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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Using Electrochemistry to Benchmark, Understand, and Develop Noble Metal Nanoparticle Syntheses

The complex chemical nature of metal nanoparticle synthesis presents obstacles for the mechanistic understanding of nanoparticle growth and predictive synthesis design, despite significant progress in this area. Real-time characterization of the chemical processes that take place throughout nanoparticle growth will enable progress toward addressing outstanding challenges in metal nanoparticle synthesis, such as mitigating synthetic reproducibility issues, defining chemical mechanisms that direct nanoparticle growth, and designing synthetic conditions for previously unachievable combinations of nanoparticle shape and composition. In this Perspective, we present open-circuit potential (OCP) measurements as an in situ, real-time method for characterizing chemical changes during nanoparticle growth and discuss the method’s strengths in comparison to and in combination with other characterization techniques. We propose the use of OCP measurements as benchmarks for troubleshooting irreproducibility and streamlining synthetic optimization. Finally, we explore possibilities for using the increased parameter space accessible by electrodeposition to accelerate the development of shape-selective nanoparticle syntheses.

benchmarking↗
DOE OSTI2024

Development of Half-Sandwich Ru, Os, Rh, and Ir Complexes Bearing the Pyridine-2-ylmethanimine Bidentate Ligand Derived from 7-Chloroquinazolin-4(3H)-one with Enhanced Antiproliferative Activity

Kinesin spindle protein (KSP) inhibitors are one of the most promising anticancer agents developed in recent years. Herein, we report the synthesis of ispinesib-core pyridine derivative conjugates, which are potent KSP inhibitors, with half-sandwich complexes of ruthenium, osmium, rhodium, and iridium. Conjugation of 7-chloroquinazolin-4(3H)-one with the pyridine-2-ylmethylimine group and the organometallic moiety resulted in up to a 36-fold increased cytotoxicity with IC 50 values in the micromolar and nanomolar range also toward drug-resistant cells. All studied conjugates increased the percentage of cells in the G 2 /M phase, simultaneously decreasing the number of cells in the G 1 /G 0 phase, suggesting mitotic arrest. Additionally, ruthenium derivatives were able to generate reactive oxygen species (ROS); however, no significant influence of the organometallic moiety on KSP inhibition was observed, which suggests that conjugation of a KSP inhibitor with the organometallic moiety modulates its mechanism of action.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Rational Modulation of Plant Root Development Using Engineered Cytokinin Regulators

Achieving precise control over quantitative developmental phenotypes is a key objective in plant biology. Recent advances in synthetic biology have enabled tools to reprogram entire developmental pathways; however, the complexity of designing synthetic genetic programs and the inherent interactions between various signaling processes remains a critical challenge. Here, we leverage Type-B response regulators to modulate the expression of genes involved in cytokinin-dependent growth and development processes. We rationally engineered these regulators to modulate their transcriptional activity (i.e., repression or activation) and potency while reducing their sensitivity to cytokinin. By localizing the expression of these engineered transcription factors using tissue-specific promoters, we can predictably tune cytokinin-regulated traits. As a proof of principle, we deployed this synthetic system in Arabidopsis thaliana to either decrease or increase the number of lateral roots. The simplicity and modularity of our approach makes it an ideal system for controlling other developmental phenotypes of agronomic interest in plants.

Cell signaling↗
DOE OSTI2026

Vision and Development of a Design, Implementation, and Verification Automation (DIVA) Software Platform for DNA Construction

Abstract DNA construction, while a prerequisite to many biological endeavors, is often a time-consuming distraction from an individual’s primary research objectives. We envisioned that with the right software infrastructure and cultural mindset, a single person could execute in parallel the batched DNA construction tasks of an entire research institute, at scales realizing efficiency gains through process and laboratory automation. In pursuit of this vision, we developed the Design, Implementation, and Verification Automation (DIVA) software platform. DIVA’s web interface enables researchers to design DNA constructs (using visual biological computer-aided design tools and biological parts repositories), submit designs for construction to dedicated staff, and track DNA construction as it progresses. DIVA supports the dedicated staff through the DNA construction process and records both successful and unsuccessful attempts toward improving the overall process. The platform is publicly available at public-diva.jbei.org and its open-source code through github.com/JBEI/DIVA.

