechanism of MoO 3 and highlight the potential of APXPS for further studies of gas sensors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Portable fiber optic sensor for rare earth elements and other critical metals using photoluminescence methods

Rare earth elements and other metals are vital to a range of technologies that are used in the energy and defense sectors. However, monopolistic market conditions have caused significant concern over the stability of the critical metal supply chain, and this has spurred extensive efforts in many nations to produce these metals domestically, both from conventional sources such as mining and well as from unconventional sources such as coal and its utilization byproducts. Slow and expensive characterization methods pose a significant barrier for both metals prospecting and process monitoring. A promising solution to this challenge is the development of highly sensitive luminescent sensors for metals, which can offer low costs, portability, and sensitivity. Anionic zinc adeninate metal-organic frameworks (BioMOFs) are known to distinguishing and detect part-per-billion levels of terbium, europium, samarium, and dysprosium in water by sensitizing the narrow, element-specific emission bands from these lanthanides. Here, a BioMOF material is immobilized onto a large diameter, solarization-resistant fiber optic tip integrated with a portable, low-cost spectrometer for rare earth element sensing. Immobilizing the sensing material on fiber instead of dispersing the sensing material in solution offers several advantages: it facilitates solvent removal, which enhances luminescent signal from the sensitized lanthanides, and it also allows the BioMOF to be recycled for multiple uses. The sensing system was deployed on a simulated process stream and exhibited qualitative agreement with inductively-coupled plasma mass spectrometry for terbium and europium detection, highlighting the potential for the sensing system to be deployed for real-world applications. By using different sensing materials, the same portable sensor may be deployed to detect other energy relevant metals such as cobalt, providing a cost-effective and sensitive platform for critical metal characterization.

Crawford, Scott↗

DOE OSTI2024

Power System Waveform Datasets for Machine Learning

The desire for increased visibility across the electricity grid will necessarily increase the deployment of sensing and measurement devices and associated data management needs to unprecedented levels. For the existing sensing and measurement infrastructure, there remains a great amount of “value” yet to be extracted through advanced data management and analytics. Availability of more data will not, by itself, lead to changes in grid visibility, security, and resiliency. To create the predictive and prescriptive environment required to enable new markets and transactions for customer revenue and a reliable grid, the data must be collected, organized, evaluated, and analyzed using sophisticated algorithms to provide actionable information allowing operators and customers to reliably manage an increasingly complex grid. Progress in artificial intelligence (AI) has been largely driven by large, publicly available datasets that can be used to train AI algorithms such as MNIST, a database of handwritten images of digits, and ImageNet, an image database of everyday objects. These types of publicly available databases of real-world training datasets have been largely credited for advancement of image processing, computer vision, and deep learning algorithms that these use cases deploy. However, in the power systems industry to date, there are few databases with proper event labeling, and data access to a publicly available collection of power system event waveforms that will allow users to interact with grid signature data. Publicly available datasets of power system event waveforms, such as the DOE/EPRI dataset, often lack critical metadata or contain limited examples of each event type, and data formats vary widely across these datasets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Deciphering the molecular components of the quorum sensing system in the fungus Ophiostoma piceae

Quorum sensing (QS) is a complex cell-cell communication mechanism that coordinates population-level behaviors in microbes. In eukaryotes, this phenomenon has been extensively described in the dimorphic yeast Candida albicans as its main QS molecule, the sesquiterpene alcohol farnesol, is responsible for various phenotypic (i.e., inhibition of yeast-to-hyphae transition, biofilm formation, and, hence, pathogenesis) and metabolic (i.e., induction of oxidative stress and apoptosis) changes. Ophiostoma piceae CECT 20416 is a dimorphic saprotrophic ascomycete with biotechnological interest that also produces farnesol as QS molecule, but in this case, the alcohol promotes the morphological transition to the mycelial form, biofilm formation, enzyme secretion, and melanin production. Here, we characterized the physiological response of Ophiostoma piceae to farnesol, the molecular components of the QS system of this fungus have been investigated using a “multiomics” approach that involved genomic, transcriptomic, and proteomic analyses. Some genes identified in this work are proposed as key factors in farnesol transport and signaling. We have also cataloged the genes undergoing major transcriptional changes triggered by the presence of the autoinducer, such as cell-wall remodeling, reactive oxygen species (ROS) protection, and melanin biosynthesis, using self-organizing maps. This analysis could be useful for applications in the forestry industry, for enzymes production, and for the valorization of residues. Furthermore, it might as well help to investigate the QS mechanisms of clinically relevant fungi phylogenetically related to Ophiostoma.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Dynamic resource allocation drives growth under nitrogen starvation in eukaryotes

Cells can sense changes in their extracellular environment and subsequently adapt their biomass composition. Nutrient abundance defines the capability of the cell to produce biomass components. Under nutrient-limited conditions, resource allocation dramatically shifts to carbon-rich molecules. Here, we used dynamic biomass composition data to predict changes in growth and reaction flux distributions using the available genome-scale metabolic models of five eukaryotic organisms (three heterotrophs and two phototrophs). We identified temporal profiles of metabolic fluxes that indicate long-term trends in pathway and organelle function in response to nitrogen depletion. Surprisingly, our calculations of model sensitivity and biosynthetic cost showed that free energy of biomass metabolites is the main driver of biosynthetic cost and not molecular weight, thus explaining the high costs of arginine and histidine. We demonstrated how metabolic models can accurately predict the complexity of interwoven mechanisms in response to stress over the course of growth.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

