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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Circadian clock control of ribosome composition promotes rhythmic translation and termination fidelity

Ribosome composition is dynamic, shifting with cell state and stress, but whether it varies with circadian time is unknown. Here, we uncover circadian clock-driven changes in ribosome composition in Neurospora crassa . Mass spectrometry of ribosomes across circadian time identified six ribosomal proteins and one associated factor under clock control. Rhythms in eL31 abundance were validated in purified ribosomes, and deletion of el31 disrupted translation rhythms in nearly half of rhythmically translated mRNAs. N. crassa eL31 promotes circadian control of translation termination and impacts elongation fidelity while maintaining Mg homeostasis, a key determinant of translational accuracy. These findings reveal that the circadian clock reprograms ribosome composition to orchestrate rhythmic translation and fidelity, temporally expanding the proteome beyond the static genome to align cellular function with time of day.

circadian clock↗
DOE OSTI2026

Alcoholysis of nylon 6 waste to ε-caprolactam promoted by phosphoric acid

Nylon 6 is widely used in high-performance materials. However, the inherent structural rigidity of nylon 6 inhibits catalytic depolymerization to its monomer, ε-caprolactam. Here, in this study, we demonstrate an acid-catalyzed alcoholysis process that depolymerizes nylon 6 to ε-caprolactam, achieving yields up to 74% using n-propanol as solvent and phosphoric acid as catalyst at 220°C for 2 h. We further applied the process to commercial nylon-containing products, demonstrating ε-caprolactam yields of 67% to 76%. Process modeling and techno-economic analysis estimated a minimum selling price of recycled nylon 6 at $\$$1.79/kg, 30% lower than the 5-year average market price of virgin nylon 6. These estimates depend on assumed costs and process performance and may vary with future conditions. Life cycle assessment indicated that nylon 6 produced via alcoholysis can reduce greenhouse gas emissions by up to 63% compared with primary production. Overall, this study demonstrates a facile, cost-effective, and environmentally beneficial process for nylon 6 chemical recycling.

09 BIOMASS FUELS↗
DOE OSTI2026

The interaction of acetonitrile with H-terminated and depassivated (100) diamond surfaces

Molecular adsorption on reactive semiconductor surfaces may enable novel surface functionalisation and atomically-precise dopant formation. In this work synchrotron-based x-ray photoelectron spectroscopy and near edge x-ray absorption spectroscopy are used to examine the interaction between acetonitrile and diamond (100) surfaces under ultrahigh vacuum (UHV) conditions. We observe two surface-bound nitrogen species on hydrogen terminated surfaces after thermal depassivation or following synchrotron radiation and subsequent exposure to acetonitrile, but find no evidence of nitrogen adsorption on the hydrogen-terminated diamond surface, at room temperature. Subsequent measurements explore the stability of the surface-bound nitrogen under various temperature conditions and with atmospheric exposure. While the nitrogen coverage achieved in this study is low, this study serves as a proof-of-concept demonstration of a UHV molecular dosing approach to selective nitrogen functionalisation of diamond surfaces as an alternative to plasma-based approaches. This development may be critical for proposed surface-based fabrication workflows using atomic placement of nitrogen-vacancy centres to create quantum devices.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

A neural-network-enhanced parameter-varying framework for multi-objective model predictive control applied to buildings

Management of the electrical grid is becoming more complex due to the increased penetration of alternative energy generation technologies and a broadening diversity of electric loads. This complexity creates challenges in balancing demand and generation that can increase the potential for grid instabilities. One effective way to address this issue is to leverage previously unexploited demand flexibility through advanced control strategies. In this work, we propose an advanced control method, called adaptive neural parameter-varying model predictive control (ANPV-MPC), to control the temperature and energy consumption of a building via its Heating, Ventilation, and Air Conditioning system. ANPV-MPC combines key ideas in parameter-varying control, adaptive control, and online learning strategies to bridge the gap between computationally efficient linear model predictive control and more accurate nonlinear model predictive control. The novelty in ANPV-MPC is the use of a physics-inspired Bayesian neural network to estimate the coefficients of the parameter-varying linear control model. The Bayesian neural network additionally provides uncertainty estimates, triggering online training to capture evolving building system conditions. We show that ANPV-MPC can approximate the building system dynamics with a 28.39% higher accuracy than traditional linear model predictive control, resulting in 36.23% better control performance without increasing complexity of the optimal control problem. ANPV-MPC also adapts in real time to previously unseen conditions using online learning, further improving its performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

