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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

An interoperable implementation of collective‐variable based enhanced sampling methods in extended phase space within the OpenMM package

Collective variable (CV)-based enhanced sampling techniques are widely used today for accelerating barrier-crossing events in molecular simulations. A class of these methods, which includes temperature accelerated molecular dynamics (TAMD)/driven-adiabatic free energy dynamics (d-AFED), unified free energy dynamics (UFED), and temperature accelerated sliced sampling (TASS), uses an extended variable formalism to achieve quick exploration of conformational space. These techniques are powerful, as they enhance the sampling of a large number of CVs simultaneously compared to other techniques. Extended variables are kept at a much higher temperature than the physical temperature by ensuring adiabatic separation between the extended and physical subsystems and employing rigorous thermostatting. Here, in this work, we present a computational platform to perform extended phase space enhanced sampling simulations using the open-source molecular dynamics engine OpenMM. The implementation allows users to have interoperability of sampling techniques, as well as employ state-of-the-art thermostats and multiple time-stepping. This work also presents protocols for determining the critical parameters and procedures for reconstructing high-dimensional free energy surfaces. As a demonstration, we present simulation results on the high dimensional conformational landscapes of the alanine tripeptide in vacuo, tetra-N-methylglycine (tetra-sarcosine) peptoid in implicit solvent, and the Trp-cage mini protein in explicit water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

High-throughput mass spectrometry analysis using immediate drop-on-demand technology coupled with an open port sampling interface

Rationale: The sampling throughput of immediate drop-on-demand technology (I.DOT) coupled with an open port sampling interface (OPSI) is limited by software communication. To enable much-needed high-throughput mass spectrometry (MS) analysis capabilities, a novel software was developed that allows for flexible sample selection from a 96-well plate and for maximized analysis throughput using I.DOT/OPSI-MS coupling. Methods: Wells of a 96-well I.DOT plate were filled with propranolol solution and were used to test maximum sampling throughput strategies to minimize analysis time. Demonstration of chemical reaction monitoring was done using acid-catalyzed ring closure of 2,3-diaminonaphthalene (DAN) with nitrite to form 2,3-naphthotriazole (NAT). Analytes were detected in positive electrospray ionization mode using selected reaction monitoring. Results: A maximum throughput of 1.54 s/sample (7.41 min/96-well plate with three technical replicates) was achieved, and it was limited by the peak width of the MS signal resulting in an occasional slight overlap between the peaks. Further, relative standard deviation was 10 ± 1% with all tested sampling strategies. Chemical reaction monitoring of DAN to NAT using nitrite was successfully accomplished with 2 s/sample throughout showing almost complete transformation in 10 min with no signal overlap. Conclusions: This work illustrates the development of a noncontact, automated I.DOT/OPSI-MS system with improved throughput achieved through an optimized software interface. Its achievable analysis time and precision make it a viable approach for drug discovery and in situ reaction monitoring studies.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Importance of Sample Handling and Preparation for Surface Analysis: A Summary of ISO Standard 20579 Part 1, Documenting and Reporting the Handling of Specimens Prior to Analysis

Reliable and reproducible analysis of surfaces is critically dependent on how samples have been collected, handled, and prepared before analysis. Although there are important, well-established general guidelines to minimize unintended alteration of the surfaces to be analyzed, specific handling steps depend on the nature of the samples and information desired. Assessments of the validity and reliability of the results require adequate information about sampling, handling, and processing. ISO standard 20579 part 1 describes the information to be recorded and reported for samples that are prepared or submitted for surface analysis. This paper summarizes elements of the standard, which includes annexes that justify and inform the sample handling processes. Also included is a checklist of information that needs to be recorded and reported and an appendix containing an example of a form that might be used to report the information.

AES↗
DOE OSTI2025

Evaluation of a high-throughput method for processing sponge-stick samples to detect viable, non-spore-forming biothreat agents