2021 GeoAI Workshop Report: The Trillion Pixel Challenge

The convergence of geospatial big data with advancements from artificial intelligence, cloud infrastructure, and high-performance computing continues to revolutionize mapping and analysis of Earth's surface in unprecedented detail. Rapid innovations in sensing technologies will soon collect geospatial data in even higher resolution and throughput. These developments offer the potential for breakthroughs in science, policy, and national security via end-to-end GeoAI systems that can provide fresh insights into how humans occupy and alter their environment over time. At the 2021 GeoAI Trillion Pixel workshop, international subject matter experts from government, academia, industry, and nonprofit organizations gathered virtually to discuss the Trillion Pixel GeoAI Challenge. The event focused on six major themes currently influencing scientific innovation and breakthroughs. Particular focus was paid to societal impacts. As an additional takeaway message, the gathering identified remaining application gaps and challenges that are in need of stronger community partnerships and collaborations.

58 GEOSCIENCES↗

DOE OSTI2025

Directed Synthesis of Gold Nanoparticle Superstructures Using Self-Assembling Peptoids Containing Metal-Bonding N-Heterocyclic Carbenes

N-Heterocyclic carbene (NHC) ligands, with strong metal-binding affinity, offer a robust platform for constructing organic–inorganic nanohybrids with high stability and tunable properties. However, achieving precise structural control in a simple manner remains challenging. Here, we report a one-pot synthesis of nanohybrids using self-assembling peptoids functionalized with histidine-2-ylidene, which simultaneously enable peptoid assembly and NHC–metal binding. Histidine-2-ylidene-functionalized peptoids were designed to self-assemble and provide NHC binding sites, while AuNPs served as the inorganic component due to NHCs’ strong affinity for gold surfaces. The resulting peptoid-NHC@AuNPs form well-defined vesicles that are characterized by UV–vis spectroscopy, X-ray photoelectron spectroscopy, and electron microscopy. Importantly, the vesicle size and morphology can be tuned via the peptoid sequence or environmental conditions. Further experiments highlight the crucial role of the NHC sites in the formation and stabilization of these nanohybrids. This modular strategy offers a versatile route to fabricating functional NHC-based nanohybrids for potential applications in sensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Synergistic Integration of Chemo-Resistive and SERS Sensing for Label-Free Multiplex Gas Detection

Practical sensing applications such as real-time safety alerts and clinical diagnoses require sensor devices to differentiate between various target molecules with high sensitivity and selectivity, yet conventional devices such as oxide-based chemo-resistive sensors and metal-based surface-enhanced Raman spectroscopy (SERS) sensors usually do not satisfy such requirements. In this report a label-free, chemo-resistive/SERS multimodal sensor based on a systematically assembled 3D cross-point multifunctional nanoarchitecture (3D-CMA), which has unusually strong enhancements in both “chemo-resistive” and “SERS” sensing characteristics is introduced. 3D-CMA combines several sensing mechanisms and sensing elements via 3D integration of semiconducting SnO 2 nanowire frameworks and dual-functioning Au metallic nanoparticles. It is shown that the multimodal sensor can successfully estimate mixed-gas compositions selectively and quantitatively at the sub-100 ppm level, even for mixtures of gaseous aromatic compounds (nitrobenzene and toluene) with very similar molecular structures. This is enabled by combined chemo-resistive and SERS multimodal sensing providing complementary information.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Switching the conductance of a single molecule: Lessons from molecular junctions

The concept of using single molecules as functional building blocks in electronic circuitry represents the ultimate device miniaturization and has garnered tremendous attention from physics, chemistry, and engineering. Recent advances in single-molecule junction techniques have enabled direct investigation of charge transport in a quasi-one-dimensional conduction channel composed of an individual molecule. Furthermore, one of the main research focuses over the past decade has been to understand the conductance switching effect of single molecules as it is essential for developing molecular-scale optoelectronics, computing, and sensing applications. In this perspective, we highlight the established physical and chemical mechanisms for single-molecule conductance switching behavior and examine the associated stimuli in single-molecule junction systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Classification of Cloud Particle Imagery and Thermodynamics (COCPIT): A New Databasing Tool for the Characterization of Cloud Particle Images Captured During DOE Field Campaigns