A bi-level advanced control framework for large-scale control of buildings with system-level impact

Increased electricity consumption combined with new forms of generation is testing the reliability of our grid infrastructure. This work describes a method to improve the reliability of the grid through large-scale advanced building control. This paper develops a bi-level distributed control framework to shift the load of 153 buildings to achieve a system-level objective of tracking a power reference signal. This bi-level control is based on the previously-developed ANPV-MPC, a predictive controller that uses a Bayesian neural network to generate an accurate control model and adapt to changing conditions over time. By shifting the building electricity demand to better match the available power, the grid system supplying the buildings is more reliable as evidenced by the analysis of node voltages across an IEEE 13-bus distribution system. The proposed bi-level control framework tracks the system-level power reference with enough accuracy to regulate node voltages across the IEEE 13-bus distribution system within ANSI limits of ±5%. Additionally, the adaptive nature of ANPV-MPC allows each building across the system to adapt to changing conditions, further amplifying the system-level reliability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2027

Hydrotreating pine-derived catalytic fast pyrolysis oil to jet fuel: Process durability and impact of operating conditions

Hydrotreating woody biomass-derived catalytic fast pyrolysis (CFP) oil to jet fuel has the potential to enhance energy security due to the large abundance of forest and woody resources. CFP oil produced from pine over ZSM-5 was hydrotreated over sulfided NiMo/Al 2 O 3 for 410 h to study the impact of hydrotreating conditions (pressure, temperature, and weight hourly space velocity (WHSV)). The degree of deoxygenation remained high across the tested hydrotreating conditions, and the products contained < 0.01 wt% oxygen. Increasing hydrotreating pressure from 84 to 125 bar enhanced hydrogenation of aromatic rings and the formation of cycloalkanes and increasing the temperature from 385 to 400 degrees C enhanced cracking and the formation of jet-range molecules. Decreasing the weight hourly space velocity from 0.2 to 0.1 g/(g cat h) further enhanced cracking and led to higher gasoline- and lower jet fuel-range fractions. The highest jet fuel fraction (49 wt%) was obtained at 400 degrees C, 125 bar, 0.2 g/(g cat h) WHSV. CFP oils produced from pine over phosphorous-modified ZSM-5 were hydrotreated at this condition for 588 h to investigate the process durability. The hydrogenation performance of the catalyst gradually stabilized in approximately 300 h after which the product composition remained constant. Of the final hydrotreated CFP oil, 53 wt% fell in the jet fuel range with most of the tested fuel properties meeting ASTM D4054 and/or ASTM D7566 specifications. The freeze points were < -50 degrees C vs. guideline of < -40 degrees C and the viscosities at -20 degrees C were 3.8-4.1 mm 2 /s vs guideline of 8 mm 2 /s. Increasing the fraction of cycloalkanes could increase the cetane number and heating value, which did not meet the guidelines.

09 BIOMASS FUELS↗
DOE OSTI2026

Predicting U 3 O 8 powder processing conditions: An AI/ML approach analyzing deep learning embeddings of SEM micrographs

High-resolution SEM images of uranium-oxide powders encode micro- and nanoscale clues to their synthesis route and calcination temperature. We trained a ResNet-50 model on 11 commercial-scale U₃O₈ classes, ammonium diuranate (ADU) or uranyl peroxide (H₂O₂) precursors calcined at temperatures ranging from 400 to 750 °C and added a 256-D projection head before the classifier to analyze the learned representation. The best of eight seeds reached 92.4 % accuracy on reserved testing data, but our focus is the structure of the embedding space rather than the accuracy and labels. We quantify class relatedness in the original 256-D space using centroid similarity and distributional distances, and we use Uniform Manifold Approximation Projection (UMAP) for visualization. ‘Unknown’ images from different preparation methods, SEM operators, and from the literature localized near the expected classes under a nearest-centroid analysis without retraining, as well as clustered in similar UMAP space. In conclusion, this embedding-centered workflow complements black-box classification by providing quantitative, similarity-based comparisons of U₃O₈ morphologies and reduces storage space by up to 98 % for image data used in millisecond vector search comparisons.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multiview Incomplete Knowledge Graph Integration with application to cross-institutional EHR data harmonization

