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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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Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products
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PubChemLite Plus Collision Cross Section (CCS) Values for Enhanced Interpretation of Nontarget Environmental Data
Finding relevant chemicals in the vast (known) chemical space is a major challenge for environmental and exposomics studies leveraging nontarget high resolution mass spectrometry (NT-HRMS) methods. Chemical databases now contain hundreds of millions of chemicals, yet many are not relevant. This article details an extensive collaborative, open science effort to provide a dynamic collection of chemicals for environmental, metabolomics, and exposomics research, along with supporting information about their relevance to assist researchers in the interpretation of candidate hits. The PubChemLite for Exposomics collection is compiled from ten annotation categories within PubChem, enhanced with patent, literature and annotation counts, predicted partition coefficient (logP) values, as well as predicted collision cross section (CCS) values using CCSbase. Monthly versions are archived on Zenodo under a CC-BY license, supporting reproducible research, and a new interface has been developed, including historical trends of patent and literature data, for researchers to browse the collection. This article details how PubChemLite can support researchers in environmental and exposomics studies, describes efforts to increase the availability of experimental CCS values, and explores known limitations and potential for future developments. The data and code behind these efforts are openly available.
Five-analyzer Johann spectrometer for hard X-ray photon-in/photon-out spectroscopy at the Inner Shell Spectroscopy beamline at NSLS-II: design, alignment and data acquisition
Here, a recently commissioned five-analyzer Johann spectrometer at the Inner Shell Spectroscopy beamline (8-ID) at the National Synchrotron Light Source II (NSLS-II) is presented. Designed for hard X-ray photon-in/photon-out spectroscopy, the spectrometer achieves a resolution in the 0.5–2 eV range, depending on the element and/or emission line, providing detailed insights into the local electronic and geometric structure of materials. It serves a diverse user community, including fields such as physical, chemical, biological, environmental and materials sciences. This article details the mechanical design, alignment procedures and data-acquisition scheme of the spectrometer, with a particular focus on the continuous asynchronous data-acquisition approach that significantly enhances experimental efficiency.
Data used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”
The package contains the data layers used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”. Spatial data layers include: topography, wetland vegetation cover, time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. The study aims to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a functional zonation approach based on machine learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region
Yesterday’s extremes, today’s new normal: flood risk in the Kathmandu Valley, Nepal
Unplanned urban growth has left many cities increasingly vulnerable to extreme rainfall events, particularly in regions with inadequate drainage infrastructures and development encroaching on natural floodplains. Here, in this perspective paper, we examine the September 2024 floods that struck Central Nepal, triggered by a persistent low-pressure system and enhanced by converging moisture flows from the Arabian Sea and the Bay of Bengal which led to widespread catastrophic damage. In the Kathmandu Valley, floodwaters expanded to more than 2.5 times the bankfull water extent, causing significant damage to housing, transportation network, and critical infrastructure, displacing thousands of residents, and severely disrupting urban services. This event highlights the urgent need for improved flood management strategies that integrate both structural and non-structural measures into the infrastructure development. While early warning systems provided critical lead time, challenges remain in reducing forecasting uncertainties and improving communication across government agencies and with local communities. A forward-looking approach is essential, including probabilistic flood forecasting systems, sustainable floodplain management, risk-sensitive land use planning, climate- and disaster- resilient infrastructure development, and the integration of nature-based solutions like urban green and blue spaces to mitigate flood impacts. By involving local communities in planning and preparedness efforts, particularly through citizen science initiatives, and engagement with underserved and disadvantaged communities, Nepal can better adapt to the growing risks posed by extreme rainfall and urban flooding and enhance long-term disaster resilience in rapidly urbanizing areas like Kathmandu Valley.
Laser pre-sintering for denudation reduction in the laser powder bed fusion additive manufacturing of Ti-6Al-4V alloy
As a fundamentally different process for creation of complex parts, laser powder bed fusion (LPBF) additive manufacturing exhibits complex behavior at the melt pool that has a direct influence on the macroscopic properties of the final material. Powder denudation, a previously studied deleterious phenomenon, is the displacement of powder due to entrainment in a jet of rapidly vaporized metal created by the process laser. To minimize the effect of denudation and inspired by methods in adjacent fields, a laser pre-sintering (LPS) method was studied in an effort to reduce denudation. LPS was found to be most effective in the 1–4 J/mm3 volumetric energy density processing regimes, minimizing the common experimentally observed problems with the LPBF process. Scanning electron microscopy of the LPS-treated powder bed revealed insight into the accelerating effect morphological irregularities along the powder particle surface have in the pre-sintering process. To further understand the process, an estimation of the sintered neck sizes necessary to eliminate denudation in LPS is made, qualitatively affirming the low energy densities needed to prevent denudation. Here, the results presented in this work represent an initial step towards understanding and implementing LPS as a quality-enhancing step in LPBF.
