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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 163 records · Page 9

Rapid Targeted Quantitation of Protein Overexpression with Direct Infusion Shotgun Proteome Analysis (DISPA-PRM)

While much effort has been placed on comprehensive quantitative proteome analysis, certain applications demand measurement of only a few target proteins from complex systems. Traditional approaches to targeted proteomics rely on nano-liquid chromatography (nLC) and targeted mass spectrometry (MS) methods, e.g., parallel reaction monitoring (PRM). However, the time requirement for nLC can limit throughput of targeted proteomics. To achieve rapid and high-throughput targeted methods, here we show that nLC separations can be eliminated and replaced with direct infusion shotgun proteome analysis (DISPA) using high-field asymmetric waveform ion mobility spectrometry (FAIMS) with PRM. We demonstrate application of DISPA-PRM for rapid targeted quantification of bacterial enzymes utilized in the production of biofuels by monitoring temporal expression in 72 metabolically engineered bacterial cultures in under 2.5 hours, with a measured dynamic range >1200-fold. Here, we conclude that DISPA-PRM presents a valuable innovative tool with results comparable to nLC-MS/MS, enabling fast and rapid detection of targeted proteins in complex mixtures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling

Label-free quantitative proteomics has become an increasingly popular tool for profiling global protein abundances. However, one major weakness is the potential performance drift of the LC-MS platform over time, which in turn limits its utility for analyzing large-scale sample sets. To address this, in this work we introduce an experimental and data analysis scheme based on a block-design with common controls within each block for enabling LC-MS-based large-scale label-free quantification. In this scheme, a large number of samples (e.g., >100 samples) are analyzed in smaller, and more manageable blocks, minimizing instrument drift and variability within a block. Furthermore, each designated block also contains common controls (or reference) samples for normalization within and across blocks. We demonstrated the effectiveness of this method by profiling the proteome response of human macrophage THP-1 cells to 11 engineered nanomaterials (ENMs) at two different doses. A total of 116 samples were analyzed in six blocks, yielding an average coverage of 4500 proteins per sample. The data revealed consistent quantification of proteins across all six blocks, as shown by highly stable quantification of house-keeping proteins in all samples and high levels of quantification correlation among samples from different blocks. The data also demonstrated that label-free quantification is robust and accurate enough to quantify even very subtle abundance changes as well as large fold-changes without potential ratio compression as often encountered with isobaric labeling. Our streamlined workflow is easy to implement and can be readily adapted to other large cohort studies for reproducible label-free proteome quantification.

59 BASIC BIOLOGICAL SCIENCES↗

Adapting the Electron Beam from SEM as a Quantitative Heating Source for Nanoscale Thermal Metrology

The electron beam (e-beam) in the scanning electron microscopy (SEM) provides an appealing mobile heating source for thermal metrology with spatial resolution of ~1 nm, but the lack of systematic quantification of the e-beam heating power limits such application development. Here, we systemically study e-beam heating in LPCVD silicon nitride (SiN x ) thin-films with thickness ranging from 200 to 500 nm from both experiments and complementary Monte Carlo simulations using the CASINO software package. There is good agreement about the thickness-dependent e-beam energy absorption of thin-film between modeling predictions and experiments. Using the absorption results, we then demonstrate adapting the e-beam as a quantitative heating source by measuring the thickness-dependent thermal conductivity of SiN x thin-films, with the results validated to within 7% by a separate Joule heating experiment. The results described here will open a new avenue for using SEM e-beams as a mobile heating source for advanced nanoscale thermal metrology development.

42 ENGINEERING↗

An improved method for quantitatively measuring antifouling coating performance using a mussel single thread tensile adhesion test

Surface biofouling reduces the efficiency and lifespan of equipment across many industries. The development of high-performance antifouling surfaces, such as foul release coatings, benefits from test methods that can quickly identify superior antifouling surfaces in the laboratory during material development. Existing test methods poorly discriminate between different foul release coatings. Here is presented a method to assess the ability of surfaces to resist mussel adhesion using a quantitative, controlled single thread adhesion test (STAT) method, allowing for meaningful comparisons between low adhesion foul release surfaces. This method provides greater accuracy and finer resolution than push-based mussel shear adhesion methods without the difficulties associated with mussel size, thread attachment angle, or harming the mussels. The single thread tensile method is demonstrated on a variety of standard and high-performance coatings, and it is shown that the method detects differentiation between commercial foul release coatings that could not be resolved using other methods.