Objective: The growing availability of electronic health records (EHR) data opens opportunities for integrative analysis of multi-institutional EHR to produce generalizable knowledge. A key barrier to such integrative analyses is the lack of semantic interoperability across different institutions due to coding differences. We propose a Multiview Incomplete Knowledge Graph Integration (MIKGI) algorithm to integrate information from multiple sources with partially overlapping EHR concept codes to enable translations between healthcare systems. Methods: The MIKGI algorithm combines knowledge graph information from (i) embeddings trained from the co-occurrence patterns of medical codes within each EHR system and (ii) semantic embeddings of the textual strings of all medical codes obtained from the Self-Aligning Pretrained BERT (SAPBERT) algorithm. Due to the heterogeneity in the coding across healthcare systems, each EHR source provides partial coverage of the available codes. MIKGI synthesizes the incomplete knowledge graphs derived from these multi-source embeddings by minimizing a spherical loss function that combines the pairwise directional similarities of embeddings computed from all available sources. MIKGI outputs harmonized semantic embedding vectors for all EHR codes, which improves the quality of the embeddings and enables direct assessment of both similarity and relatedness between any pair of codes from multiple healthcare systems. Results: With EHR co-occurrence data from Veteran Affairs (VA) healthcare and Mass General Brigham (MGB), MIKGI algorithm produces high quality embeddings for a variety of downstream tasks including detecting known similar or related entity pairs and mapping VA local codes to the relevant EHR codes used at MGB. Based on the cosine similarity of the MIKGI trained embeddings, the AUC was 0.918 for detecting similar entity pairs and 0.809 for detecting related pairs. For cross-institutional medical code mapping, the top 1 and top 5 accuracy were 91.0% and 97.5% when mapping medication codes at VA to RxNorm medication codes at MGB; 59.1% and 75.8% when mapping VA local laboratory codes to LOINC hierarchy. When trained with 500 labels, the lab code mapping attained top 1 and 5 accuracy at 77.7% and 87.9%. MIKGI also attained best performance in selecting VA local lab codes for desired laboratory tests and COVID-19 related features for COVID EHR studies. Compared to existing methods, MIKGI attained the most robust performance with accuracy the highest or near the highest across all tasks. Conclusions: The proposed MIKGI algorithm can effectively integrate incomplete summary data from biomedical text and EHR data to generate harmonized embeddings for EHR codes for knowledge graph modeling and cross-institutional translation of EHR codes.

Zhou, Doudou↗
DOE OSTI2024

Recommendations to standardize reporting, execution, and interpretation of X-ray Absorption Spectroscopy measurements

In July of 2022, a National Science Foundation (NSF)- and US Department of Energy (DOE)-sponsored workshop was held on the topic of rigor and reproducibility (R&R) in thermal, heterogeneous catalysis. As a result of the workshop, a report has been published entitled "Addressing Rigor and Reproducibility in Thermal, Heterogeneous Catalysis"[1]. This manuscript summarizes one section of the report: "Recommendations for catalyst characterization by X-ray absorption spectroscopy". Here, the section focuses exclusively on X-ray absorption spectroscopy (XAS) and is narrowly focused on recommendations for the collection and interpretation of XAS data for routine experiments. Therefore, one should not expect a detailed guide for each different experiment that one may perform nor are the recommendations designed to be overly prescriptive. However, the report is designed to draw attention to key pitfalls and common problems in the collection, reporting, analysis and interpretation of XAS studies.

