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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Conservative fourth-order finite-volume Vlasov–Poisson solver for axisymmetric plasmas in cylindrical (r,v r ,v θ ) phase space coordinates

In this paper, a fourth-order finite-volume Vlasov–Poisson algorithm is developed for simulating axisymmetric plasma configurations in ( r , v r , v θ ) phase space coordinates. The Vlasov equation for cylindrical phase space coordinates is cast into conservation-law form and is discretized on a structured grid. The conservative finite-volume discretization is based on fifth-order upwind reconstructions of the distribution function and a fourth-order quadrature rule that accounts for transverse variations of fluxes along control volume surfaces. High-order specular reflection boundary conditions enable high-fidelity treatment of plasma distribution functions at the axis and at wall boundaries. The numerical method is applied to simulate a confined uniform neutral gas to assess convergence properties for an equilibrium system in which effects of finite-temperature and acceleration due to centrifugal and Coriolis forces are present. The discretization is also applied to a Z-pinch configuration to study electrostatic ion confinement and the dynamics of the associated breathing mode. Simulations show that the ion distribution function exhibits non-Maxwellian features and that a temperature anisotropy develops and is sustained. Finally, the finite-volume implementation is demonstrated to converge at fourth-order for both applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2014

Martian Oceans: Old Debate - New Insights

The possibility of an ancient ocean in the northern lowlands of Mars has been discussed for decades [1-14], but the subject remains controversial [15-20]. Among the many unique features of the northern lowlands is the extensive development of "giant polygons" - polygonal landforms that range from 1 to 20 km across. The kilometer-scale size of these features distinguishes them from a variety of smaller polygons (usually < 250 m) on Mars that have been compared to terrestrial analogs such as ice-wedge and desiccation features. However, until recently, geologists were aware of no examples of polygons on Earth comparable in scale to the giant polygons of Mars, so there were no good analogs from which to draw interpretations. That picture has changed with 3D seismic data acquired by the petroleum industry in exploration of offshore basins. The new data reveal kilometer-scale polygonal features in more than 50 offshore basins on Earth]. These features provide a credible analog for the giant polygons of Mars.

Oehler, Dorothy Z.↗
DOE OSTI2023

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. Furthermore, we observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2010

Combustion and Performance Analyses of Coaxial Element Injectors with Liquid Oxygen/Liquid Methane Propellants

Liquid rocket engines using oxygen and methane propellants are being considered by the National Aeronautics and Space Administration (NASA) for in-space vehicles. This propellant combination has not been previously used in a flight-qualified engine system, so limited test data and analysis results are available at this stage of early development. NASA has funded several hardware-oriented activities with oxygen and methane propellants over the past several years with the Propulsion and Cryogenic Advanced Development (PCAD) project, under the Exploration Technology Development Program. As part of this effort, the NASA Marshall Space Flight Center has conducted combustion, performance, and combustion stability analyses of several of the configurations. This paper summarizes the analyses of combustion and performance as a follow-up to a paper published in the 2008 JANNAF/LPS meeting. Combustion stability analyses are presented in a separate paper. The current paper includes test and analysis results of coaxial element injectors using liquid oxygen and liquid methane or gaseous methane propellants. Several thrust chamber configurations have been modeled, including thrust chambers with multi-element swirl coax element injectors tested at the NASA MSFC, and a uni-element chamber with shear and swirl coax injectors tested at The Pennsylvania State University. Configurations were modeled with two one-dimensional liquid rocket combustion analysis codes, the Rocket Combustor Interaction Design and Analysis (ROCCID), and the Coaxial Injector Combustion Model (CICM). Significant effort was applied to show how these codes can be used to model combustion and performance with oxygen/methane propellants a priori, and what anchoring or calibrating features need to be applied or developed in the future. This paper describes the test hardware configurations, presents the results of all the analyses, and compares the results from the two analytical methods

Hulka, J. R.↗
DOE OSTI2011

Crystal Structure of the Dimeric Oct6 (Pou3fl) POU Domain Bound to Palindromic MORE DNA