42 ENGINEERING↗

From quantitative trait loci towards mechanisms: Linkage Integration Hypothesis Testing (LIgHT) sheds light on the mechanisms of genetically modulated stress tolerance

The goal of this work is to assess the mechanistic bases of natural genetic variations in plant responses of photosynthesis to stress. To achieve this goal, we devised the Linkage Integration Hypothesis Testing (LIgHT) approach, comparing chromosomal locations of quantitative trait loci (QTLs) for multiple phenotypes to distinguish between hypothetical mechanisms. As a use case, we explored genetic variations in photosynthesis-related processes under chilling stress in recombinant inbred lines of cowpea ( Vigna unguiculata L. Walp.). We focused on photosynthesis-related parameters measurable in high throughput and indicative of proposed chilling responses, including the states of PSI and PSII, photoprotective non-photochemical quenching, PSII photodamage, and nyctinastic leaf movements (NLMs). The patterns of QTL linkages indicated that chilling stress tolerance is genetically controlled by avoiding PSII photodamage rather than PSI damage or NLMs. This model was validated in a separate experiment measuring the rates of PSII photodamage and repair. Additional linkages suggest that chilling-induced damage to PSII is controlled by the thylakoid proton motive force and redox state of PSII. This regulation appears to be modulated by thylakoid fatty acid composition, previously associated with the same genetic loci and now supported by broader mechanistic evidence. We propose that the LIgHT approach can be broadly applied to test mechanisms underlying genetic variations.

MultispeQ↗

Quantitative phase measurements of human cell nuclei using X-ray ptychography

The human cell nucleus serves as an important organelle holding the genetic blueprint for life. In this work, X-ray ptychography was applied to assess the masses of human cell nuclei using its unique phase shift information. Measurements were carried out at the I13-1 beamline at the Diamond Light Source that has extremely large transverse coherence properties. Furthermore, the ptychographic diffractive imaging approach allowed imaging of large structures that gave quantitative measurements of the phase shift in 2D projections. In this paper a modified ptychography algorithm that improves the quality of the reconstruction for weak scattering samples is presented. The application of this approach to calculate the mass of several human nuclei is also demonstrated.

59 BASIC BIOLOGICAL SCIENCES↗

Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design

Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. Here, the present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity (dOS)), and using ANNs trained with QSPR descriptors to directly predict OS. Twenty-five ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. Twenty-five additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.

09 BIOMASS FUELS↗

Application of quantitative risk assessment to address stakeholder questions in geologic carbon storage

Ambitious international greenhouse gas emissions reduction targets demand a rapid transformation to a low-carbon economy. This transformation includes the accelerated adoption of carbon dioxide (CO2) capture and storage (CCS) technology. However, as with any large-scale engineering enterprise, the widespread commercial-scale deployment of geologic carbon storage (GCS) raises important questions about technology and cost-effectiveness, safety, environmental risk, and long-term liability. Effectively assessing and managing risks and liability associated with GCS projects is a key technical need throughout the project life cycle-from site selection and permitting to monitoring design, operational risk management, and post-operational site closure. This presentation highlights recent advancements in tools for quantitative risk assessment, being developed by the National Risk Assessment Partnership (NRAP). NRAP is a multi-year, multinational laboratory research collaboration sponsored by the U.S. Department of Energy's Office of Fossil Energy and Carbon Management. Our focus will be on these tools' applications in addressing critical stakeholder questions related to supporting permitting to ensure secure and environmentally protective storage; designing effective and efficient monitoring plans; evaluating the effectiveness of remedial actions and risk management alternatives; and informing liability assessment and investment decisions. This paper will detail the key functionality of NRAP’s Open-Source Integrated Assessment Model (NRAP-Open-IAM), a computational framework for assessing leakage risk and containment assurance. This model features streamlined workflows for calculating leakage risk profiles, delineating risk-based area of review, and assessing contingency plans and post-injection site care requirements. ORION is an open-source, observation-based ensemble forecasting toolkit to help operators assess the seismic hazard at a carbon storage site. The State of Stress Analysis Tool (SOSAT), designed to assess subsurface stress conditions and evaluate geomechanical risk resulting from CO2 injection in an area of interest will also be presented. We will also introduce a prototype model to evaluate storage project costs and liability associated with risk management. The Technoeconomic and Liability Evaluation for Storage (TALES) model uses results from forecasts of leakage and induced seismicity risk to estimate the lifecycle cost of managing risk. Finally, a preliminary example of how the NRAP Risk-based Adaptive Monitoring Plan (RAMP) tool can be used to design efficient and effective site monitoring plans and estimate the detectability of fluid leakage will be provided. The relevance of these tools for addressing key stakeholder questions amidst uncertainty will be emphasized.

