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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Material Needs and Measurement Challenges for Advanced Semiconductor Packaging: Understanding the Soft Side of Science

This Perspective builds upon insights from the National Institute of Standards and Technology (NIST)-organized workshop, “Materials and Metrology Needs for Advanced Semiconductor Packaging Strategies,” held at the 35th annual Electronics Packaging Symposium in Binghamton, NY, on September 5, 2024. It outlines critical challenges and opportunities related to polymer-based “soft” materials in advanced semiconductor packaging, with emphasis on polymer science, measurement science (metrology), and the strategic development of Research-Grade Test Materials (RGTMs). These efforts, led by the NIST CHIPS team, aim to advance the fundamental understanding of structure-property-processing relationships, promote standardized guidelines and innovative methods for material characterization, and accelerate the development, qualification, and adoption of next-generation packaging materials. The Perspective also distills key insights from the panel discussion with industry experts, emphasizing the need for close collaboration among materials scientists, process engineers, and metrology experts to enable a holistic strategy, further highlighting the importance of cross-sector partnerships among industry, academia, and government to address pressing challenges in packaging materials and processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Preface for the special topic collection honoring Dr. Scott Chambers’ 70th birthday and his leadership in the science and technology of oxide thin films

It is an honor to dedicate this special issue to Dr. Scott A. Chambers, who has had a rewarding and impactful career in surface science, spectroscopy, and thin film synthesis. His research career, spanning from his graduate work in the 1970’s to the present day, was built upon pioneering early work in precision thin film synthesis and spectroscopic characterization that occurred beginning in the 1960’s. Notably, this includes the contributions of both Art Gossard to precision film synthesis by molecular beam epitaxy (MBE) and Chuck Fadley to photoelectron spectroscopy; both Art and Chuck were recently honored with JVSTA commemorative issues of their own. Yet Scott is no mere copycat; he extended and expanded their contributions to further advance the field of surface science, and he applied the same scientific rigor to the emerging field of precision epitaxial oxide synthesis. This rigor was perhaps not always appreciated by the more “enthusiastic” members of the community who tended to draw exciting conclusions from limited data. He was once referred to, fondly, by a collaborator as a “spoilsport” for his penchant for using careful, defensible synthesis and characterization to prove that popular models and assumptions of the day did not stand up to scrutiny.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Microscopy with ultraviolet surface excitation (MUSE) enables translation of optical biopsy principles to enhance life science education

The translation of microscopy with ultraviolet surface excitation (MUSE) into a high school science classroom is investigated with the goal of providing a suitable new modality to enhance life science education. A key part of this effort is the development of laboratory exercises that can integrate the advanced capabilities of MUSE into a classroom setting. MUSE utilizes the unique property of ultraviolet light at wavelengths between 250 and 285 nm to propagate about 10 um into tissues, thus illuminating only the top cell layer. Furthermore this illumination is provided by a low-power UV LED source, which enables one to cost-efficiently implement this method into the educational environment. MUSE in education can eliminate the need for premade microscope slides and provide a far more engaging and rewarding experience for students.

47 OTHER INSTRUMENTATION↗
DOE OSTI2013

Hell and High Water: Practice-Relevant Adaptation Science

Recent extreme weather such as Hurricane Sandy and the 2012 drought demonstrate the vulnerability of the United States to climate extremes in the present and point to the potential for increased future damages under a changing climate. They also provide lessons for reducing harm and realizing any potential benefits. Preparedness measures – also referred to as adaptation – can cost-effectively increase resilience today and in the future. The upfront costs will be more than offset by reductions in property damage, lives and livelihoods lost, and expensive post-disaster recovery processes. While others have addressed use of science for adaptation in specific sectors including biodiversity (Heller and Zavaleta, 2009) and freshwater ecosystem management (Wilby et al., 2010), or by simply taking a more pragmatic approach to adaptation under uncertainty (Hallegatte, 2009), here the authors make the case that a new, comprehensive approach is needed to create and use science to inform adaptations with applicable and sound knowledge (Kerr et al., 2011).

Moss, Richard H.↗
DOE OSTI2021

Planned Relocation: A Pluralistic and Integrated Approach to Science and Governance

Planned relocation is an ambitious objective that occurs in complex governance settings rife with inequalities of power, discrimination, distrust, deep uncertainty, misalignment of incentives and authorities, and other challenges. Taking planned relocation from ambition to an equitable and effective portfolio of measures will require changes in science, governance and how they work together. Research will need to understand perverse incentives, conflicts, limits to risk perception, and other factors across individuals, communities, governance levels, and sectors. Knowledge to enable planned relocation will emerge not just from technical studies but from entwined processes of action, learning and capacity building. Science and governance must advance their collaboration to better support and learn from these tangled processes to identify adaptive pathways, build agency and implement strategies under deep uncertainty.

global environmental change, adapatation, decision↗
DOE OSTI2017

Application of nuclear-physics methods in space materials science

The brief history of the development of investigations at the Skobeltsyn Institute of Nuclear Physics, Moscow State University (SINP MSU) in the field of space materials science is outlined. A generalized scheme of a numerical simulation of the radiation impact on spacecraft materials and elements of spacecraft equipment is examined. The results obtained by solving some of the most important problems that modern space materials science should address in studying nuclear processes, the interaction of charged particles with matter, particle detection, the protection from ionizing radiation, and the impact of particles on nanostructures and nanomaterials are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2011