POU domains (named after their identification in Pit1, Oct1 unc86) are found in around 15 transcription factors encoded in mammalian genomes many of which feature prominently as key regulators at development bifurcations. For example, the POU III class Octamer binding protein 6 (Oct6) is expressed in embryonic stem cells and during neural development and drives the differentia5tion of myelinated cells in the central and peripheral nervous system. Defects in oct6 expression levels are linked to neurological disorders such as schizophrenia. POU proteins contain a bi-partite DNA binding domain that assembles on various DNA motifs with differentially configured subdomains. Intriguingly, alternative configurations of POU domains on different DNA sites were shown to affect the subsequent recruitment of transcriptional coactivators. Namely, binding of Oct1 to a Palindromic Oct-factor Recognition Element (PORE) was shown to facilitate the recruitment of the OBF1 coactivator whereas More of PORE (MORE) bound Oct1 does not. Moreover, Pit1 was shown to recruit the corepressor N-CoR only when bound to a variant MORE motif with a 2 bp half-site spacing. Therefore, POU proteins are seen as a paradigm for DNA induced allosteric effects on transcription factors modulating their regulatory potential. However, a big unresolved conundrum for the POU class and for most if not all other transcription factor classes is how highly similar proteins regulate different sets of genes causing fundamentally different biological responses. Ultimately, there must be subtle features enabling those factors to engage in contrasting molecular interactions in the cell. Thus, the dissection of the molecular details of the transcription-DNA recognition in general, and the formation of multimeric regulatory complexes, in particular, is highly desirable. To contribute to these efforts they solved the 2.05 {angstrom} crystal structure of Oct6 bound as a symmetrical homodimer to palindromic MORE DNA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Detailed Hydrographic Feature Extraction from High-Resolution LiDAR Data

Detailed hydrographic feature extraction from high-resolution light detection and ranging (LiDAR) data is investigated. Methods for quantitatively evaluating and comparing such extractions are presented, including the use of sinuosity and longitudinal root-mean-square-error (LRMSE). These metrics are then used to quantitatively compare stream networks in two studies. The first study examines the effect of raster cell size on watershed boundaries and stream networks delineated from LiDAR-derived digital elevation models (DEMs). The study confirmed that, with the greatly increased resolution of LiDAR data, smaller cell sizes generally yielded better stream network delineations, based on sinuosity and LRMSE. The second study demonstrates a new method of delineating a stream directly from LiDAR point clouds, without the intermediate step of deriving a DEM. Direct use of LiDAR point clouds could improve efficiency and accuracy of hydrographic feature extractions. The direct delineation method developed herein and termed “mDn”, is an extension of the D8 method that has been used for several decades with gridded raster data. The method divides the region around a starting point into sectors, using the LiDAR data points within each sector to determine an average slope, and selecting the sector with the greatest downward slope to determine the direction of flow. An mDn delineation was compared with a traditional grid-based delineation, using TauDEM, and other readily available, common stream data sets. Although, the TauDEM delineation yielded a sinuosity that more closely matches the reference, the mDn delineation yielded a sinuosity that was higher than either the TauDEM method or the existing published stream delineations. Furthermore, stream delineation using the mDn method yielded the smallest LRMSE.

58 GEOSCIENCES↗
NASA NTRS1985

Mechanical features of the shuttle rotating service structure

With the development of the space shuttle launching facilities, it became mandatory to develop a shuttle rotating service structure to provide for the insertion and/or removal of payloads at the launch pads. The rotating service structure is a welded tubular steel space frame 189 feet high, 65 feet wide, and weighing 2100 tons. At the pivot column the structure is supported on a 30 inch diameter hemispherical bearing. At the opposite terminus the structure is supported on two truck assemblies each having eight 36 inch diameter double flanged wheels. The following features of the rotating service structure are discussed: (1) thermal expansion and contraction; (2) hurricane tie downs; (3) payload changeout room; (4) payload ground handling mechanism; (5) payload and orbiter access platforms; and (6) orbiter cargo bay access.

Crump, J. M.↗
DOE OSTI2024

Numerical modeling of a proof-of-principle experiment on optical stochastic cooling at an electron storage ring

Cooling of beams circulating in storage rings is critical for many applications including particle colliders and synchrotron light sources. A method enabling unprecedented beam-cooling rates, optical stochastic cooling (OSC), was recently demonstrated in the Integrable Optics Test Accelerator (IOTA) electron storage ring at Fermilab [J. Jarvis , ]. This paper describes the numerical implementation of the OSC process in the particle-tracking program and discusses the validation of the developed model with available experimental data. The model is also employed to highlight some features associated with different modes of operation of OSC. The developed simulation tool should be valuable in guiding future configurations of optical stochastic cooling and, more broadly, modeling self-field-based beam manipulations. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2015

Rigorous force field optimization principles based on statistical distance minimization