Use of kaolinite clays in development of a low carbon MgO-clay binder system
Magnesium oxide based cements may provide a promising alternative to the conventional Portland cement in many applications. This study investigates the feasibility of using calcined kaolinitic clay to produce an MgO binder. The MgO-based binder were prepared using a low kaolinite content clay and metakaolin and were compared with a silica fume system. Implications of the addition of magnesium carbonate in the binder were also investigated. Isothermal calorimetry, X-ray diffraction (XRD), thermogravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FTIR) were used to study the hydration characteristics of the binder system whereas compressive strength and porosity were measured to determine mechanical and durability attributes. The economic and environmental aspects of the binder system is also discussed. Hydrotalcite like phases were clearly produced on hydration in the clay mixes containing carbonate additions. The compressive strength of clay mixes was at par or better compared to systems containing silica fume, and the clay systems had significantly lower porosity levels. The presence of magnesium carbonate further enhanced the physical properties of the clay mixes.
Intrinsic and hard magnetic properties of (Sm 1-x R x )-Fe-Co-V alloys (R = Gd, Zr, and Y)
SmFe 12 -based compounds with the ThMn 12 -type structure have a great potential as future rare-earth-lean permanent magnets. However, their reliance on stabilizing non-magnetic elements has impeded practical applications. Therefore, elements such as Gd, Y, and Zr have gained popularity as candidates to minimize the need for stabilizing elements by reducing the formation energy of the ThMn 12 (1:12) phase. Here, this study examines the effect of Gd, Y, and Zr on the intrinsic and hard magnetic properties in (Sm 1-x R x ) 1.2 Fe 8.4 Co 2.1 V 1.5 (or with nominal composition of (Sm 1-x R x ) 9.1 Fe 63.6 Co 15.9 V 11.4 ) with a single 1:12 phase for 0 ≤ x ≤ 0.3. Notably, samples substituted with Gd exhibit an enhanced temperature dependence of the magnetic properties.
Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning
The use of machine learning (ML) to refine low-level theoretical calculations to achieve higher accuracy is a promising and actively evolving approach known as Δ-ML. The density matrix renormalization group (DMRG) is a powerful variational approach widely used for studying strongly correlated quantum systems. High computational efficiency can be achieved without compromising accuracy. Here, we demonstrate the potential of a simple ML model to significantly enhance the performance of the quantum chemical DMRG method.
A chemical kinetics perspective on thermoelectric transport
There is a growing community of chemists and materials scientists exploring thermoelectric properties of organic, polymeric, hybrid, and composite materials. Many of these materials are nondegenerate semiconductors, meaning that the Fermi and transport charge transport energy levels are significantly offset. Others are ionic conductors. While the meaning of the essential parameters electrical conductivity (σ) and Seebeck coefficient (S, the voltage difference per degree of temperature difference) are accessible, the origins of S are not readily apparent to one trained mainly in chemistry. The purpose of this manuscript is to illustrate S as being the result of a steady state difference between concentrations of two designations of charge carriers, those on hotter and those on colder sides of a material sample, analogous to the equilibrium among interconverting chemical species that differ in enthalpy and entropy. The ion-based Seebeck coefficient, also known as the Soret effect, can be explained using principles similar to those applicable to electrons and holes. We hope that this analysis leads to wider understanding of the origins of S through an explanation using the language of chemical kinetics and thermodynamics, and appreciation of ways that thermoelectric efficiency can be enhanced in emerging materials.
Improved deep learning prediction of antigen–antibody interactions
Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning–based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen–antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen–antibody interactions.
Magnetite nanoparticle anchored graphene cathode enhances microbial electrosynthesis of polyhydroxybutyrate by Rhodopseudomonas palustris TIE-1
Microbial electrosynthesis (MES) is an emerging technology that can convert carbon dioxide (CO 2 ) into value-added organic carbon compounds using electrons supplied from a cathode. However, MES is affected by low product formation due to limited extracellular electron uptake by microbes. Herein, a novel cathode was developed from chemically synthesized magnetite nanoparticles and reduced graphene oxide nanocomposite (rGO-MNPs). This nanocomposite was electrochemically deposited on carbon felt (CF/rGO-MNPs), and the modified material was used as a cathode for MES production. The bioplastic, polyhydroxybutyrate (PHB) produced by Rhodopseudomonas palustris TIE-1 (TIE-1), was measured from reactors with modified and unmodified cathodes. Results demonstrate that the magnetite nanoparticle anchored graphene cathode (CF/rGO-MNPs) exhibited higher PHB production (91.31 ± 0.9 mg l -1 ). This is ~4.2 times higher than unmodified carbon felt (CF), and 20 times higher than previously reported using graphite. This modified cathode enhanced electron uptake to -11.7 ± 0.1 μ A cm -2 , ~5 times higher than CF cathode (-2.3 ± 0.08 μ A cm -2 ). The faradaic efficiency of the modified cathode was ~2 times higher than the unmodified cathode. Electrochemical analysis and scanning electron microscopy suggest that rGO-MNPs facilitated electron uptake and improved PHB production by TIE-1. Overall, the nanocomposite (rGO-MNPs) cathode modification enhances MES efficiency.
