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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Fully Integrated Ultra-thin Intraoperative Micro-imager for Cancer Detection Using Upconverting Nanoparticles

Abstract Purpose Intraoperative detection and removal of microscopic residual disease (MRD) remain critical to the outcome of cancer surgeries. Today’s minimally invasive surgical procedures require miniaturization and surgical integration of highly sensitive imagers to seamlessly integrate into the modern clinical workflow. However, current intraoperative imagers remain cumbersome and still heavily dependent on large lenses and rigid filters, precluding further miniaturization and integration into surgical tools. Procedures We have successfully engineered a chip-scale intraoperative micro-imager array—without optical filters or lenses—integrated with lanthanide-based alloyed upconverting nanoparticles (aUCNPs) to achieve tissue imaging using a single micro-chip. This imaging platform is able to leverage the unique optical properties of aUCNPs (long luminescent lifetime, high-efficiency upconversion, no photobleaching) by utilizing a time-resolved imaging method to acquire images using a 36-by-80-pixel, 2.3 mm $$\times$$ × 4.8 mm silicon-based electronic imager micro-chip, that is, less than 100-µm thin. Each pixel incorporates a novel architecture enabling automated background measurement and cancellation. We have validated the performance, spatial resolution, and the background cancellation scheme of the imaging platform, using resolution test targets and mouse prostate tumor sample intratumorally injected with aUCNPs. To demonstrate the ability to image MRD, or tumor margins, we evaluated the imaging platform in visualizing a single-cell thin section of the injected prostate tumor sample. Results Tested on USAF resolution targets, the imager is able to achieve a resolution of 71 µm. We have also demonstrated successful background cancellation, achieving a signal-to-background ratio of 8 when performing ex vivo imaging on aUCNP-injected prostate tumor sample, improved from originally 0.4. The performance of the imaging platform on single-cell layer sections was also evaluated and the sensor achieved a signal-to-background ratio of 4.3 in resolving cell clusters with sizes as low as 200 cells. Conclusion The imaging system proposed here is a scalable chip-scale ultra-thin alternative for bulky conventional intraoperative imagers. Its novel pixel architecture and background correction scheme enable visualization of microscopic-scale residual disease while remaining completely free of lenses and filters, achieving an ultra-miniaturized form factor—critical for intraoperative settings.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

In-Situ SEM High Strain Rate Testing of Large Diameter Micropillars Followed by TEM and EBSD Postmortem Analysis

Background Dislocation dynamic simulations are intended as a tool to understand and predict the mechanical behavior of metallic materials, but its prediction has never been directly verified by experiments due to differences in specimen strain rate and size. Objective In this work, a comprehensive experimental framework is proposed to attempt direct comparison between experiments and discrete dislocation dynamics (DDD) modelling. Methods By integrating high-throughput sample fabrication and a customized testing apparatus, the sample size and strain rate typically employed in DDD simulations are explored experimentally. Constitutive properties such as stress-strain response are measured, and microstructural information is obtained from transmission electron microscopy (TEM) imaging, electron backscatter diffraction (EBSD), and TEM-based orientation mapping. Results Magnesium and copper were selected, as case studies, to demonstrate the newly developed experimental procedure. Measured stress-strain responses for Mg are consistent with those obtained with a miniaturized Hopkison bar experiments. Furthermore, by exploiting the validated workflow, the effect of strain rate on micropillar heterogeneous deformation and associated dislocation plasticity were revealed. Conclusion The work establishes a methodology for the systematic study of not only metals but also other materials and structures at the microscale and high strain rates.

42 ENGINEERING↗
DOE OSTI2020

Mochi: Composing Data Services for High-Performance Computing Environments

Technology enhancements and the growing breadth of application workflows running on high-performance computing (HPC) platforms drive the development of new data services that provide high performance on these new platforms, provide capable and productive interfaces and abstractions for a variety of applications, and are readily adapted when new technologies are deployed. The Mochi framework enables composition of specialized distributed data services from a collection of connectable modules and subservices. Rather than forcing all applications to use a one-size-fits-all data staging and I/O software configuration, Mochi allows each application to use a data service specialized to its needs and access patterns. This paper introduces the Mochi framework and methodology. The Mochi core components and microservices are described. Examples of the application of the Mochi methodology to the development of four specialized services are detailed. Finally, a performance evaluation of a Mochi core component, a Mochi microservice, and a composed service providing an object model is performed. The paper concludes by positioning Mochi relative to related work in the HPC space and indicating directions for future work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