decision support↗

SPRUCE Quantitative PCR (qPCR) of Microbial Gene Copy Numbers, 2021-2022

This dataset provides the results for quantitative polymerase chain reaction (qPCR) of peat samples collected from ambient and experimental plots in the Spruce and Peatland Responses Under Climatic and Environmental Change (SPRUCE) experiment site in June and August of 2021, and June of 2022. SPRUCE is located within the Marcell Experimental Forest in northern Minnesota, USA. The dataset includes bacterial, archaeal, fungal gene copy numbers, along with corresponding logarithmic values, at 11 depth increments of two-meter deep peat cores taken from 12 sampling sites locations inside SPRUCE plots (10 chambered and 2 ambient plots). The sampling, sample prep and analysis followed standard methods outlined in prior publications (Wilson et al. 2016; Kluber et al. 2020) except that a higher yielding Omega Bio-Tek Mag-Bind Environmental DNA 96 Kit was used for extractions and DNA was quantified using Qubit dsDNA High Sensitivity Assay Kit. qPCR subsamples of peat cores from the SPRUCE plots characterize changes in the abundance and composition of microbial communities of peat seasonally showing how composition varies under multiple levels of experimental peat warming and atmospheric CO2 concentrations. This dataset contains one data file in comma-separated values (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma-separated values (.csv) format and a user guide in PDF (*.pdf) format. On 2026-07-07 this dataset was updated to add three columns to the data file: ‘Fungal_copy_dry’, ‘Log_fungal_copy_dry’, ‘Fungal_copy_wet’. No previously released data values were altered. Additionally, the abstract, data dictionary, and user guide were updated, and a file-level metadata file was added.

Archaea↗

Genome_shuffling_enables_quantitative_trait_locus_mapping_in_Bacillus_subtilis

Genetic mapping is a powerful tool for eukaryotic genetics that has only been applied to bacteria in limited circumstances. Quantitative trait locus (QTL) mapping generally relies on sexual recombination to break linkages between genes, yet bacteria rarely undergo sufficient homologous recombination to generate suitable mapping populations. In this work, we used iterative biparental genome shuffling by protoplast fusion inBacillus subtilisto generate a population of bacteria with substantial random recombination throughout their genomes. Individual shuffled progeny were arrayed in well plates, resequenced, and characterized for a range of complex phenotypes including spore germination and swarming motility. Genetic mapping of the resulting phenotypes identified high-confidence QTLs of moderate size (∼10 kb), and these associations were validated through targeted genetic swaps. ThisB. subtilisQTL population can easily be used to map additional phenotypes, and the general approach for QTL mapping is applicable in a wide range of bacteria.

Bacillus subtilis↗

Fabrication of Black Body Grids by Thick Film Printing for Quantitative Neutron Imaging

Neutron imaging offers deep penetration through many high-Z materials while also having high sensitivity to certain low-Z isotopes such as 1H, 6Li, and 10B. This unique combination of properties has made neutron imaging an attractive tool for a wide range of material science and engineering applications. However, measurements made by neutron imaging or tomography are generally qualitative in nature due to the inability of detectors to discriminate between neutrons which have been transmitted through the sample and neutrons which are scattered by the sample or within the detector. Recent works have demonstrated that deploying a grid of small black bodies (BBs) in front of the sample can allow for the scattered neutrons to be measured at the BB locations and subsequently subtracted from the total measured intensity to yield a quantitative transmission measurement. While this method can be very effective, factors such as the scale and composition of the sample, the beam divergence, and the resolution and construction of the detector may require optimization of the grid design to remove all measurement biases within a given experimental setup. Therefore, it is desirable to have a method by which BB grids may be rapidly and inexpensively produced such that they can easily be tailored to specific applications. In this work, we present a method for fabricating BB patterns by thick film printing of Gd2O3 and evaluate the performance with variation in feature size and number of print layers with cold and thermal neutrons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Quantitative Decoupling of Oxygen-Redox and Manganese-Redox Voltage Hysteresis in a Cation-Disordered Rock Salt Cathode