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

Recommendations to standardize reporting, execution and interpretation of STEM/TEM measurements

In July of 2022, a National Science Foundation (NSF)- and US Department of Energy (DOE)-sponsored workshop was held on the topic of rigor and reproducibility (R&R) in thermal, heterogeneous catalysis. A report has been published entitled “Addressing Rigor and Reproducibility in Thermal, Heterogeneous Catalysis” as a result of the workshop [1]. This manuscript attempts to summarize one section of the report entitled Recommendations for catalyst characterization by transmission electron microscopy.

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

A Markov chain Monte Carlo (MCMC) Bayesian inference approach to analyze apparent activation barriers and reaction orders from microreactor data

Statistical analysis of steady-state catalytic kinetic data is often limited by data sparsity due to the slow pace at which the data is collected. Data sparsity and limitations in statistical analysis make it difficult to differentiate between mechanistic models and catalytic sites. A Bayesian inference tool is reported for catalysis researchers to estimate error in the determination of reaction orders from steady state microreactor data. The benefits of a Bayesian inference approach are discussed, as an alternative to the more common frequentist approach. The approach incorporates prior knowledge of the system and the data collected to form an error estimate on reaction orders. We investigated the effects of three distinct data treatments—individual fitting of trials, pooled analysis, and constrained regression methods—on the precision and uncertainty of reaction order determinations. To assess the robustness of our findings, we conducted sensitivity analyses to evaluate the influence of Bayesian parameters on uncertainty estimation. Additionally, we utilized synthetic data to illustrate how data quality impacts the precision of uncertainty assessments. We show Bayesian analysis can obtain a more precise estimation of error with a sparse data set than a frequentist analysis. Finally, this work provides strong evidence that the adoption of Bayesian analysis of kinetic data may help researchers make more precise arguments as to the strength of their evidence for a particular mechanistic hypothesis, or in comparing across different catalysts.

42 ENGINEERING↗
DOE OSTI2021

Conformational Changes of RORγ During Response Element Recognition and Coregulator Engagement

The retinoic acid receptor-related orphan receptor γ (RORγ) is a ligand-dependent transcription factor of the nuclear receptor super family that underpins metabolic activity, immune function, and cancer progression. Despite being a valuable drug target in health and disease, our understanding of the ligand-dependent activities of RORγ is far from complete. Like most nuclear receptors, RORγ must recruit coregulatory protein to enact the RORγ target gene program. To date, a majority of structural studies have been focused exclusively on the RORγ ligand-binding domain and the ligand-dependent recruitment of small peptide segments of coregulators. Herein, we examine the ligand-dependent assembly of full length RORγ:coregulator complexes on cognate DNA response elements using structural proteomics and small angle x-ray scattering. The results from our studies suggest that RORγ becomes elongated upon DNA recognition, preventing long range interdomain crosstalk. We also determined that the DNA binding domain adopts a sequence-specific conformation, and that coregulatory protein may be able to ‘sense’ the ligand- and DNA-bound status of RORγ. We propose a model where ligand-dependent coregulator recruitment may be influenced by the sequence of the DNA to which RORγ is bound. Overall, the efforts described herein will illuminate important aspects of full length RORγ and monomeric orphan nuclear receptor target gene regulation through DNA-dependent conformational changes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Diamond rotors

The resolution of magic angle spinning (MAS) nuclear magnetic resonance (NMR) spectra remains bounded by the spinning frequency, which is limited by the material strength of MAS rotors. Since diamond is capable of withstanding 1.5–2.5x greater MAS frequencies, compared to state-of-the art zirconia, we fabricated rotors from single crystal diamond. When combined with bearings optimized for spinning with helium gas, diamond rotors could achieve the highest MAS frequencies to date. Furthermore, the excellent microwave transmission properties and thermal conductivity of diamond could improve sensitivity enhancements in dynamic nuclear polarization (DNP) experiments. The fabrication protocol we report involves novel laser micromachining and produced rotors that presently spin at ω r = 111.000 ± 0.004 kHz, with stable spinning up to 124 kHz, using N 2 gas as the driving fluid. Here, we present the first proton-detected 13 C/ 15 N MAS spectra recorded using diamond rotors, a critical step towards studying currently inaccessible ex-vivo protein samples with MAS NMR. Previously, the high aspect ratio of MAS rotors (~10:1) precluded fabrication of MAS rotors from diamond.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