High-Energy X-ray Tomographic Analysis of Precursor Metal Powders (Ti-6Al-4V) Used for Additive Manufacturing

We utilized high-energy x-ray tomography to characterize Ti-6Al-4V metal powders (both as-received and recycled) used in powder-bed additive manufacturing process. The image processing workflow was developed to process and analyze large amount of data objectively by computer program. The distribution of size and shape of the metal particles as well as defect (mainly porosity) inside the particles was analyzed with the statistical representation and resolution in micrometer. The result revealed that circular-shaped porosity with various sizes could be embedded in the powder particles. These porosities could potentially be transferred to the 3D printed part and critically affect the mechanical performance of the component. The present study shows the effectiveness of characterizing metal powders using x-ray imaging techniques where sufficient number of particles can be sampled within tens of minutes with a minimum sample preparation and high accuracy. Clear structural differences in the as-received and recycled powders were delineated that helps in determining the feasibility of using the recycled powders.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions

Here, we present a data-centric deep learning (DL) approach using neural networks (NNs) to predict the thermodynamics of ternary solid solutions. We explore how NNs can be trained with a dataset of Gibbs free energies computed from a CALPHAD database to predict ternary systems as a function of composition and temperature. We have chosen the energetics of the FCC solid solution phase in 226 binaries consisting of 23 elements at 11 different temperatures to demonstrate the feasibility. The number of binary data points included in the present study is 102,000. We select six ternaries to augment the binary dataset to investigate their influence on the NN prediction accuracy. We examine the sensitivity of data sampling on the prediction accuracy of NNs over selected ternary systems. It is anticipated that the current DL workflow can be further elevated by integrating advanced descriptors beyond the elemental composition and more curated training datasets to improve prediction accuracy and applicability.

42 ENGINEERING↗
DOE OSTI2021

Machine-Learning-based Algorithms for Automated Image Segmentation Techniques of Transmission X-ray Microscopy (TXM)

Four state-of-the-art Deep Learning-based Convolutional Neural Networks (DCNN) were applied to automate the semantic segmentation of a 3D Transmission x-ray Microscopy (TXM) nanotomography image data. The standard U-Net architecture as baseline along with UNet++, PSPNet, and DeepLab v3+ networks were trained to segment the microstructural features of an AA7075 micropillar. A workflow was established to evaluate and compare the DCNN prediction dataset with the manually segmented features using the Intersection of Union (IoU) scores, time of training, confusion matrix, and visual assessment. Comparing all model segmentation accuracy metrics, it was found that using pre-trained models as a backbone along with appropriate training encoder-decoder architecture of the Unet++ can robustly handle large volumes of x-ray radiographic images in a reasonable amount of time. This opens a new window for handling accurate and efficient image segmentation of in situ time-dependent 4D x-ray microscopy experimental datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Adoption of image-driven machine learning for microstructure characterization and materials design: A Perspective

Microstructure characterization enables the development of structure-processing-property relationships critical to several research areas within the broad field of materials science, from alloy design to the assessment of corrosion resistance, and failure analysis. Conventional approaches to material characterization have relied on either qualitative inference by the human ex-pert or software applications that can extract high-level features from images, such as boundary segmentation, average grain diameter, etc. Such approaches rely heavily on subject matter expert user intervention and knowledge of what phases or more generally, what microstructural features, are of interest. The recent surge in the adoption of machine learning techniques to address problems in materials engineering has brought with it an increased interest and application of Image Driven Machine Learning (IDML) approaches. In this work, we review the applications of IDML to the field of materials characterization. A canonical hierarchy of stages is defined, which when put sequentially together completes an IDML study: problem definition, dataset building, model selection and training, model evaluation, and integration with existing instrumentation or simulation workflow. The studies reviewed in this work are analyzed from the perspective of each of these stages. Such a review permits agranular assessment of the field, for example the impact of IDML on materials characterization at the nanoscale, the size of a typical dataset required to train a semantic segmentation model on electron microscopy images, ubiquitousness of transfer learning in the domain, etc. Finally, we discuss the importance of interpretability and explainability in the field of IDML for materials characterization, and provide an overview of two emerging techniques in the field: semantic segmentation and generative adversarial networks.