Pronounced voltage hysteresis in Li-excess cathode materials is commonly thought to be associated with oxygen redox. However, these materials often possess overlapping oxygen and transition-metal redox, whose contributions to hysteresis between charge and discharge are challenging to distinguish. In this work, a two-step aqueous redox titration is developed with the aid of mass spectrometry (MS) to quantify oxidized lattice oxygen and Mn 3+/4+ redox in a representative Li-excess cation-disordered rock salt—Li 1.2 Mn 0.4 Ti 0.4 O 2 (LMTO). Two MS-countable gas molecules evolve from two separate titrant-analyte reactions, thereby allowing Mn and O redox capacities to be decoupled. The decoupled O and Mn redox coulombic efficiencies are close to 100% for the LMTO cathode, indicating high charge-compensation reversibility. As incremental Mn and O redox capacities are quantitatively decoupled, each redox voltage hysteresis is further evaluated. Overall, LMTO voltage hysteresis arises not only from an intrinsic charge-discharge voltage mismatch related to O redox, but also from asymmetric Mn-redox overvoltages. The results reveal that O and Mn redox both contribute substantially to voltage hysteresis. In conclusion, this work further shows the potential of designing new analytical workflows to experimentally quantify key properties, even in a disordered material having complex local coordination environments.

25 ENERGY STORAGE↗

Quantitative Analysis of The High‐Yield Hydrolysis of Kelp by Laminarinase and Alginate Lyase

Abstract Kelp is an abundant, farmable biomass‐containing laminarin and alginate as major polysaccharides, providing an excellent model substrate to study their deconstruction by simple enzyme mixtures. Our previous study showed strong reactivity of the glycoside hydrolase family 55 during hydrolysis of purified laminarin, raising the question of its reactivity with intact kelp. In this study, we determined that a combination of a single glycoside hydrolase family 55 β‐1,3‐exoglucanase with a broad‐specificity alginate lyase from the polysaccharide lyase family 18 gives efficient hydrolysis of untreated kelp to a mixture of simple sugars, that is, glucose, gentiobiose, mannitol‐end glucose, and mannuronic and guluronic acids and their soluble oligomers. Quantitative assignments from nanostructure initiator mass spectrometry (NIMS) and 2D HSQC NMR spectroscopy and analysis of the reaction time‐course are provided. The data suggest that binary combinations of enzymes targeted to the unique polysaccharide composition of marine biomass are sufficient to deconstruct kelp into soluble sugars for microbial fermentation.

2D HSQC NMR↗

Identification of quantitative trait loci for sorghum leaf blight resistance

Sorghum leaf blight and northern corn leaf blight, both caused by Exserohilum turcicum {(Pass.) K. J. Leonard and Suggs [syn. Setosphaeria turcica (Luttr.) K. J. Leonard and Suggs.]}, are major diseases of sorghum [Sorghum bicolor (L.) Moench] and maize (Zea mays L.), respectively. Examining the genetic architecture of resistance in sorghum will lead to a better understanding of the relationship between resistance in sorghum and maize, which can ultimately enhance management options in both crops. In 2018 and 2019, we evaluated two sorghum recombinant inbred line (RIL) populations for resistance to E. turcicum. The BTx623 × IS3620C and BTx623 × SC155 populations consisted of 235 and 81 RILs, respectively. Resistance in both populations was moderately to highly heritable. We identified a total of six quantitative trait loci (QTL) across the two populations. Three QTL with small- to moderate-effect sizes were identified in the BTx623 × IS3620C population. Three QTL, including a large-effect QTL on chromosome three that explained 24% of the variation, were identified in the BTx623 × SC155 population. We compared the identified QTL with the position of northern corn leaf blight candidate genes and found eight candidate resistance gene orthologs that colocalize with the sorghum leaf blight QTL. There were also several nucleotide-binding leucine-rich repeat encoding genes within the candidate intervals. Understanding host resistance in multiple species furthers our understanding of the Exserohilum turcicum patho-system.

59 BASIC BIOLOGICAL SCIENCES↗

Quantitative trait locus (QTL) mapping and transcriptome profiling identify QTLs and candidate genes associated with heat stress response during reproductive development in Camelina sativa

Camelina sativa (L.) Crantz is a low-input oilseed crop that has great potential in providing sustainable feedstock for biofuels and bioproducts. Climate change is threatening production of camelina with rising global temperatures. Elucidating the genetic response to high temperatures is essential for successful breeding of heat-tolerant camelina varieties. Here, we report a combinatorial approach to identifying candidate genes associated with heat stress by quantitative trait locus (QTL) mapping and comparative transcriptome profiling. A population of recombinant inbred lines (RILs) was grown in a controlled growth chamber under the high-temperature regimes for 14 days beginning at the onset of the reproductive stage. Several traits related to seed production were evaluated at maturity. The QTL analysis identified several regions with co-located traits on chromosomes 8, 10, and 12. Two RILs with contrasting phenotypic responses to heat stress were chosen for gene expression profiling via RNA sequencing. Multiple pathways and genes were found to be strongly affected by heat stress, and many genes expressed differently between the two RILs. Several genes identified within the QTL regions were considered strong candidates that may control heat tolerance during reproduction in camelina. These studies provide resources for future studies that may assist in improving the heat tolerance of camelina.