After a bioterrorism incident, surface sampling is often used to determine the extent of contamination and exposure, guiding decontamination efforts and decisions for re-occupancy of affected sites. The sponge-stick (SS) is a preferred and commonly used device for sample collection to detect both spore-forming and non-spore-forming biothreat agents from non-porous surfaces. Here, in this study, a recently developed high-throughput method (HTM) for processing SS samples to detect viable Bacillus anthracis spores was adapted for detection of non-spore-forming biothreat agents, Yersinia pestis and Francisella tularensis. The scalable HTM was used to process up to 20 SS samples simultaneously, compared to the current stomacher-based method which processes one SS at a time. Comparisons of the HTM and the stomacher-based method were statistically indistinguishable for most experiments (P > 0.05) with HTM recoveries of 37–60 % for Y. pestis inoculated at 102–103 cells/SS and held 48 h at 4 °C to mimic sample transport/storage. The HTM was integrated with Rapid Viability-Polymerase Chain Reaction (RV-PCR) analysis to detect viable Y. pestis in the presence of particulate contamination (Arizona Test Dust, ATD). This approach detected Y. pestis inoculated at 20 cells/SS and ATD did not impact detection (P > 0.05). F. tularensis showed significantly lower recoveries between no-hold time and 48-h hold time (4 °C, P < 0.05) using the HTM, which further testing showed could be due to toxicity of the neutralizing buffer used for SS pre-wetting. With modifications, this method could enhance throughput capacity while maintaining similar recovery efficiencies to current methods for other non-spore-forming bacterial pathogens.

Biological and medical sciences↗
DOE OSTI2022

Dispersion-enhanced sequential batch sampling for adaptive contour estimation

In computer simulation and optimal design, sequential batch sampling offers an appealing way to iteratively stipulate optimal sampling points based upon existing selections and efficiently construct surrogate modeling. Nonetheless, the issue of near duplicates poses tremendous quandary for sequential learning. It refers to the situation that selected critical points cluster together in each sampling batch, which are individually but not collectively informative towards the optimal design. Near duplicates severely diminish the computational efficiency as they barely contribute extra information towards update of the surrogate. To address this issue, we impose a dispersion criterion on concurrent selection of sampling points, which essentially forces a sparse distribution of critical points in each batch, and demonstrate the effectiveness of this approach in adaptive contour estimation. Specifically, we adopt Gaussian process surrogate to emulate the simulator, acquire variance reduction of the critical region from new sampling points as a dispersion criterion, and combine it with the modified expected improvement (EI) function for critical batch selection. The critical region here is the proximity of the contour of interest. This proposed approach is vindicated in numerical examples of a two-dimensional four-branch function, a four-dimensional function with a disjoint contour of interest and a time-delay dynamic system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A sensitivity analysis to predict the neutronics behavior of samples irradiated in the VTR rabbit system

We report a low-order neutronics model is developed to carry out hundreds of simulations efficiently and investigate the neutronics behavior of samples being irradiated in a test reactor setting under different geometrical constraints. The low-order model allowed for simulations that yield the expected neutronics behavior of any irradiated sample in any environment and allows for the calculation of highly accurate spatially averaged statistics and idealized spatial distributions in the neutron flux. Several benchmarks are performed to evaluate the performance and limitations of the low-order model revealing many important findings. The low-order model predicted the LHGR in the EBR-II driver fuel to within 2.34% by only simulating the fuel rod by itself, which served as a validation for the model. Sensitivity studies investigated 3% enriched UO 2 and U-10Zr being irradiated in the Versatile Test Reactor rabbit system. The analyses investigated a range of combinations of 15 radii and 5 heights for each sample in the rabbit system. Similar data sets are also provided for irradiations in the Advanced Test Reactor’s B-10 irradiation position, which is a thermal neutron spectrum environment. Generalized fits and fit coefficients are obtained for sample heating, reaction rate densities, and local multiplication rate characteristics, allowing the predictions of the neutronics behavior of the samples based on their geometrical constraints. The analyses and fits laid the groundwork for developing a user-end Multiphysics analysis framework to assist and accelerate irradiation experiment design and optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

High-Throughput Microfluidics Platform for Intracellular Delivery and Sampling of Biomolecules from Live Cells

Nondestructive cell membrane permeabilization systems enable the intracellular delivery of exogenous biomolecules for cell engineering tasks as well as the temporal sampling of cytosolic contents from live cells for the analysis of dynamic processes. Here, in this paper, we report a microwell array format live-cell analysis device (LCAD) that can perform localized-electroporation induced membrane permeabilization, for cellular delivery or sampling, and directly interfaces with surface-based biosensors for analyzing the extracted contents. We demonstrate the capabilities of the LCAD via an automated high-throughput workflow for multimodal analysis of live-cell dynamics, consisting of quantitative measurements of enzyme activity using self-assembled monolayers for MALDI mass spectrometry (SAMDI) and deep-learning enhanced imaging and analysis. By combining a fabrication protocol that enables robust assembly and operation of multilayer devices with embedded gold electrodes and an automated imaging workflow, we successfully deliver functional molecules (plasmid and siRNA) into live cells at multiple time-points and track their effect on gene expression and cell morphology temporally. Furthermore, we report sampling performance enhancements, achieving saturation levels of protein tyrosine phosphatase activity measured from as few as 60 cells, and demonstrate control over the amount of sampled contents by optimization of electroporation parameters using a lumped model. Lastly, we investigate the implications of cell morphology on electroporation-induced sampling of fluorescent molecules using a deep-learning enhanced image analysis workflow.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Design and Evaluation of a Tethered, Open Port Sampling Interface for Liquid Extraction-Mass Spectrometry Chemical Analysis