Baskaran, Arun↗
DOE OSTI2024

Standardization Gaps in Powder Feedstock Characterization and Establishing Acceptability for Reuse in Additive Manufacturing

Characterization techniques for powder feedstocks used in additive manufacturing (AM) have long been relied upon to describe the inputs to an AM workflow. However, functional gaps remain between tests to measure intrinsic and extrinsic properties with the direct performance within AM equipment. Furthermore, the common practice of reusing powder through multiple build cycles introduces effects and changes to feedstock performance that are otherwise difficult to measure quantitatively. Here, standardization and the development of new test methods have not kept pace with the rapid evolution of the AM industry and its reliance on highly coupled process-structure–property-performance relationships.

Characterization and Analytical Technique↗
DOE OSTI2021

Enabling modern data discovery for atmospheric measurements

The Atmospheric Radiation Measurement (ARM) user facility is a US Department of Energy Office of Science user facility that is managed and operated through a collaborative effort led by nine US Department of Energy national laboratories. The ARM Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. The ARM Data Center holds more than 11,000 data products, including metadata collected from field campaigns, instruments, value-added products, and principal investigator–contributed data. These data sets are checked for successful transfer (for most data, this transfer is carried out automatically via the network; however, some of the largest data sets and some of the most remote sites require manual shipping of hard disks) and both the data and metadata are processed to a standard format, which is an ARM-standardized structure, via the Network Common Data Form. The Network Common Data Form is a self-describing binary format with many compatible software tools. Once processed, the data are cataloged, stored in the ARM Data Archive, and made discoverable through association with an array of metadata-characterizing information, such as location and measurement classification. These metadata enable powerful search capabilities through the ARM Data Center Data Discovery interface. This paper discusses the workflow of how the new discovery system has been redesigned from user requirements and how the data are distributed to the scientific community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Generation and representation of synthetic smart meter data

Advanced energy algorithms running at big-data scale will be necessary to identify, realize, and verify energy savings to meet government and utility goals of building energy efficiency. Any algorithm must be well characterized and validated before it is trusted to run at these scales. Smart meter data from real buildings will ultimately be required for the development, testing, and validation of these energy algorithms and processes. However, for initial development and testing, smart meter data are difficult to work with due to privacy restrictions, noise from unknown sources, data accessibility, and other concerns which can complicate algorithm development and validation. This paper describes a new methodology to generate synthetic smart meter data of electricity use in buildings using detailed building energy modeling, which aims to capture the variability and stochastics of real energy use in buildings. The methodology can create datasets tailored to represent specific scenarios with known truth and controllable amounts of synthetic noise. Knowledge of ground truth also allows the development and validation of enhanced processes which leverage building metadata, such as building type or size (floor area), in addition to smart meter data. The methodology described in this paper includes the key influencing factors of real-world building energy use including weather data, occupant-driven loads, building operation and maintenance practices, and special events. Data formats to support workflows leveraging both synthetic meter data and associated metadata are proposed and discussed. Finally, example use cases of the synthetic meter data are described to illustrate potential applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Implementing a unified solver for nonlinearly constrained optimization

SQP and interior-point methods (also referred to as Lagrange-Newton methods) typically share key algorithmic components, such as strategies for computing descent directions and mechanisms that promote global convergence. Building on this insight, we introduce a unifying framework with eight building blocks that abstracts the workflows of Lagrange-Newton methods. We then present Uno, a modular C++ solver that implements our unifying framework and allows the automatic combination of a wide range of strategies with no programming effort from the user. Uno is meant to (1) organize mathematical optimization strategies into a coherent hierarchy; (2) offer a wide range of efficient and robust methods that can be compared for a given instance; (3) enable researchers to experiment with novel optimization strategies; and (4) reduce the cost of development and maintenance of multiple optimization solvers. Uno’s software design allows user to compose new customized solvers for emerging optimization areas such as robust optimization or optimization problems with complementarity constraints, while building on reliable nonlinear optimization techniques. We demonstrate that Uno is highly competitive against state-of-the-art solvers filterSQP, IPOPT, SNOPT, MINOS, LANCELOT, LOQO, and CONOPT on a subset of 429 small problems from the CUTE collection. Uno is available as open-source software under the MIT license at https://github.com/cvanaret/Uno and via its C, Julia, Python, Fortran, and AMPL interfaces.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