We use the concept of statistical distance to define a measure of distinguishability between a pair of statistical mechanical systems, i.e., a model and its target, and show that its minimization leads to general convergence of the model’s static measurable properties to those of the target. We exploit this feature to define a rigorous basis for the development of accurate and robust effective molecular force fields that are inherently compatible with coarse-grained experimental data. The new model optimization principles and their efficient implementation are illustrated through selected examples, whose outcome demonstrates the higher robustness and predictive accuracy of the approach compared to other currently used methods, such as force matching and relative entropy minimization. We also discuss relations between the newly developed principles and established thermodynamic concepts, which include the Gibbs-Bogoliubov inequality and the thermodynamic length.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Putting Regional Climate Prediction in Reach

Developing the capability to project how climate change will affect a particular region--any region--on Earth is a high-performance computing problem with enormous economic and health impacts. Techniques to resolve regional-scale features in global climate models are being developed and validated on leadership computing systems. In time, these regional-scale-resolving global climate models will arm local and state decision makers with the information needed to prepare for both short- and long-term impacts of climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

MindSynchro

This report presents the developments and results of MindSynchro project as part of DOE OE FOA 1861. DOE and Pacific Northwest National Laboratory (PNNL) have made available to FOA awardees datasets containing years of real historical data recorded from various phasor measurement units (PMUs) which are installed in three large US interconnections: Texas (IC A), Western (IC B), and Eastern (IC C). The main goal of the project, which was successfully achieved, was to develop methods for detection and identification of events which are relevant for power grid operation. Tasks performed for achieving the project goals included data exploration and pre-processing, the development and application of physics-based features, data analysis and labeling based on unsupervised learning approaches, training and testing of DSSL models for classification of events which are relevant for power grid operation, and deployment of solutions to cloud environments. The methods developed in the project can potentially provide relevant benefits to power grid asset owners/operators in general in terms of situational awareness. Two main types of outcomes can be provided by these tools: Identification of specific relevant power grid event types: Semi-supervised ML methods developed in the project can adequately employ not only the relatively scarce labeled data but also the large amount of available unlabeled data to train models for detection of specific event types. Such methods enable the application of trained models for the detection of events in a population of PMUs much larger than that associated to the labeled events. Support in data labeling / label validation: Labels are critical for training of models for identification of specific types of events. However, labeling large amounts of data is a manual and tedious process. This means that such process is error prone and is not scalable. Methods developed in the project, based on ensembles of clustering models, have been successfully employed for turning manual labeling into a scalable process. Accurate identification of specific relevant events can provide the operators with immediate situational awareness that could otherwise require hours or days of analysis from domain experts. We envision that such methods could be initially employed in support of post-mortem analysis of events and, as confidence is gained, they could be employed for online/real-time support, providing, among other benefits, insights for avoiding major events which could happen due to a combination of smaller ones. On the longer term, related methods could potentially be employed to improve protection and control.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Datum: A Scientific Metadata Catalog

The data catalog market is currently flooded with a myriad of different products, but none serve the scientific community well. There are cloud-native tools like Databricks, Snowflake,to on-premise solutions like Collibra and Datahub. The common failing of all these tools however, is their inability to serve the scientific data community directly. Most catalogs are targeted towards financial, health, or user data - not sensor or scientific domain data. They also prioritize integrations that often don’t exist or are just starting to be used in the scientific realm - all while ignoring common scientific tools and file types. Datum is a catalog which targets the scientific data directly, including the tools and networks in which those tools are used. We work with the producers and consumers of the data where they are, targeting cloud and on-premise with a focus on classified networks. Datum is an Erlang/Elixir application. Technical Features Note: The features listed below are still under development and may change, slightly, upon final delivery of the product. File Formats - Datum has the ability to read additional metadata and provides processing pipelines for the following file formats: Plain Text, PDF, LaTeX, HTML, Open Document Format (.odt), XML, CSV/TSV (and other standard delimiters), OpenDocument Database and Spreadsheets, Geo-Referenced TIFF, Common Data Format, HDF/HDF5, LabView TDMS, Excel, DeltaTables, Parquet, Apache Iceberg, Apache Hudi and many others. Metadata Collection - Scanners for the local and networked file systems and cloud storage providers. Network integration with common databases such as MSSQL and MySQL. User Plugin System - Users are able to provide either file processing, metadata extraction, or sampling plugins in the programming language of their choice. Authentication/Authorization -: OIDC integration, SCIM provisioning and EntraID integration out of the box. Full user and group management system with a “least privilege” operating mode. Governance - Customizable data governance platform; dictate and enforce required metadata, enforce data embargos, and enforce user agreements and NDAs before data access. Ability to create health checks on data, rejecting abandoned or poorly curated data and automatically removing it from the search index. Ability for users to submit corrections. Search - Semantic search is a first class citizen. No licenses to expensive, external software required. Integrated use of vectors and vector-based search allows for AI agent integration at all levels of operation. Metadata Model - Display and control data’s lineage and connections to other data and data directories. Data is modeled after a filesystem - an organization instantly recognizable and navigable by most any user. CLI and SDK - Ships with a Command Line Interface (CLI) tool and with a fully-featured Python SDK. This allows for rapid and programmatic use of Datum by every level of user. Minimal Infrastructure - Datum ships as a single executable file and can be run on any operating system and most CPU architectures. Datum has no reliance on external databases, search indexing tools, or other outside services - and it runs equally well on edge computing devices, cloud services, or in a clustered HPC environment.

