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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

ROCker: accurate detection and quantification of target genes in short-read metagenomic data sets by modeling sliding-window bitscores

Functional annotation of metagenomic and metatranscriptomic data sets relies on similarity searches based on e-value thresholds resulting in an unknown number of false positive and negative matches. To overcome these limitations, we introduce ROCker, aimed at identifying position-specific, most-discriminant thresholds in sliding windows along the sequence of a target protein, accounting for non-discriminative domains shared by unrelated proteins. ROCker employs the receiver operating characteristic (ROC) curve to minimize false discovery rate (FDR) and calculate the best thresholds based on how simulated shotgun metagenomic reads of known composition map onto well-curated reference protein sequences and thus, differs from HMM profiles and related methods. We showcase ROCker using ammonia monooxygenase (amoA) and nitrous oxide reductase (nosZ) genes, mediating oxidation of ammonia and the reduction of the potent greenhouse gas, N 2 O, to inert N 2 , respectively. ROCker typically showed 60-fold lower FDR when compared to the common practice of using fixed e-values. Previously uncounted ‘atypical’ nosZ genes were found to be two times more abundant, on average, than their typical counterparts in most soil metagenomes and the abundance of bacterial amoA was quantified against the highly-related particulate methane monooxygenase (pmoA). Therefore, ROCker can reliably detect and quantify target genes in short-read metagenomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Monolithic Photoelectrochemical CO 2 Reduction Producing Syngas at 10% Efficiency

We report increasing anthropogenic carbon dioxide emissions have prompted the search for photoelectrochemical (PEC) methods of converting CO 2 to useful commodity products, including fuels. Ideally, such PEC approaches will be sustained using only sunlight, water, and CO 2 as energetic and reactant inputs. However, low peak conversion efficiencies (< 5%) have made commercialization of fully-integrated PEC devices prohibitive. Here, a 4 cm 2 monolithic PEC device exceeding 10% solar-to-fuel efficiency with principal fuel products of carbon monoxide and hydrogen is reported. The corresponding solar-to-CO and solar-to-H 2 efficiencies are 7% and 3.5%, respectively. Screening of a range of operating conditions reveals a tunable product mixture of H 2 and CO using a gold electrocatalyst. Accordingly, it is shown that device optimization yields a H 2 -to-CO ratio of 1:2 commonly present in synthesis gas (syngas). Notably, the modularity and facile fabrication of this device permit the incorporation of a broad array of materials for various applications. For example, the electrocatalyst may easily be swapped to target a different set of products.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Genetics of rectal cancer and novel therapies: primer for radiologists

Rectal cancer accounts for one-third of newly diagnosed colorectal cancer cases. Given its anatomical location and risk for local recurrence, a multidisciplinary treatment program including surgery, radiation therapy, and chemotherapy has demonstrated improved outcomes in localized disease. Genetic analysis has become part of the standard approach for management of advanced disease and new trials are considering tailored therapies for locally advanced disease. This review describes molecular subsets of colorectal cancer; implications for clinical management, including patterns of metastatic spread and response to therapies; and emerging matched therapies. During the last decade, significant biological differences have been noted based on colorectal cancer primary location and here we focus on rectal cancers and relevant markers for this disease. As more treatment for localized rectal cancer is shifted to the neoadjuvant setting and more targeted regimens are developed for metastatic disease, radiologists will increasingly see patients defined by molecular subsets and their awareness of the genetics of rectal cancer will help further refine our understanding of this disease.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

BTG/Tob family members Tob1 and Tob2 inhibit proliferation of mouse embryonic stem cells via Id3 mRNA degradation

The mammalian BTG/Tob family is a group of proteins with anti-proliferative ability, and there are six members including BTG1, BTG2/PC3/Tis21, BTG3/ANA, BTG4/PC3B, Tob1/Tob and Tob2. Among them, Tob subfamily members, specifically Tob1/Tob and Tob2, have the most extensive C-terminal regions. As previously reported, overexpression of BTG/Tob proteins is associated with the inhibition of G1 to S-phase cell cycle progression and decreased cell proliferation in a variety of cell types. Tob subfamily proteins have similar anti-proliferative effects on cell cycle progression in cultured tumor cells. An important unresolved question is whether or not they have function in rapidly proliferating cells, such as embryonic stem cells (ESCs). Tob1 and Tob2 were expressed ubiquitously in mouse ESCs (mESCs), suggesting a possible role in early embryonic development and mESCs. To address the above question and explore the possible functions of the Tob subfamily in ESCs, we established ESCs from different genotypic knockout inner cell mass (ICM). We found that Tob1{sup −/−}, Tob2{sup −/−}, and Tob1/2 double knockout (DKO, Tob1{sup −/−} & Tob2{sup −/−}) ESCs grew faster than wild type (WT) ESCs without losing pluripotency, and we provide a possible mechanistic explanation for these observations: Tob1 and Tob2 inhibit the cell cycle via degradation of Id3 mRNA, which is a set of directly targeted genes of BMP4 signaling in mESCs that play critical roles in the maintenance of ESC properties. Together, our data suggest that BTG/Tob family protein Tob1 and Tob2 regulation cell proliferation does not compromise the basic properties of mESCs. - Highlights: • We established mouse Tob1/2 double knockout embryonic stem cells. • Tob1 and Tob2 inhibit the proliferation of ESCs without effect on pluripotency. • Tob1 and Tob2 involved in the degradation of Id3 in mESCs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset

Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Infrastructure and resource development needs to support a significant expansion of US nuclear capacity

In December 2023, the United States pledged at a United Nations conference to triple nuclear energy by 2050. In May of 2025 a new administration announced plans to quadruple nuclear energy by 2025, setting a new target that requires understanding the key infrastructure and resources needed to achieve large-scale deployment. This study attempts to quantify “what it takes” to deploy an additional 200 GWe of nuclear capacity. It projects demand levels for workforce, fuel, uranium mining and enrichment, waste management, supply chain, land, licensing, and water. Demand growth is also contextualized. For example, the peak number of construction workers required is approximately 215,000, a 37 % increase in the current US utility construction workforce. Up to 80,000 MT/year of mined uranium will be needed, necessitating a 60-fold increase in US mining production or a doubling of global mining if sourced externally. Advanced reactor fuels, uranium mining, and nuclear-grade equipment production would need substantial expansion. Operational workforce, enrichment needs, and licensing submissions also face significant increases. Although water consumption may see relatively lower increases, securing water rights poses unique challenges. This study consolidates various demand metrics in a broad context, highlighting the necessity of early preparations, such as workforce training, to support a tripling of nuclear energy. While it outlines the demand-side requirements, it does not assess the difficulties of ramping up or other supply-side issues. The findings also shed light on the impact of reactor size and reactor technology on various demand metrics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

A shock tube study of ignition delay times in diluted methane, ethylene, propene and their blends at elevated pressures

Ignition delay times (IDT) of small hydrocarbons at elevated pressures provide a valuable constraint for the refinement of the core small-hydrocarbon sub-mechanisms used in all combustion kinetics. Current knowledge of these core mechanisms is based largely on low-pressure data, with only limited high-pressure data available. To remedy this, here the present study focuses on ignition delay times in methane, ethylene, propene and their blends at elevated pressures. IDT measurements were performed in 4% O 2 , balance Ar mixtures, over the temperature range of 950–1800 K, at pressures of 14–60 atm and equivalence ratios of 1 and 2. IDT was determined from recorded sidewall pressure, OH* emission measurements and fuel time-histories measured using laser absorption at 3.39 μm. These measurements extend the test conditions of earlier studies, with the advantage that they have all been performed at similar conditions and with the same facility and should provide a uniform set of kinetics targets for the evaluation of core small-hydrocarbon mechanisms. This dataset also allowed the temperature variation of the pressure and equivalence ratio scaling for methane and ethylene IDT to be investigated.

42 ENGINEERING↗
DOE OSTI2021

Data-driven chemical kinetic reaction mechanism for F-24 jet fuel ignition

A data-driven chemical kinetic mechanism for the military version of Jet A, F-24, is developed for numerical simulations of the ignition process. The main purpose of this study is to obtain a practical F-24 mechanism across wide temperature and equivalence ratio ranges, with a particular focus on the negative temperature coefficient and low temperature regions. The new mechanism (ARLMech-HC-F24) is based on the HyChem model of a similar fuel and optimized using a micro-genetic algorithm against an experimental ignition delay data set of the target fuel. The development and optimization processes include reaction selection, population creation, shuffled tournament implementation based on a merit function, and child-individual creation for the next generation. Several techniques and parameters are proposed to generate an accurate mechanism through an efficient process. The newly introduced data-driven mechanism based on these techniques shows better merit value convergence and represents the ignition behavior more accurately than that without the techniques. This practical mechanism is suitable for the numerical simulations of the F-24 or Jet A ignition problem, and the suggested strategies can be employed in similar problems of rate coefficients estimation.