microMS: A Python Platform for Image-Guided Mass Spectrometry Profiling

Image-guided mass spectrometry (MS) profiling provides a facile framework for analyzing samples ranging from single cells to tissue sections. The fundamental workflow utilizes a whole-slide microscopy image to select targets of interest, determine their spatial locations, and subsequently perform MS analysis at those locations. Improving upon prior reported methodology, a software package was developed for working with microscopy images. microMS, for microscopy-guided mass spectrometry, allows the user to select and profile diverse samples using a variety of target patterns and mass analyzers. Written in Python, the program provides an intuitive graphical user interface to simplify image-guided MS for novice users. The class hierarchy of instrument interactions permits integration of new MS systems while retaining the feature-rich image analysis framework. microMS is a versatile platform for performing targeted profiling experiments using a series of mass spectrometers. The flexibility in mass analyzers greatly simplifies serial analyses of the same targets by different instruments. The current capabilities of microMS are presented, and its application for off-line analysis of single cells on three distinct instruments is demonstrated. The software has been made freely available for research purposes. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Quantitative Proteomic Analysis of Optimal Cutting Temperature (OCT) Embedded Core-Needle Biopsy of Lung Cancer

With recent advances in understanding the genomic underpinnings and oncogenic drivers of pathogenesis in different subtypes, it is increasingly clear that proper pretreatment diagnostics are essential for the choice of appropriate treatment options for non-small cell lung cancer (NSCLC). Tumor tissue preservation in optimal cutting temperature (OCT) compound is commonly used in the surgical suite. However, proteins recovered from OCT-embedded specimens pose a challenge for LC-MS/MS experiments, due to the large amounts of polymers present in OCT. Here we present a simple workflow for whole proteome analysis of OCT-embedded NSCLC tissue samples, which involves a simple trichloroacetic acid precipitation step. Comparisons of protein recovery between frozen versus OCT-embedded tissue showed excellent consistency with more than 9200 proteins identified. Using an isobaric labeling strategy, we quantified more than 5400 proteins in tumor versus normal OCT-embedded core needle biopsy samples. Gene ontology analysis indicated that a number of proliferative as well as squamous cell carcinoma (SqCC) marker proteins were overexpressed in the tumor, consistent with the patient’s pathology based diagnosis of “poorly differentiated SqCC”. Among the most downregulated proteins in the tumor sample, we noted a number of proteins with potential immunomodulatory functions. Finally, interrogation of the aberrantly expressed proteins using a candidate approach and cross-referencing with publicly available databases led to the identification of potential druggable targets in DNA replication and DNA damage repair pathways. We conclude that our approach allows LC-MS/MS proteomic analyses on OCT-embedded lung cancer specimens, opening the way to bring powerful proteomics into the clinic. .

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Native Mass Spectrometry, Ion mobility, and Collision-Induced Unfolding Categorize Malaria Antigen/Antibody Binding

Plasmodium vivax Duffy Binding Protein (PvDBP) is a promising vaccine candidate for P. vivax malaria. Recently, we reported the epitopes on PvDBP region II (PvDBP-II) for three inhibitory monoclonal antibodies (2D10, 2H2, and 2C6). In this communication, we describe the combination of native mass spectrometry and ion mobility (IM) with collision induced unfolding (CIU) to study the conformation and stabilities of three malarial antigen–antibody complexes. These complexes, when collisionally activated, undergo conformational changes that depend on the location of the epitope. CIU patterns for PvDBP-II in complex with antibody 2D10 and 2H2 are highly similar, indicating comparable binding topology and stability. A different CIU fingerprint is observed for PvDBP-II/2C6, indicating that 2C6 binds to PvDBP-II on an epitope different from 2D10 and 2H2. This work supports the use of CIU as a means of classifying antigen-antibody complexes by their epitope maps in a high throughput screening workflow. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