darrington, john↗
DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Deconstructing the root system of grasses through an exploration of development, anatomy and function

Abstract Well‐adapted root systems allow plants to grow under resource‐limiting environmental conditions and are important determinants of yield in agricultural systems. Important staple crops such as rice and maize belong to the family of grasses, which develop a complex root system that consists of an embryonic root system that emerges from the seed, and a postembryonic nodal root system that emerges from basal regions of the shoot after germination. While early seedling establishment is dependent on the embryonic root system, the nodal root system, and its associated branches, gains in importance as the plant matures and will ultimately constitute the bulk of below‐ground growth. In this review, we aim to give an overview of the different root types that develop in cereal grass root systems, explore the different physiological roles they play by defining their anatomical features, and outline the genetic networks that control their development. Through this deconstructed view of grass root system function, we provide a parts‐list of elements that function together in an integrated root system to promote survival and crop productivity.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1997

Intelligent System Development Using a Rough Sets Methodology

The purpose of this research was to examine the potential of the rough sets technique for developing intelligent models of complex systems from limited information. Rough sets a simple but promising technology to extract easily understood rules from data. The rough set methodology has been shown to perform well when used with a large set of exemplars, but its performance with sparse data sets is less certain. The difficulty is that rules will be developed based on just a few examples, each of which might have a large amount of noise associated with them. The question then becomes, what is the probability of a useful rule being developed from such limited information? One nice feature of rough sets is that in unusual situations, the technique can give an answer of 'I don't know'. That is, if a case arises that is different from the cases the rough set rules were developed on, the methodology can recognize this and alert human operators of it. It can also be trained to do this when the desired action is unknown because conflicting examples apply to the same set of inputs. This summer's project was to look at combining rough set theory with statistical theory to develop confidence limits in rules developed by rough sets. Often it is important not to make a certain type of mistake (e.g., false positives or false negatives), so the rules must be biased toward preventing a catastrophic error, rather than giving the most likely course of action. A method to determine the best course of action in the light of such constraints was examined. The resulting technique was tested with files containing electrical power line 'signatures' from the space shuttle and with decompression sickness data.

Anderson, Gray T.↗
DOE OSTI2016

Reliability Assessment of Safety-Critical Network Communication in a Digitalized Nuclear Power Plant