Remote operation of the DIII-D National Fusion Facility
Abstract Full remote scientific operation of the DIII-D National Fusion Facility is now possible through significant advances in the computer science hardware and software infrastructure made over the last decade. Capabilities around information visualization, data movement, and communication have all been enhanced. The level of capability deployed to remotely operate DIII-D required an infrastructure advancement over what had previously been achieved in the fusion community. The large quantity of real-time data that is automatically displayed on DIII-D’s control room screens can now be visualized by remote participants via web-based applications. New audio/video solutions using the VoIP and instant messaging application Discord have been implemented to mimic the dynamic and ad-hoc scientific conversations that are critical in successfully operating an experimental campaign. Discord’s ability for a user to rapidly move between audio channels, text with images, and share screens is a significant enhancement over traditional videoconferencing tools. In addition, multiple combinations of broadcast audio are made available via a web-based application to allow remote participants to simultaneously listen to general announcements/sounds while conducting their own specific conversations. Secure methodologies have been put into place to allow remote control of hardware including DIII-D’s plasma control system application. Secure methods also included the ability of the on-site team to closely coordinate their work with remote team members which has been enhanced through extensions to the wireless network and the use of tablet computers for audio/video/screen sharing. However, no amount of software can fully replace the need for ‘hands on hardware.’ This infrastructure was severely stress tested during the COVID-19 pandemic where occupancy of the DIII-D control room was restricted. Operational efficiency during the pandemic, measured in discharges per hour, remained high (3.8 ± 0.8) compared to values obtained pre-pandemic (3.7 ± 0.8).
Quantum-enhanced detection of viral cDNA via luminescence resonance energy transfer using upconversion and gold nanoparticles
Abstract The COVID-19 pandemic has profoundly impacted global economies and healthcare systems, revealing critical vulnerabilities in both. In response, our study introduces a sensitive and highly specific detection method for cDNA, leveraging Luminescence Resonance Energy Transfer (LRET) between upconversion nanoparticles (UCNPs) and gold nanoparticles (AuNPs), and achieves a detection limit of 242 fM for SARS-CoV-2 cDNA. This innovative sensing platform utilizes UCNPs conjugated with one primer and AuNPs with another, targeting the 5′ and 3′ ends of the SARS-CoV-2 cDNA, respectively, enabling precise differentiation of mismatched cDNA sequences and significantly improving detection specificity. Through rigorous experimental analysis, we established a quenching efficiency range from 10.4 % to 73.6 %, with an optimal midpoint of 42 %, thereby demonstrating the superior sensitivity of our method. Our work uses SARS-CoV-2 cDNA as a model system to demonstrate the potential of our LRET-based detection method. This proof-of-concept study highlights the adaptability of our platform for future diagnostic applications. Instrumental validation confirms the synthesis and formation of AuNPs, addressing the need for experimental verification of the preparation of nanomaterial. Our comparative analysis with existing SARS-CoV-2 detection methods revealed that our approach provides a low detection limit and high specificity for target cDNA sequences, underscoring its potential for targeted COVID-19 diagnostics. This study demonstrates the superior sensitivity and adaptability of using UCNPs and AuNPs for cDNA detection, offering significant advances in rapid, accessible diagnostic technologies. Our method, characterized by its low detection limit and high precision, represents a critical step forward in developing next-generation biosensors for managing current and future viral outbreaks. By adjusting primer sequences, this platform can be tailored to detect other pathogens, contributing to the enhancement of global healthcare responsiveness and infectious disease control.