Plahar, Hector [DOE Agile BioFoundry , , ,; DOE Jo↗
DOE OSTI2025

Development, Application, and Mechanistic Interrogation of a Dual Ni Catalysis Approach to Photoredox-Based C(sp 3 )–C(sp 3 ) Cross-Coupling

The installation of alkyl substituents such as methyl groups is a crucial tactic for the synthesis and diversification of medicinal compounds with improved pharmacological profiles. However, strategies that leverage methyl radical for C(sp 3 ) methylation remain underdeveloped due to the challenge of obtaining cross-selectivity between fleeting aliphatic radicals and alkyl electrophiles. Here, we report the development, application, and interrogation of a conceptually novel mechanistic framework for C(sp 3 )–Me bond formation using two distinct Ni catalysts capable of cross-coupling sterically and electronically diverse alkyl halides (chlorides and bromides) with methyl radical generated photocatalytically from benzaldehyde dimethyl acetal. Furthermore, by modifying the alkyl substituents on the acetal coupling partner, we demonstrate cross-couplings beyond methylation to access an array of 1°–1° and 1°–2° alkyl–alkyl bonds. Experimental and computational mechanistic studies provide support for cooperativity between an in situ-generated (bpy)Ni I (X) catalyst that facilitates XAT and inner-sphere C–C bond formation and a (Tp*)Ni II (acac) cocatalyst that captures methyl radical and engages in concurrent outer-sphere S H 2 coupling.

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

Development of Nontargeted Workflow of Occupational Exposure by Infrared Ion Spectroscopy and Silicone Wristbands’ Passive Sampling

Monitoring chemical exposure has well-established protocols and procedures for occupational health and industrial hygiene across various career fields and in a variety of occupational and environmental conditions. Iterative analytical development has led to validated methods to provide employers and employees with safe working conditions in terms of industrial hygiene. These validated methods are commonly supported by commercial laboratories. Here, this workflow is explicitly a targeted approach in which sampling media, sample preparation, chemical separation, quantitative target detection, and post processing are well established. This workflow also relies on commercially available chemical surrogates, isotopically labeled internal standards, and certified/standard reference materials from reputable vendors. An untargeted workflow lacks many key ingredients of validated analytical methods to include the known chemical target.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Developing Scenario‐Based Strategies for Health, Climate, and Environmental Preparedness: The One Health, One Earth Approach

Climate change amplifies many threats to human health. Despite advances in understanding climate change dynamics and impacts, there remains a critical gap in translating scientific knowledge into equitable, and community-driven health interventions. The inaugural One Earth, One Health workshop sought to explore this gap through human-centered design exercises involving interdisciplinary researchers from climate and Earth sciences, engineering, epidemiology, microbiology, and environmental health. Although participants did not co-develop solutions with affected communities, they used stakeholder role-playing to guide ideation and lay groundwork for actionable plans. Through these methods, participants identified community needs and proposed prototype solutions to alleviate health threats exacerbated by global environmental change. Prototypes were organized around infectious diseases, extreme weather, and air quality, as illustrative themes rather than an exhaustive set of risks. Key solutions included strategies for anticipatory systems and early warning (e.g., integrating environmental signals with health data), inclusive communication and infrastructure needs for responding to extreme weather events, and integrated platforms visualizing air quality trends to support tailored, context-aware guidance beyond one-size-fits-all alerts. The workshop highlighted opportunities such as leveraging machine learning, Earth observation, and real-time surveillance to protect communities, but also noted barriers including data quality, technological redundancy, privacy, and governance challenges. Additionally, participants emphasized the need for interdisciplinary teams capable of collaborating across sectors, breaking down silos and addressing gaps in training and education. Overall, the workshop illustrates how process-driven, human-centered approaches can help surface user needs and generate testable prototype concepts, while underscoring the importance of direct community partnership for implementation.

Abadi, Azar M. [University of Alabama, Birmingham,↗
DOE OSTI2025

Developing a Lagrangian Frame Transformation on Satellite Data to Study Cloud Microphysical Transitions in Arctic Marine Cold Air Outbreaks

Abstract Arctic marine cold air outbreaks (CAOs) generate distinct and dynamic cloud regimes due to intense air‐sea interactions. To understand the temporal evolution of CAO cloud properties and compare different CAO events, a Lagrangian perspective is particularly useful. We developed a novel technique that enables the conversion of inherently Eulerian satellite data into a Lagrangian framework, combining the broad spatiotemporal coverage of satellite observations with the advantages of Lagrangian tracking. This technique was applied to eight CAO cases associated with a recent field campaign. Our results reveal a striking contrast among the cases in terms of cloud‐top phase transitions, providing new insights into the evolution of CAO cloud properties.