Neutron activation analysis in the life sciences

Development of methods for instrumental neutron activation analysis (INAA) and their applications in the life sciences are reviewed. Emphasis is placed on epithermal activation with reactor neutrons (ENAA), and the advantages of this technique in analysis of environmental objects are shown. The results of applied INAA studies in the field of the life sciences carried out at the world’s leading nuclear centers are reported. Experience in employing a radioanalytical complex at the IBR-2 reactor (Frank Laboratory of Neutron Physics, Joint Institute for Nuclear Research, Dubna) for such studies is summarized.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Machine learning-driven predictive resource management in complex science workflows

Here, the collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

FAIR Ecosystems for Science at Scale

High Performance Computing (HPC) centers provide resources to users who require greater scale to “get science done”. They deploy infrastructure with singular hardware architectures, cutting-edge software environments, and stricter security measures as compared with users’ own resources. As a result, users often create and configure digital artifacts in ways that are specialized for the unique infrastructure at a given HPC center. Each user of that center will face similar challenges as they develop specialized solutions to take full advantages of the center’s resources, potentially resulting in significant duplication of effort. Much duplicated effort could be avoided, however, if users of these centers found it easier to discover others’ solutions and artifacts as well as share their own. The FAIR principles address this problem by presenting guidelines focused around metadata practices to be implemented by vaguely defined “communities”; in practice, these tend to gather by domain (e.g. bioinformatics, geosciences, agriculture). Domain-based communities can unfortunately end up functioning as silos that tend both to inhibit sharing of solutions and best practices as well as to encourage fragile and unsustainable improvised solutions in the absence of best-practice guidance. We propose that these communities pursuing “science at scale” be nurtured both individually and collectively by HPC centers so that users can take advantage of shared challenges across disciplines and potentially across HPC centers. We describe an architecture based on the EOSC-Life FAIR Workflows Collaboratory, specialized for use with and inside HPC centers such as the Oak Ridge Leadership Computing Facility (OLCF), and we speculate on user incentives to encourage adoption. We note that a focus on FAIR workflow components rather than FAIR workflows is more likely to benefit the users of HPC centers.

Wilkinson, Sean [ORNL] (ORCID:0000000214437479)↗
DOE OSTI2016

Nuclear Forensic Science: Analysis of Nuclear Material Out of Regulatory Control

Nuclear forensic science seeks to identify the origin of nuclear materials found outside regulatory control. It is increasingly recognized as an integral part of a robust nuclear security program. Our review highlights areas of active, evolving research in nuclear forensics, with a focus on analytical techniques commonly employed in Earth and planetary sciences. Applications of nuclear forensics to uranium ore concentrates (UOCs) are discussed first. UOCs have become an attractive target for nuclear forensic researchers because of the richness in impurities compared to materials produced later in the fuel cycle. Furthermore, the development of chronometric methods for age dating nuclear materials is then discussed, with an emphasis on improvements in accuracy that have been gained from measurements of multiple radioisotopic systems. Finally, papers that report on casework are reviewed, to provide a window into current scientific practice.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Quantitative Parameter Estimation, Model Selection, and Variable Selection in Battery Science

Numerical physics-based models are useful in understanding battery performance and devel- oping optimal battery design architectures. Data science developments have enabled software algorithms to perform data analysis and decision making that traditionally could only be per- formed by technical experts. Traditional workflows of model development - manual parameter estimation through visual comparison of simulations to experimental observations, and model discrimination through expert intuition - are time-consuming, and difficult to justify. This paper compares the conclusions derived from computationally scalable algorithms to conclu- sions developed by expert researchers. This paper illustrates how data science techniques, such as cross-validation and lasso regression, can be used to augment physics-based simulations to perform data analysis such as parameter estimation, model selection, variable selection, and model-guided design of experiment. The validation of these algorithms is that they produce results similar to those of the expert modeler. The algorithms outlined are well-established and the approaches are general, so can be applied to a variety of battery chemistries and architectures. The conclusions reached using these approaches are in agreement with expert analysis (literature results), were reached with minimal human intervention, and provide quantitative justification. By minimizing the amount of expert time, and providing rigorous quantitative justifications, these methods may accelerate battery development.

25 ENERGY STORAGE↗
DOE OSTI2017

Template Interfaces for Agile Parallel Data-Intensive Science

Tigres provides a programming library to compose and execute large-scale data-intensive scientific workflows from desktops to supercomputers. DOE User Facilities and large science collaborations are increasingly generating large enough data sets that it is no longer practical to download them to a desktop to operate on them. They are instead stored at centralized compute and storage resources such as high performance computing (HPC) centers. Analysis of this data requires an ability to run on these facilities, but with current technologies, scaling an analysis to an HPC center and to a large data set is difficult even for experts. Tigres is addressing the challenge of enabling collaborative analysis of DOE Science data through a new concept of reusable "templates" that enable scientists to easily compose, run and manage collaborative computational tasks. These templates define common computation patterns used in analyzing a data set.