Effects of Experimental Warming and Elevated CO 2 on CO 2 and CH 4 Fluxes in an Ombrotrophic Black-Spruce. Final Report
High latitude peatlands and permafrost soils contain nearly half of the soil carbon pool on Earth. The potential for large soil C stocks in peatlands and permafrost to become a positive feedback to climate change by increasing net emissions of CO₂ and CH₄ to the atmosphere is a subject of grave concern. This research was conducted at the Spruce and Peatland Responses Under Changing Environments (herein SPRUCE) experimental facility, located in the USDA’s Marcell Experimental Forest in northern Minnesota. The site is dominated by Picea mariana [herein black spruce], ericaceous shrubs and Sphagnum spp. Ten 12m-diameter open top chambers expose portions of the 8.1-ha S1 bog to warming up to +9°C in 2.25°C increments at ambient and elevated CO₂ (eCO₂, 900ppm). The Finzi lab group began manual measurements of CO₂ and CH₄ fluxes and their C-isotopic compositions in 2014. With funding from the DOE-TES program automated measurements of these species began in 2015. With a one-year no cost extension we were able to make measurements through the 2018 growing season. Across the study period we find that experimental warming of the peatland significantly increases the flux of CO₂ and CH₄ to the atmosphere. The highest temperature treatments had the highest fluxes. As peatlands have complex topography and water table dynamics, many of these results were contingent upon the topographic location in which the measurements were made. In the raised portions of the peatland divorced from the water table, fluxes of CO₂ were high and fluxes of CH₄ were low. By contrast, in the lower portions of the peatland surface, which is at or very near the water table, fluxes of CH₄ were substantially higher. A significant fraction of the total annual CH₄ flux was derived from episodic ebullitive fluxes. The occurrence and size of these ebullitive fluxes also increased with temperature. Experimental fumigation of the black spruce peatland with elevated concentrations of atmospheric CO₂ [herein eCO₂] also had significant effects on emissions of greenhouse gasses. In general these effects were far more subtle than the effects of rising temperature. This indicates that temperature change more than atmospheric chemistry changes are affect the belowground cycle of C in the peatland. Overall, this study shows that boreal peatlands are highly sensitive to changes in temperature. The warmer it becomes the more C will be lost from the peatland to the atmosphere. This appears to create a positive feedback loop that is likely to enhance the concentration of greenhouse gases in the atmosphere.
Demonstration and Validation of Additively Manufactured Intensified Device for Enhanced Carbon Capture
In FY 2018 and 2019, ORNL personnel designed, prototyped, characterized, and tested the enhancement of amine-based carbon capture using additively manufactured intensified devices, i.e., devices combining multiple thermodynamic and kinetic processes into one unit operation. State of the art scrubbing technology often employs inter-stage cooling which requires multiple stages of single-purpose unit operations, leading to larger equipment size, higher capital cost, and sometimes less than optimal operating conditions for each piece of equipment. It was proposed that integrating multiple thermodynamic and kinetic operations into one unit could mitigate or negate these drawbacks. Heat and mass transfer studies demonstrated that an additively manufactured intensified device could significantly improve capture efficiency at bench scale. This project is a continuation of FEAA130 and aims at (1) demonstration and validation of enhanced CO 2 capture with 3D printed intensified devices (i.e., mass exchanger packing with internal cooling channels), for low-aqueous-solvent (LAS) based capture at the ORNL-constructed laboratory-scale (4.5-feet tall) column, namely Column A, with built-in cooling and (2) computational scoping on the conceptual design of a flexible and modular larger column, namely Column B, at ORNL for further demonstration of enhanced capture for aqueous solvent. To execute this multi-faceted project scope, an integrated project team from ORNL’s Manufacturing Science Division is tasked with applying capabilities in computational fluid dynamics (CFD), additive manufacturing, and absorber-scale demonstration/validation experiments.
Development of Low-cost Magnetocaloric Materials - CRADA 446
The overall goal of the project is to develop a faster heat-treatment process to produce low-cost high-performance magnetocaloric materials (MCM) for magnetic refrigeration technology. This research is in support of DOE-Fuel Cell Technology Office’s efforts to develop energy-efficient magnetic refrigeration technology for hydrogen liquefaction. Magnetic refrigeration offers a lower-cost and more energy-efficient alternative to traditional compression-based refrigeration technologies for liquefaction of gaseous hydrogen. Thus, PNNL is working with its industrial partner who is engaged in discovery, development and commercialization of low-cost high-performance MCM for use in magnetic refrigeration systems. Currently used MCM are typically a mixture of rare-earth and other elements. These are made in small batches and often need to be annealed for long durations, ranging from days to weeks, to obtain a good performance. Such extremely long heat-treatments add to the overall cost and increase the lead time to market of the commercial product. Therefore, this project will use recently developed innovative heat-treatment techniques at PNNL to shorten the annealing times and enhance the performance and lower the processing cost of MCM.