In analog instrumentation and control (I and C) systems for nuclear power plants (NPPs), most connections are comprised of hardwired point-to-point types. Since they are based on analog technologies, they are fragile to noise and require analog-to-digital conversion time. Therefore, many NPPs have adopted communication network for the exchange of safety-critical data between programmable logic controllers (PLCs) in digital I and C systems to effectively accommodate a huge number of field controllers. However, the primary issue of digital network communication in a safety-critical system involves potential hazards, such as partial or total loss of communication between PLCs, which may result in a failure of safety critical signal generation when it is needed. Therefore, the probability that a safety-critical system in digitalized NPP becomes unsafe due to a network failure must be evaluated to quantify the risk of digital I and C system. In Korea, the Engineered Safety Feature-Component Control System (ESF-CCS) was developed as one of the digital safety-critical system in Advanced Power Reactor- 1400 (APR-1400). The function of a digital engineered safety feature (ESF) actuation system and field actuator controllers of conventional NPPs are performed by the ESF-CCS. In this system, safety-critical network, High Reliable- Safety Data Network (HR-SDN), was employed for the safety-critical data transmission from group controllers (GCs) to loop controllers (LCs). In this study, a framework for assessing the reliability of a HR-SDN network communication between GCs and LCs in the ESF-CCS in addition to a fault-tree model for ESF components considering network failure between GCs and LCs were developed to assess the plant risk effect of a newly developed digitalized ESF-CCS. Based on the case studies and cut set analysis, the risk effect of network failure on plant risk and the important risk contributors of network failure were quantitatively addressed. A framework for identifying the potential hazardous states of network communication in the ESFCCS was proposed. In addition, a fault-tree model for network communication failure was developed to estimate the risk effects of network failure between the GCs and LCs on ESF-CCS signal failure. The developed fault-tree model was then applied to several case studies. The fault-tree model of ESF-CCS signal failure for ESF components required for various mitigation actions regarding NPP DBAs was designed by considering the identified hazardous states of network failure that would result in a failure to provide input signals to the corresponding LC. As a sensitivity study, three case studies were performed based on the periodic test methods for hardware components in HR-SDN module and their test intervals in this study. The quantitative results for three case studies demonstrated that the probability of overall network communication failure, which was calculated as the sum of the failure probability associated with each failure cause, contributed up to 1.58% on the Small-LOCA CD frequency. The results also revealed the important basic events related to network failure in the case studies which included CCF of hardware and software in HR-SDN module. This study is expected to provide insight into the development of the fault-tree model for the network failure in digital I and C system and into the quantification of the risk effect of network failure for safety-critical data transmission in NPPs. As a further study, the risk effects of network failure in the ESF-CCS on the plant risk can be estimated in a more detailed manner by analyzing the failure modes and causes in a sub-component level and the fault detection coverage of the fault tolerance techniques for network failure implemented in ESF-CCS after the detailed configuration of ESF-CCS is decided. In terms of the failure of manual ESF actuation, the relationship among the human action failures must be investigated to consider multiple human error condition since ESF-CCS provides multiple manual actuation to assure the diversity of the system. Since the conventional HEP method cannot accommodate the multiple conditions in a fault tree, condition-based human reliability assessment (CBHRA) method or the dynamic human reliability modeling approach can be applied to include a complex relationship among the automated safety signal generation and the human operator's manual actuation, avoiding the optimistic estimation of HEP of the human action failure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRSDate not supplied

Development of the Microcalorimeter Detector for Athena/X-IFU

The X-ray Integral Field Unit (X-IFU) will be a microcalorimeter instrument on ESA’s Athena X-ray observatory, set to launch around 2037. Athena recently underwent a reformulation exercise resulting in a simplified X-IFU architecture, with a new baseline configuration featuring fewer pixels and readout channels. Newly developed transition edge sensor pixels that are slower and have reduced magnetic field sensitivity have helped enable the new instrument design whilst maintaining flagship quality science. In this paper, we report on the status and plan for continued development of the X-IFU microcalorimeter array. We describe details of the current pixel architecture and performance specifications. We will present the design of the first full scale microcalorimeter arrays that have the necessary flight like electrical and mechanical interfaces for X-IFU. These arrays will undergo testing in a focal-plane assembly (FPA) Development Model (DM) in Europe later in 2024 and serve as a precursor to the X-IFU Engineering Model detector.

Stephen J Smith↗
DOE OSTI2016

Quantitative metrics for assessment of chemical image quality and spatial resolution

Rationale: Currently objective/quantitative descriptions of the quality and spatial resolution of mass spectrometry derived chemical images are not standardized. Development of these standardized metrics is required to objectively describe chemical imaging capabilities of existing and/or new mass spectrometry imaging technologies. Such metrics would allow unbiased judgment of intra-laboratory advancement and/or inter-laboratory comparison for these technologies if used together with standardized surfaces. Methods: We developed two image metrics, viz., chemical image contrast (ChemIC) based on signal-to-noise related statistical measures on chemical image pixels and corrected resolving power factor (cRPF) constructed from statistical analysis of mass-to-charge chronograms across features of interest in an image. These metrics, quantifying chemical image quality and spatial resolution, respectively, were used to evaluate chemical images of a model photoresist patterned surface collected using a laser ablation/liquid vortex capture mass spectrometry imaging system under different instrument operational parameters. Results: The calculated ChemIC and cRPF metrics determined in an unbiased fashion the relative ranking of chemical image quality obtained with the laser ablation/liquid vortex capture mass spectrometry imaging system. These rankings were used to show that both chemical image contrast and spatial resolution deteriorated with increasing surface scan speed, increased lane spacing and decreasing size of surface features. Conclusions: ChemIC and cRPF, respectively, were developed and successfully applied for the objective description of chemical image quality and spatial resolution of chemical images collected from model surfaces using a laser ablation/liquid vortex capture mass spectrometry imaging system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