Presented in this work is a tethered, liquid-extraction-sampling interface designed for the mass spectrometric surface sampling/analysis of 3D objects. The tethered, open port sampling interface (TOPSI) incorporates a vacuum line between the sampling probe and ionization source, which enables the ability for an extended, tethered sample transfer line. Herein, several designs of the hand-held TOPSI are presented and evaluated on the basis of the analytical metrics of analyte transport time, peak width, and analyte sensitivity. The best analytical metrics were obtained with capillary flow resistances arranged in a particular order and the vacuum region set at 6.2 kPa. This TOPSI design incorporated a transfer capillary 1 m in length, while retaining a fast analyte transport time (12 s), short signal peak width (5 s baseline-to-baseline), and high analyte signal at 90% of that obtained with a regular open port sampling interface (OPSI). The hand-held TOPSI was demonstrated for the characterization of extracted small molecules and metabolites from the surface of mint and rosemary leaves.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Power Grid Reliability Estimation via Adaptive Importance Sampling

Electricity production currently generates approximately 25% of greenhouse gas emissions in the USA. Thus, increasing the amount of renewable energy is a key step to carbon neutrality. However, integrating a large amount of fluctuating renewable generation is a significant challenge for power grid operating and planning. Grid reliability, i.e., an ability to meet operational constraints under power fluctuations, is probably the most important of them. In this letter, we propose computationally efficient and accurate methods to estimate the probability of line overflow, i.e., reliability constraints violation, under a known distribution of renewable energy generation. To this end, we investigate an importance sampling approach, a flexible extension of Monte-Carlo methods, which adaptively changes the sampling distribution to generate more samples near the reliability boundary. The approach allows to estimate overload probability in real-time based only on a few dozens of random samples, compared to thousands required by the plain Monte-Carlo. Our study focuses on high voltage direct current power transmission grids with linear reliability constraints on power injections and line currents. Herein, we propose a novel theoretically justified physics-informed adaptive importance sampling algorithm and compare its performance to state-of-the-art methods on multiple IEEE power grid test cases.

power system faults↗
DOE OSTI2022

Ensemble approximate control variate estimators: Applications to multi-fidelity importance sampling.

The recent growth in multifidelity uncertainty quantification has given rise to a large set of variance reduction techniques that leverage information from model ensembles to provide variance reduction for estimates of the statistics of a high-fidelity model. In this paper we provide two contributions: (1) we utilize an ensemble estimator to account for uncertainties in the optimal weights of approximate control variate (ACV) approaches and derive lower bounds on the number of samples required to guarantee variance reduction; and (2) we extend an existing multifidelity importance sampling (MFIS) scheme to leverage control variates. Our approach directly addresses a limitation of many multifidelity sampling strategies that require the usage of pilot samples to estimate covariances. As such we make significant progress towards both increasing the practicality of approximate control variates—for instance, by accounting for the effect of pilot samples—and using multifidelity approaches more effectively for estimating low-probability events. The numerical results indicate our hybrid MFIS-ACV estimator achieves up to 50% improvement in variance reduction over the existing state-of-the-art MFIS estimator, which had already shown an outstanding convergence rate compared to the Monte Carlo method, on several problems of computational mechanics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Spatial Study 2021: Sample-Based Surface Water Chemistry and Organic Matter Characterization across Watersheds in the Yakima River Basin, Washington, USA (v3)