42 ENGINEERING↗
DOE OSTI2018

Proteogenomic Analysis of Surgically Resected Lung Adenocarcinoma

Despite apparently complete surgical resection, approximately half of resected early-stage lung cancer patients relapse and die of their disease. Adjuvant chemotherapy reduces this risk by only 5% to 8%. Thus, there is a need for better identifying who benefits from adjuvant therapy, the drivers of relapse, and novel targets in this setting. RNA sequencing and liquid chromatography/liquid chromatography–mass spectrometry proteomics data were generated from 51 surgically resected non–small cell lung tumors with known recurrence status. We present a rationale and framework for the incorporation of high-content RNA and protein measurements into integrative biomarkers and show the potential of this approach for predicting risk of recurrence in a group of lung adenocarcinomas. In addition, we characterize the relationship between mRNA and protein measurements in lung adenocarcinoma and show that it is outcome specific. Our results suggest that mRNA and protein data possess independent biological and clinical importance, which can be leveraged to create higher-powered expression biomarkers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Listening to laser sparks: a link between Laser-Induced Breakdown Spectroscopy, acoustic measurements and crater morphology

In preparation for the SuperCam/Mars Microphone scientific investigation, the acoustic signal associated with the plasma formation during Laser-Induced Breakdown Spectroscopy (LIBS) experiment is studied with regard to the shot-to-shot evolution of the laser induced crater morphology and plasma emission lines. A set of geological targets are depth profiled using a specifically designed LIBS setup coupled with acoustic test bench under ambient terrestrial atmosphere. Experiments confirm that the decrease of the acoustic energy as a function of the number of shots is well correlated with the target hardness/density and also demonstrate that the acoustic energy can be used as a remote tracer of the ablated volume of the target. Overall, listening to LIBS sparks provides a new information relative to the ablation process that is independent from the LIBS spectrum.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Improving logistic efficiency of WEEE collection through dynamic scheduling using simulation modeling

Highlights: • Latest EU regulation on WEEE require more flexible logistics models. • The implementation of dynamic scheduling is evaluated for WEEE collection. • A hybrid simulation model compares dynamic (pure and “mixed”) and static collection schemes. • Results confirm the higher flexibility and efficiency of dynamic systems. • Dynamic scheduling “mixed” solutions result more efficient than pure ones. - Abstract: The complexity of collection systems for Waste from Electric and Electronic Equipment (WEEE) in the EU is increasing, due to the latest directive that sets new collection targets and modes. The high variability and the uncertainty of reverse flows require innovative logistic approaches. One recent option for increasing efficiency and responsiveness in waste collection services, boosted by new technological solutions for waste level monitoring, is to adopt a dynamic collection scheme, where the collection frequency is not established a priori (based on a fixed plan), but it is based on the actual filling levels of waste bins. This option can allow the service provider to plan the collection service following the actual demand, resulting in a more responsive service, while improving the logistic efficiency. This paper evaluates the implementation of dynamic scheduling schemes for the collection of WEEE. A hybrid simulation model has been developed in order to support researchers and practitioners in assessing quantitative impacts of adopting dynamic scheduling in WEEE collection. Three logistic alternatives (a fixed collection schedule scheme, a pure dynamic scheme and a mixed one) have been compared in a test case based on data of an Italian municipality; collection services for different types of WEEE (i.e. large appliances and small items) have been analyzed. Results show a promising performance of dynamic schedules compared to the fixed one, revealing, for the specific test case, how a mixed solution can combine the advantages of dynamic and fixed scheduling, gaining flexibility towards customer demand while improving truck resource utilization.

42 ENGINEERING↗
DOE OSTI2023

Neural Network Water Model Based on the MB-Pol Many-Body Potential

The MB-pol many-body potential accurately predicts many properties of water, including cluster, liquid phase, and vapor–liquid equilibrium properties, but its high computational cost can make applying it in large-scale simulations quite challenging. Here, in order to address this limitation, we developed a “deep potential” neural network (DPMD) model based on the MB-pol potential for water. We find that a DPMD model trained on mostly liquid configurations yields a good description of the bulk liquid phase but severely underpredicts vapor–liquid coexistence densities. By contrast, adding cluster configurations to the neural network training set leads to a good agreement for the vapor coexistence densities. Liquid phase densities under supercooled conditions are also represented well, even though they were not included in the training set. These results confirm that neural network models can combine accuracy and transferability if sufficient attention is given to the construction of a representative training set for the target system.