60 APPLIED LIFE SCIENCES↗

RuKY Catalyst‐Packed Permeation Membrane for Quantitative Ammonia and d3‐Ammonia Dehydrogenation to Ultrapure Hydrogen

Ammonia is a promising carbon-free hydrogen carrier, but incomplete ammonia dehydrogenation (cracking) generates atmospheric emissions of NO x , a potent greenhouse gas. Additionally, incomplete cracking of ammonia leads to regulatory challenges in nuclear and fusion power, where tritiated ammonia (NT 3 ) emissions are strictly controlled. Therefore, we report the use of low-temperature ammonia dehydrogenation catalysts (3%Ru/1%Y/12%K/Al 2 O 3 ) in a palladium alloy H 2 permeation membrane for quantitative conversion of ammonia into hydrogen and nitrogen at industry-relevant conditions. This catalytic membrane reactor system achieved an astonishing effluent concentration of <1 ppm at 450°C under a 100% NH 3 stream, which is far beyond the 99.6% conversion target required for the adoption of ammonia as a vehicle fuel. The low-temperature ammonia dehydrogenation catalyst was tested in a packed bed reactor with NH 3 and ND 3 to both elucidate the reaction mechanism and to quantify the kinetic isotope effect of the membrane reactor. The rate-limiting step at temperatures relevant to the palladium membrane are isotope independent, indicating that the isotopologue content will not modify the desired reaction kinetics. By reducing emissions to below-trace levels with no additional separation, this work provides a path to greatly simplified and miniaturized ammonia cracking processes.

ammonia decomposition↗

What is (quantitative) system dynamics modeling? Defining characteristics and the opportunities they create

A clear definition of system dynamics modeling can provide shared understanding and clarify the impact of the field. We introduce a set of characteristics that define quantitative system dynamics, selected to capture core philosophy, describe theoretical and practical principles, and apply to historical work but be flexible enough to remain relevant as the field progresses. The defining characteristics are: (1) models are based on causal feedback structure, (2) accumulations and delays are foundational, (3) models are equation-based, (4) concept of time is continuous, and (5) analysis focuses on feedback dynamics. We discuss the implications of these principles and use them to identify research opportunities in which the system dynamics field can advance. These research opportunities include causality, disaggregation, data science and AI, and contributing to scientific advancement. Progress in these areas has the potential to improve both the science and practice of system dynamics.

97 MATHEMATICS AND COMPUTING↗

Identification and mapping of quantitative trait loci for Fusarium head blight resistance in a synthetic hexaploid × hard red spring wheat population

Abstract Fusarium head blight (FHB), caused byFusarium graminearumSchwabe, is one of the most devastating diseases in wheat (Triticum aestivumL.). The synthetic hexaploid wheat line Largo was developed from a cross between the durum wheat [T. turgidumssp.durum(Desf.) Husn.] variety Langdon and theAegilops tauschiiCosson accession PI 268210, and it was previously found to have a moderate level of FHB resistance. This study was conducted to identify quantitative trait loci (QTL) associated with FHB resistance using a population of 188 recombinant inbred lines (RILs) from a cross between Largo and the susceptible wheat line ND495. The RILs were evaluated for Type II resistance in two greenhouse and two field environments. The disease severity and 90K single‐nucleotide polymorphism marker data were used for QTL analysis, which revealed six QTL on chromosomes 1D, 2D, 5B, and 7D. Four QTL (QFhb.rwg‐1D,QFhb.rwg‐5B,QFhb.rwg‐7D.1, andQFhb.rwg‐7D.3) from Largo had minor effects, whereas two QTL (QFhb.rwg‐2DandQFhb.rwg‐7D.2) from ND495 showed large effects on FHB resistance. The result suggested that ND495 may possess suppressor or susceptibility gene(s) suppressing or masking FHB resistance controlled by the resistance QTL. Among these QTL, four coincided with previously reported QTL, includingFhb9, and two (QFhb.rwg‐1DandQFhb.rwg‐7D.1) are likely novel QTL. From the six QTL regions, 10 Kompetitive allele‐specific PCR markers were developed and validated for marker‐assisted selection. The QTL detected from the resistant and susceptible parents enhance our understanding of FHB resistance expression and provide new resources for improving FHB resistance in wheat.

Genetics & Heredity↗