Lagrangian analysis↗
DOE OSTI2025

Development of the United States GReenhouse Gas and Air Pollutants Emissions System (GRA 2 PES)

In the U.S., emissions of greenhouse gases and air pollutants are often developed independently. Here, we describe the GReenhouse gas And Air Pollutants Emissions System (GRA 2 PES), which provides gridded emissions of fossil-fuel carbon dioxide (ffCO 2 ) and 93 air quality (AQ) species for 17 combustion and non-combustion sectors at 4 km × 4 km spatial resolution across the contiguous US. We find that the AQ emissions most spatially correlated with ffCO 2 are nitrogen oxides (NO x , ρ = 0.67), followed by sulfur dioxide (SO 2 , ρ = 0.51), carbon monoxide (CO, ρ = 0.44), and fine particulate matter (PM 2.5 , ρ = 0.38). We evaluate GRA 2 PES ffCO 2 emissions with an ensemble of publicly available regional and global inventories at national (Normalized Mean Bias (NMB) = +1.4%), state (NMB = +1.5%, R 2 = 0.98), and urban (NMB = +11.5%, R 2 = 0.97) scales. Nationally, the differences of publicly available inventories from the ensemble average range from −10.0% to +5.7%, and consistency diverges at state and urban scales. We simulate GRA 2 PES ffCO 2 in a particle dispersion model and compare to measurements of radiocarbon ( 14 C)-derived ffCO 2 collected in Los Angeles (August 2021), with results suggesting that GRA 2 PES ffCO 2 may be low by 19% for this city, but well within model-observation differences for other publicly available inventories (−43% to +94%). GRA 2 PES AQ/ffCO 2 ratios converted to concentration space generally agree with field observations (NMB = +4%, log R 2 = 0.90). Lastly, we present a method by which to utilize GRA 2 PES to derive AQ emission fluxes from ffCO 2 emissions.

Lyu, Congmeng [National Oceanic and Atmospheric Ad↗
DOE OSTI2025

Perspectives on Systematic Cloud Microphysics Scheme Development With Machine Learning

Cloud microphysics—the collection of processes that govern the small‐scale formation, evolution, and interactions of liquid droplets and ice crystals in clouds and precipitation—remains a major source of uncertainty in weather and climate models. Although too small in scale to be explicitly resolved in any large‐eddy simulation, weather, or climate model, the representation of cloud microphysical processes has significant impact at the climate scale. Current microphysical schemes are limited by both parametric uncertainty, linked to uncertainty in physical parameter values, and structural uncertainty, arising from incomplete physical understanding of the processes at play or approximations made for computational efficiency. Recent advances in the application of machine learning (ML) to the physical sciences show significant potential for minimizing these limitations by leveraging high‐fidelity simulations and observations. Here we outline the challenges that must be addressed to apply ML toward cloud microphysics scheme development. This perspectives paper synthesizes recent progress in using data‐driven methods, including ML, to improve cloud microphysics parameterizations and highlights opportunities to address key uncertainties. We discuss the roles of aleatoric (irreducible, or statistical) and epistemic (reducible, or systematic) errors in contributing to microphysics parameterization uncertainty. ML can leverage observations to improve microphysical schemes via bottom‐up and top‐down constraints. Methods such as differentiable programming and ML‐enhanced sampling strategies and the creation of large scale benchmark data sets promise to bridge the gap between observations and models and to improve the consistency of cloud microphysical representation across temporal and spatial scales.

Lamb, Kara D. [Columbia Univ., New York, NY (Unite↗
DOE OSTI2025

Development and flight-testing of modular autonomous cultivation systems for biological plastics upcycling aboard the ISS

Cultivation of microorganisms in space has enormous potential to enable in-situ resource utilization (ISRU) Here, we develop an autonomous payload with fully programmable serial passaging and sample preservation, termed the Modular Open Biological Platform (MOBP), and flight-test the MOBP aboard the International Space Station (ISS) by conducting enzymatic and microbial plastics upcycling experiments. The MOBP is a compact, modular bioreactor system that allows for sustained microbial growth via automated media transfers, such as those for sample collection and storage for terrestrial analyses, and precise data monitoring from integrated sensors. The MOBP was flight-tested with two experiments designed to evaluate biological upcycling of the plastic poly(ethylene terephthalate) (PET). The bioproduct βKA can be polymerized into a nylon-6,6 analog with improved properties for use in the production of a variety of materials. We posit the MOBP will aid in democratizing the execution of synthetic biology in spaceflight towards enabling ISRU.

09 BIOMASS FUELS↗
DOE OSTI2025

Developing machine learning for heterogeneous catalysis with experimental and computational data

Machine learning techniques have emerged as a useful tool for identifying complex patterns and correlations in large datasets, such as associating catalyst performance to its physicochemical properties. In the heterogeneous catalysis communities, machine learning models have mostly been developed using high-throughput quantum chemistry calculations, with only a few case studies resulting in experimentally validated catalyst improvements. This limited success may be due to the use of simplified catalyst structures in computational studies and the lack of comprehensive experimental datasets. In this Review, we bring together studies integrating high-throughput approaches and machine learning for the advancement of solid heterogeneous catalysis, leveraging both experimental and computational data. We systematically analyze trends in the field, based on the descriptors used as model input and output; the materials, devices, or reactions investigated; the dataset size; and the overall achievements. Furthermore, for models reporting unitless R 2 values, we compare the performances based on these mentioned trends.