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

Accelerating Structure–Property Relationship Discovery with Multimodal Machine Learning and Self-Driving Microscopy

Microscopy combined with local spectroscopy is widely used to correlate nanoscale structure with functional properties in materials, but conventional measurements rely heavily on human-selected sampling locations and predefined targets, limiting data set diversity and the potential for discovery. Here, we present a framework that integrates autonomous microscopy with dual-novelty deep kernel learning (DN-DKL) for adaptive data acquisition and a dual variational autoencoder (VAE) for representation learning. DN-DKL actively guides the microscopy toward structurally and spectroscopically novel regions, enabling efficient collection of large spectral data sets. Dual-VAE embeds local structures and spectroscopic responses into a shared latent manifold that serves as a structure–property relationship map. We applied this framework for the investigation of halide perovskite films by using conductive atomic force microscopy. The results reveal distinct hysteresis behaviors that are linked to specific nanoscale structural motifs, including grain boundary junction points that show hysteresis under different bias conditions and asymmetric grain boundaries that suppress the charge transport. This framework establishes a general strategy that leverages the complementary strengths of self-driving microscopy, machine learning, and human expertise to accelerate scientific discovery in functional materials.

atomic force microscopy↗
DOE OSTI2026

Hierarchical Testing of a Hybrid Machine Learning‐Physics Global Atmosphere Model

Machine learning (ML)-based models have demonstrated high skill and computational efficiency, often outperforming conventional physics-based models in weather and subseasonal predictions. While prior studies have assessed their fidelity in capturing synoptic-scale atmospheric dynamics, their performance across timescales and under out-of-distribution forcing, such as +3K or +4K uniform-warming forcings, and the sources of biases remain elusive, to establish the model's reliability for Earth science. Here, we design three sets of experiments targeting synoptic-scale phenomena, interannual variability, and out-of-distribution uniform-warming forcings. We evaluate the Neural General Circulation Model (NeuralGCM), a hybrid model integrating a dynamical core with ML-based component, against observations and physics-based Earth system models (ESMs). At the synoptic scale, NeuralGCM captures the evolution and propagation of extratropical cyclones with performance comparable to ESMs. At the interannual scale, when forced by El Niño-Southern Oscillation sea surface temperature (SST) anomalies, NeuralGCM successfully reproduces associated teleconnection patterns but exhibits deficiencies in capturing nonlinear response. Under out-of-distribution uniform-warming forcings, NeuralGCM simulates similar responses in global-average temperature and precipitation and reproduces large-scale tropospheric circulation features similar to those in ESMs. Notable weaknesses include overestimating the tracks and spatial extent of extratropical cyclones, biases in the teleconnected wave train triggered by tropical SST anomalies, and differences in upper-level warming and stratospheric circulation responses to SST warming compared to physics-based ESMs. The causes of these weaknesses were explored. Despite the noted weaknesses, NeuralGCM reproduces responses across experiments reasonably and performs comparably to ESMs. By integrating a dynamical core with ML, NeuralGCM shows potential for developing ML-based ESMs.

global warming↗
DOE OSTI2023

Radiolytically reworked Archean organic matter in a habitable deep ancient high-temperature brine

Abstract Investigations of abiotic and biotic contributions to dissolved organic carbon (DOC) are required to constrain microbial habitability in continental subsurface fluids. Here we investigate a large (101–283 mg C/L) DOC pool in an ancient (>1Ga), high temperature (45–55 °C), low biomass (10 2 −10 4 cells/mL), and deep (3.2 km) brine from an uranium-enriched South African gold mine. Excitation-emission matrices (EEMs), negative electrospray ionization (–ESI) 21 tesla Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS), and amino acid analyses suggest the brine DOC is primarily radiolytically oxidized kerogen-rich shales or reefs, methane and ethane, with trace amounts of C 3 –C 6 hydrocarbons and organic sulfides. δ 2 H and δ 13 C of C 1 –C 3 hydrocarbons are consistent with abiotic origins. These findings suggest water-rock processes control redox and C cycling, helping support a meagre, slow biosphere over geologic time. A radiolytic-driven, habitable brine may signal similar settings are good targets in the search for life beyond Earth.

Science & Technology - Other Topics↗
DOE OSTI2018

Interaction of consumer preferences and climate policies in the global transition to low-carbon vehicles

Burgeoning demands for mobility and private vehicle ownership undermine global efforts to reduce energy-related greenhouse gas emissions. Advanced vehicles powered by low-carbon sources of electricity or hydrogen offer an alternative to conventional fossil-fuelled technologies. Yet, despite ambitious pledges and investments by governments and automakers, it is by no means clear that these vehicles will ultimately reach mass-market consumers. In this work, we develop state-of-the-art representations of consumer preferences in multiple global energy-economy models, specifically focusing on the non-financial preferences of individuals. We employ these enhanced model formulations to analyse the potential for a low-carbon vehicle revolution up to 2050. Our analysis shows that a diverse set of measures targeting vehicle buyers is necessary to drive widespread adoption of clean technologies. Carbon pricing alone is insufficient to bring low-carbon vehicles to the mass market, though it may have a supporting role in ensuring a decarbonized energy supply.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

An autonomous laboratory for the accelerated synthesis of novel materials

To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

36 MATERIALS SCIENCE↗