Ramakrishnan, Lavanya↗
DOE OSTI2023

2023 Data Science Challenge Notebooks

THE DATA SCIENCE INSTITUTE WILL BE INVITING STUDENTS FROM UC MERCED AND UC RIVERSIDE TO PARTICIPATE IN A DATA SCIENCE CHALLENGE AT LLNL. IN THE CHALLENGE, THE STUDENTS WILL EXPLORE A DATA-DRIVEN APPROACH TO RECONSTRUCTING ELECTROANATOMICAL MAPS OF THE HEART AT CLINICALLY RELEVANT RESOLUTIONS, COMBINING INPUT FROM THE STANDARD 12-LEAD ECG WITH ADVANCED MACHINE LEARNING TECHNIQUES. THE DATA THE STUDENTS WILL HAVE ACCESS TO HAS BEEN IM REVIEWED FOR EXTERNAL RELEASE. (LLNL-MI-835833) THESE NOTEBOOKS WILL PROVIDE THE STUDENTS WITH INSTRUCTIONS OF HOW TO LOAD THE DATA AND WORK WITH THE DATA, ALONG WITH TUTORIALS TO DO BASIC MACHINE LEARNING TASKS LIKE LOGISTIC REGRESSION AND IMAGE CLASSIFICATION.

Landajuela Larma, Mikel↗
DOE OSTI2023

Data Science Infrastructure SOFTWARE

LANL science workflows generate complex data sets and ensembles of data requiring significant compute and storage resources. The Data Science Infrastructure (DSI) project focuses on data-driven approaches to make data more readily available to LANL projects. DSI workflows leverage metadata stored in data-agnostic databases, supported by an abstraction layer API to simplify searching and accessing data across simulation runs, experimental runs, filesystems and environments. The abstraction layer API allows the user to query a range of data types: raw output, processed data, configuration data, machine learning models, performance data, etc. In addition to the abstraction backend API, the DSI project is developing client-driven query APIs and UIs to support specific user workflows.

Turton, Terece↗
DOE OSTI2025

BLDAP Intro to Python/Data Science Curriculum v1

The Github repository contains the Jupyter notebooks for the intro to Python / Data Science course for Berkeley Lab Director's Apprenticeship Program (BLDAP). This course is designed for students with little to no experience in coding to learn skills in Python necessary for data science. Students utilize Jupyter notebooks throughout the course. The overall goal is for students to learn how to use Python to clean, analyze, and visualize large data sets in order to communicate effectively their conclusions about the data set. Students apply the skills they learned on actual data sets provided by researchers in Berkeley Lab.

Hales, Laurel [Lawrence Berkeley National Laborato↗
DOE OSTI2016

Application of DRIFTS, 13 C NMR, and py-MBMS to Characterize the Effects of Soil Science Oxidation Assays on Soil Organic Matter Composition in a Mollic Xerofluvent

Chemical oxidations are routinely employed in soil science to study soil organic matter (SOM), and their interpretation could be improved by characterizing oxidation effects on SOM composition with spectroscopy. We investigated the effects of routinely employed oxidants on SOM composition in a Mollic Xerofluvent representative of intensively managed agricultural soils in the California Central Valley. Soil samples were subjected to oxidation by potassium permanganate (KMnO4), sodium hypochlorite (NaOCl), and hydrogen peroxide (H2O2). Additionally, non-oxidized and oxidized soils were treated with hydrofluoric acid (HF) to evaluate reduction of the mineral component to improve spectroscopy of oxidation effects. Oxidized non-HF and HF-treated soils were characterized by diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), 13C cross polarization magic angle spinning (CP-MAS) nuclear magnetic resonance (NMR) spectroscopy, and pyrolysis molecular beam mass spectrometry (py-MBMS), and for particle size distribution (PSD) using laser diffractometry (LD). Across the range of soil organic carbon (OC) removed by oxidations (14-72%), aliphatic C-H stretch at 3000-2800 cm-1 (DRIFTS) decreased with OC removal, and this trend was enhanced by HF treatment due to significant demineralization in this soil (70%). Analysis by NMR spectroscopy was feasible only after HF treatment, and did not reveal trends between OC removal and C functional groups. Pyrolysis-MBMS did not detect differences among oxidations, even after HF treatment of soils. Hydrofluoric acid entailed OC loss (13-39%), and for H2O2 oxidized soils increased C:N and substantially decreased mean particle size. This study demonstrates the feasibility of using HF to improve characterizations of SOM composition following oxidations as practiced in soil science, in particular for DRIFTS. Since OC removal by oxidants, mineral removal by HF, and the interaction of oxidants and HF observed for this soil may differ for soils with different mineralogies, future work should examine additional soil and land use types to optimize characterizations of oxidation effects on SOM composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

BioHackathon 2015: Semantics of data for life sciences and reproducible research

We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Materials data science using CRADLE: A distributed, data-centric approach

Abstract There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE’s capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed. Graphical abstract

97 MATHEMATICS AND COMPUTING↗