A Semi-Empirical Framework for Interpreting Traveling Wave Ion Mobility Arrival Time Distributions

The inherent structural heterogeneity of biomolecules is an important biophysical property that is essential to their function, but is challenging to characterize experimentally. We present a workflow that rapidly and quantitatively assesses the conformational heterogeneity of peptides and proteins in the gas phase using traveling wave ion mobility (TWIM) arrival time distributions (ATDs). We have established a set of semi-empirical equations that model the TWIM ATD peak width and resolution across a wide range of wave amplitudes (V) and wave velocities (v). In addition, a conformational broadening parameter, δ, can be extracted from this analysis that reports on the contribution of conformational heterogeneity to the broadening of TWIM ATD peak width during ion mobility separation. We use this δ value to evaluate the conformational heterogeneity of a set of helical peptides, and our analysis correlates well with previous peak width observations reported for these ions. Furthermore, we use molecular dynamics simulations to independently investigate the general flexibility of these peptides in the gas phase, and generate similar trends found in experimental TWIM data. Finally, we extended our analysis to Avidin, a 64-kDa homotetramer, and quantify the structural heterogeneity of this intact complex using TWIM ATD data as a function of cross-linking. We observe an initial reduction in δ values as a function of cross-linker concentration, demonstrating the sensitivity of our δ value analysis to changes in flexibility of the assembly. .

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Augmented and Virtual Reality Navigation for Interventions in the Musculoskeletal System

Purpose of Review: Augmented reality (AR) and virtual reality (VR) are cutting-edge technologies that offer advanced navigational solutions. These systems are just starting to be used in interventional radiology and the literature has been limited to pre-clinical and translational experiments. In this paper, we present a review of AR and VR for musculoskeletal interventions. Recent Findings: Mixed reality systems have evolved from costly research systems to fully commercialized clinical instruments designed to facilitate operators in navigating complex anatomy. Studies within interventional radiology have demonstrated safety, improved accuracy, and decreased exposure to ionizing radiation. Summary: We review the progression of mixed reality systems from their early origins in computer science through their current day surgical applications, with a special focus on landmark studies within radiologic interventions for the musculoskeletal system. We highlight the instrumentation, clinical workflow, benefits and drawbacks, and suggested future directions for the two main AR systems: head-mounted display and image-overlay.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

MR Imaging of Patients with Cardiac Implantable Electronic Devices (CIEDs): Implementing a Program and Optimizing CMR

Purpose of review: How to implement a program for MRI in patients with conditional and non-conditional (“legacy”) CIED. How to optimize safety and image quality in CMR? What the radiologists need to know: device programming, pre- and post-MRI CIED parameters. Recent findings: New Center for Medicare and Medicaid Services (CMS) guidelines have opened MRI to many patients with nonconditional devices. Newer pulse sequences for cardiac MRI (CMR) in device patients, including modified cine, late gadolinium enhancement and T1 mapping sequences, have resulted in improved image quality in device patients. We present several cases of CMR in device patients, including imaging of leadless pacemakers subcutaneous ICDs. Imaging pitfalls/artifacts in CMR interpretation are also presented. Summary: An organized institutional workflow with an established protocol involving proper patient screening, device programming, scan monitoring, and pre- and post-ICD interrogation minimizes risk in patients with conditional and non-conditional CIEDs. Although rare, significant changes in lead performance, power-on/reset, inappropriate pacing or ICD discharges and battery voltage depletion should be documented. High-quality scans with accurate scar characterization in CIED patients require the use of wideband sequences. CMS has recently acknowledged MRI as a reasonable test necessary for the diagnosis and treatment of beneficiaries with implanted CIEDs, improving the financial prospects and encouraging future developments.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community

Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multimodal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. Furthermore, modern materials engineering requires the tuning of not only composition but also of phase and microstructure to elucidate processing–structure–property–performance relationships. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing (i) synthesis and processing conditions, (ii) characterization results, and (iii) property and performance measurements. In this work, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure.

36 MATERIALS SCIENCE↗