This dataset supports a broader study examining the drivers of spatial variability in sediment respiration rates in the Yakima River Basin. The dataset provides geochemistry and organic matter characterization data generated from samples collected during the same two-week period at 47 sites within multiple rivers throughout the Yakima River Basin in Washington, USA. Related sensor data are published at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1892054. This data package was originally published September 2022. It was updated May 2023 (modified files) and November 2024 (new and modified files). See the change history section in the readme for more details. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) dissolved inorganic carbon (DIC), dissolved organic carbon (DOC; reported as non-purgeable organic carbon; NPOC), total nitrogen (TN), total suspended solids (TSS), ions, and benzene polycarboxylic acid (BPCA) concentration and stable isotope data; (5) averaged values from water chemistry data; (6) surface water sampling protocol; (7) sensor protocol (8) readme; (9) methods codes; (10) international generic-sample number (IGSN) mapping file; and (11) folder of high resolution characterization of organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory). This folder contains two subfolders, one containing the .xml data files and the other containing instructions for using Formultitude (https://github.com/PNNL-Comp-Mass-Spec/Formultitude) and an R script to process the data based on the user's specific needs. All files are .csv, .pdf, .R, .ref, or .xml. We thank the United States Forest Service, Washington Department of Natural Resources, Washington Department of Fish and Wildlife, Washington State Parks, Confederated Tribes and Bands of the Yakama Nation, and Cowiche Canyon Conservancy for access to field locations where these samples were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Calendar Year 2021 Underground Test Area Annual Sampling Letter Report Nevada National Security Site, Nevada (Rev. 1) WITH ROTC-1

This calendar year (CY) 2021 annual sampling letter report presents Plan implementation and a summary of field activities, sampling methods, well completion diagrams, and analytical laboratory results from sampling of wells on Pahute Mesa. The CY 2021 letter report also presents data from samples collected in CY 2019. These CY 2019 results were not reported in previous sampling reports because they were not available from the specialized laboratory (Lawrence Livermore National Laboratory [LLNL]) at the time of the previous reports’ publication.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Improvement of Drop‐Hammer Impact Testing for Safety Assessment of High Explosives Using 10‐mg Samples

Here, in this study, we established an improved method for drop-hammer impact testing of small quantities of high explosives (10 mg). We performed about seven hundred impact tests under various experimental conditions (e.g., sandpaper vs bare anvil, different sample masses, drop-weights, and striker diameters) to determine an optimal set of conditions and reaction detection methods (e.g., gas analysis, video, and sound recordings) that give the most statistically reliable results with 10 mg samples. We used both Frequentist and Bayesian statistical approaches to compare estimates of the drop height (DH50) that initiates a reaction 50% of the time, and to quantify the associated uncertainty. Gas analysis proved to be the most reliable reaction detection method, showing unambiguous rises in HE decomposition products (e.g., CO 2 ) even when the other indicators (e.g., sound, video) were inconclusive. The impact tests performed with a bare anvil showed much better reproducibility than those conducted with sandpaper, reducing the largest uncertainty observed in the data sets by a factor of 1.7. The DH 50 values obtained from three different sample masses (10, 20, and 35 mg) fell within the uncertainties of the measurements. We demonstrated the improved procedure (i.e., 10-mg samples, gas analysis, bare anvil, and Bayesian approach) on a variety of PETN samples having different surface areas and thermal histories.

PETN↗
DOE OSTI2024

Robust surfactant-assisted one-pot sample preparation for label-free single-cell and nanoscale proteomics

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk cells. However, such bulk measurement obscures cell to cell heterogeneity, precluding proteome profiling of single cells and small numbers of cells of interest. To address this issue, in recent 5 years there are a surge of small sample preparation methods developed for robust effective collection and processing of single cells and small numbers of cells for in-depth MS-based proteome profiling. Based on their broad accessibility, they can be categorized into two types: specific device- and standard PCR tube- or multi-well plate-based methods. Herein we describe the detailed protocol of our recently developed, easily adoptable, Surfactant-assisted One-Pot (SOP) sample preparation coupled with MS method termed SOP-MS for label-free single-cell and nanoscale proteomics. SOP-MS capitalizes on the combination of a MS-compatible surfactant, DDM (n-Dodecyl-ß-D-maltoside), and standard low-bind PCR tube or multi-well plate for ‘all-in-one’ one-pot sample preparation without sample transfer. With its robust and convenient features, SOP-MS can be readily implemented in any MS laboratory for single-cell and nanoscale proteomics. With further improvements in MS detection sensitivity and sample throughput, we believe that SOP-MS could open an avenue for single-cell proteomics with broad applicability in the biological and biomedical research.