The Department of Energy for decades has explored the earth system and atmosphere through research and deployment of in-situ and remote sensing platforms during field campaigns. Among these datasets exists a vast supply of cloud particle images that provide visual insight into the complex microphysics in the clouds that span our globe. The millions of images collected over decades of deployments provides a unique opportunity to further our understanding of our atmosphere down to the crystal size. This work over the past 5 years has sought to organize these images into digestible datasets that can then be used by scientists to further our understanding of microphysics. A machine learning model was developed that categorizes over 1.5 million images across 11 weather events with over 90% accuracy according to particle type. The database was then extended to include dimensional characteristics of the particle as well as co-location of environmental properties, such as temperature and water content. Then, to initialize the connection between these data and our understanding of how crystals form and grow, weather research and forecasting simulations were run to generate the growth histories of the classified crystals. This research culminates with 2 databases per event: (1) a database of all classified crystals and their dimensional and environmental properties and (2) simulated growth histories of each crystal. Finally, a user interface was created to allow researchers to explore data statistics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Bilayer Crystals of Trapped Ions for Quantum Information Processing

Trapped-ion systems are a leading platform for quantum information processing, but they are currently limited to 1D and 2D arrays, which imposes restrictions on both their scalability and their range of applications. Here, we propose a path to overcome this limitation by demonstrating that Penning traps can be used to realize remarkably clean bilayer crystals, wherein hundreds of ions self-organize into two well-defined layers. These bilayer crystals are made possible by the inclusion of an anharmonic trapping potential, which is readily implementable with current technology. We study the normal modes of this system and discover salient differences compared to the modes of single-plane crystals. The bilayer geometry and the unique properties of the normal modes open new opportunities—in particular, in quantum sensing and quantum simulation—that are not straightforward in single-plane crystals. Furthermore, we illustrate that it may be possible to extend the ideas presented here to realize multilayer crystals with more than two layers. Our work increases the dimensionality of trapped-ion systems by efficiently utilizing all three spatial dimensions, and it lays the foundation for a new generation of quantum information processing experiments with multilayer 3D crystals of trapped ions. Published by the American Physical Society 2024

Physics↗

DOE OSTI2021

Modulation of sensory perception by hydrogen peroxide enables Caenorhabditis elegans to find a niche that provides both food and protection from hydrogen peroxide

Hydrogen peroxide (H 2 O 2 ) is the most common chemical threat that organisms face. Here, we show that H 2 O 2 alters the bacterial food preference of Caenorhabditis elegans , enabling the nematodes to find a safe environment with food. H 2 O 2 induces the nematodes to leave food patches of laboratory and microbiome bacteria when those bacterial communities have insufficient H 2 O 2 -degrading capacity. The nematode’s behavior is directed by H 2 O 2 -sensing neurons that promote escape from H 2 O 2 and by bacteria-sensing neurons that promote attraction to bacteria. However, the input for H 2 O 2 -sensing neurons is removed by bacterial H 2 O 2 -degrading enzymes and the bacteria-sensing neurons’ perception of bacteria is prevented by H 2 O 2 . The resulting cross-attenuation provides a general mechanism that ensures the nematode’s behavior is faithful to the lethal threat of hydrogen peroxide, increasing the nematode’s chances of finding a niche that provides both food and protection from hydrogen peroxide.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Synthesis of High-Quality Mg-MOF-74 Thin Films via Vapor-Assisted Crystallization

The unique features of metal-organic frameworks (MOFs), such as their large surface areas and diversity of structures, makes them suitable to a broad range of applications. One of the most critical enabling technologies for implementing MOF films is the preparation of dense and continuous films that would maximize the performance of the configurations. While formation of crystalline Mg-MOF-74 thin films presents significant challenges, demonstrating large-scale coatings of the MOF, which exhibits the highest CO2 uptake among MOFs at 1 bar and 25 °C, would be particularly beneficial for CO2- related sensing applications. In this work, we demonstrate for the first time high-quality Mg-MOF-74 films synthesized via a vapor-assisted crystallization (VAC) process. The VAC process described herein produces dense and highly crystalline layers of Mg-MOF-74 with a low coefficient of variation (COV) of film thickness below 7%. We first optimized a precursor solution for the VAC process, and then investigated the effects of synthesis temperature, time and droplet volume on the growth, crystallinity and thickness of Mg-MOF-74 films. The porosity of the film was assessed by measuring the CO2 uptake at room temperature and 1 bar. The obtained Mg-MOF-74 films possess a well-defined microporosity, as deduced from CO2 adsorption studies via quartz crystal microbalance (QCM) and comparison with bulk Mg MOF-74 reference data. Furthermore, CO2 cyclic adsorption-desorption experiments on the Mg-MOF-74

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Single molecule insights into interfacial molecular recognition for model electrochemical DNA biosensors

Electrochemical sensors that use surface-immobilized DNA to bind analytes and transduce the binding into electrochemical signals, have the potential for rapid, specific, and sensitive detection of bioanalytes via a compact and portable platform. However, accessing the structure of these surfaces/interfaces at the relevant spatial scale (< 10 nm), which determines the interfacial interactions and ultimately sensing performance, remains an unsolved challenge. Here, we review studies that have used high resolution atomic force microscope imaging and spatial statistical analysis tools to understand crowding interactions between thiolated DNA probes immobilized on gold electrodes and how such interactions impact target binding. We also review related studies that attempt to control the nanoscale spatial arrangement of the immobilized recognition elements to optimize sensing performance. Furthermore, these efforts have led to new advances in understanding of the structure-function relationships of DNA-based electrochemical biosensors to move the field toward rational engineering of these biosensing interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