Computational chemistry↗
DOE OSTI2025

Development of an ultrahigh affinity, trimeric ACE2 biologic as a universal SARS-CoV-2 antagonist

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the COVID-19 pandemic, utilizes membrane-bound, angiotensin-converting enzyme II (ACE2) for internalization and infection. We describe the development of a biologic that takes advantage of the proximity of the N-terminus of bound ACE2 to the three-fold symmetry axis of the spike protein to create an ultrapotent, trivalent ACE2 entry antagonist. Distinct disulfide bonds were added to enhance serum stability and a single point mutation was introduced to eliminate enzymatic activity. Through surface plasmon resonance, pseudovirus neutralization assays, and single-particle cryo-electron microscopy, we show this antagonist binds to and inhibits SARS-CoV-2 variants. We further show the antagonist binds to and inhibits a 2003 SARS-CoV-1 strain. Collectively, structural insight has allowed us to design a universal trivalent antagonist against all variants of SARS-CoV-2 tested, suggesting it will be active against the emergence of future mutants.

Gonzales, Juliet (ORCID:0000000327219566)↗
DOE OSTI2024

Development of a coarse-grained molecular dynamics model for poly(dimethyl- co -diphenyl)siloxane

Polydimethylsiloxane is an important polymeric material with a wide range of applications. However, environmental effects like low temperature can induce crystallization in this material with resulting changes in its structural and dynamic properties. The incorporation of phenyl-siloxane components, e.g., as in a poly(dimethyl-co-diphenyl)siloxane random copolymer, is known to suppress such crystallization. Molecular dynamics (MD) simulations can be a powerful tool to understand such effects in atomistic detail. Unfortunately, all-atomistic molecular dynamics (AAMD) is limited in both spatial dimensions and simulation times it can probe. Here, to overcome such constraints and to extend to more useful length- and time-scales, we systematically develop a coarse-grained molecular dynamics (CGMD) model for the poly(dimethyl-co-diphenyl)siloxane system with bonded and non-bonded interactions determined from all-atomistic simulations by the iterative Boltzmann inversion (IBI) method. Additionally, we propose a lever rule that can be useful to generate non-bonded potentials for such systems without reference to the all-atomistic ground truth. Our model captures the structural and dynamic properties of the copolymer material with quantitative accuracy and is useful to study long-time dynamics of highly-entangled systems, sequence-dependent properties, phase behaviour, etc.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Development and investigation of efficient resonance ionization mass spectrometry schemes of gadolinium

Resonance ionization mass spectrometry of gadolinium can be used for nuclear forensics and to further the understanding of stellar nucleosynthesis but has been used only a handful of times due to the high laser power required and interference from non-resonant ionization of molecules of other elements. Herein we present the development of two novel resonance ionization spectroscopy schemes for gadolinium that provide improvements in isotopic fractionation and ionization efficiency, respectively, opening new applications for gadolinium analysis. The schemes are demonstrated and compared in a mixed sample of gadolinium and neodymium.

and nuclear chemistry↗
DOE OSTI2025

Development of High-Granularity Dual-Readout Calorimetry with psec Timing

Dual-readout and particle flow algorithm (PFA) are technologies proposed for precise jet energy measurement in future colliders. While PFA requires highly granular calorimeters, dual-readout has mainly been used with fiber-based calorimeters that do not have highly segmented capabilities. It is still non-trivial to combine these two technologies in one calorimeter system because of the use of fibers in most of the dualreadout calorimeters, which is not compatible with the high granularity requirement of PFA technologies. The aim of this study is to develop a novel calorimetry that combines dual-readout and PFA by adopting a highly segmented tile-based configuration. This paper compares the improvement of energy resolution using dual-readout approach across several configurations of highly granular hadron calorimeters through simulation. Results indicate that setups with fine sampling and close placement of scintillators and Cherenkov detectors improve dual-readout performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

The FLUKA code: Overview and new developments

The FLUKA Monte Carlo Radiation Transport and Interaction code package is widely used to simulate the interaction of particles with matter in a variety of fields, including high energy physics, space radiation, medical applications, radiation protection and shielding assessments, accelerator studies, astrophysical studies and well logging. This paper gives a brief overview of the FLUKA program and describes recent developments, in particular, improvements in the modelling of particle interactions and transport are described in detail. In addition, an overview of selected applications is given.

Ballarini, Francesca↗