Single-cell proteomics, nanoscale proteomics, SOP-↗
DOE OSTI2025

Evaluation of dried blood spot sampling for verification of exposure to chemical threat agents

Abstract Purpose Exposure to chemical threat agents (CTAs), including nerve agents, the vesicating agent sulfur mustard, and opioids, remains a significant threat to warfighter and civilian populations. Definitive analytical methods to verify exposure to CTAs require shipping refrigerated or frozen biomedical samples to reference laboratories for analysis. Logistical and financial burdens arise as the transport of biomedical samples is subject to strict restrictions and complex packaging, which, if done incorrectly, can lead to sample deterioration. The use of dried blood spot (DBS) sampling could provide operational improvements for collecting, storing, and shipping important forensic samples. Therefore, this effort focuses on developing DBS techniques with Mitra® 30-µL volumetric absorptive microsampling (VAMS®) devices for use in CTA exposure verification. Methods VAMS® devices were loaded and dried with human whole blood that was exposed to the metabolites pinacolyl methylphosphonic acid (PMPA), ethyl methylphosphonic acid (EMPA), 1,1’sulfonylbis[2-(methylsulfinyl)ethane] (SBMSE), norfentanyl, norcarfentanil, norsufentanil, and norlofentanil. Following extraction from the VAMS® devices, metabolites were detected using liquid chromatography-tandem mass spectrometry (LC–MS/MS). The methods were validated for performance by assessing sensitivity, precision, accuracy, and recovery. Results These methods were sensitive to 1 ng/mL for SBMSE, 0.5 ng/mL for PMPA, EMPA, and norfentanyl; 0.1 ng/mL for norlofentanil, and 0.05 ng/mL for norsufentanil and norcarfentanil. All methods met acceptable precision and accuracy criteria with favorable recovery. Conclusions These results demonstrated the utility of VAMS® in stabilizing human whole blood and show promise as an improved collection method for verification of exposure to various CTAs.

Toxicology↗
DOE OSTI2026

Sampling two-dimensional isometric tensor network states

Sampling a quantum system’s underlying probability distributions is an important computational task, e.g., for quantum advantage experiments and quantum Monte Carlo algorithms. Tensor networks are an invaluable tool for efficiently representing states of large quantum systems with limited entanglement. Algorithms for sampling one-dimensional (1D) tensor networks are well-established and utilized in several 1D tensor network methods. In this paper we introduce two novel sampling algorithms for two-dimensional (2D) isometric tensor network states (isoTNS) that generalize existing 1D tensor network sampling algorithms. Our first proposed algorithm performs independent sampling and yields a single configuration together with its associated probability. The second algorithm employs a greedy search strategy to identify high-probability configurations and their corresponding probabilities. Numerical results demonstrate the effectiveness of these algorithms across quantum states with varying entanglement and system size.

Dumitrescu, Eugene [ORNL] (ORCID:0000000158519567)↗
DOE OSTI2023

Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform

Conventional proteomics measures the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of significant changes in subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot precisely infer the abundance changes of intact proteins, which are presented as proteoforms. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantification directly on thin tissue sections. The spatial TDP platform consisted of a nanoPOTS (nanodroplet Processing in One pot for Trace Samples)-based sample preparation system and an LCM (laser capture microdissection)-based cell isolation system. We improved the nanoPOTS sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ~200 cultured cells as model samples, the improved approach increased proteoform identifications from 493 to 700; newly identified proteoforms primarily corresponded to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed LCM-isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 426 proteoforms by combining identifications from TopPIC and TDPortal with the quantitation from ProMex. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential PTM-specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A machine-learning approach to measure 3D sample properties from 2D Transmission Electron Microscopy images

Transmission Electron Microscopy (TEM) is a powerful tool for the characterization of materials at the nanoscale; however, its inherent two-dimensional (2D) nature poses significant challenges to accurately measure three-dimensional (3D) properties. We introduce a supervised machine-learning model that predicts 3D structural information, such as sample thickness and curvature, from a series of conventional 2D TEM images. The model, a U-Net convolutional neural network, is trained on a large synthetic dataset generated from dynamical diffraction simulations that model TEM’s complex, nonlinear image formation, accounting for sample thickness and curvature. This physically realistic framework enables exploration of a broad parameter space impractical to sample experimentally. We demonstrate that the trained model has accurate predictions for experimental single-crystal silicon samples, achieving performance comparable to established measurement techniques. This work highlights the critical role of robust, simulation-based training in overcoming the limitations of real-world imaging artifacts and inconsistent sample geometries. By integrating machine learning with numerical simulations, we offer an efficient and scalable framework for quantitative TEM analysis, paving the way for more sophisticated 3D characterization of complex materials.

Dynamical diffraction↗