Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “SCIENTISTS”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Preface: Challenges for Catalytic Exhaust Aftertreatment

This special issue of Catalysis Today continues the tradition established since the 18th NAM in Cancun, 2003, of publishing the highlights coming from these catalytic after-treatment technologies sessions, where this volume contains 18 papers based on oral and poster presentations of the 23rd NAM, 2013. The guest editors would like to thank all of the catalyst scientists and engineers who presented in the "Emission control" sessions, and especially the authors who contributed to this special issue of Catalysis Today.

diesel particulate filters↗
DOE OSTI2019

Pollen Cell Wall Patterns Form from Modulated Phases

The ornately geometric walls of pollen grains have inspired scientists for decades. Here, we show that the evolved diversity of these patterns is entirely recapitulated by a biophysical model in which an initially uniform polysaccharide layer in the extracellular space, mechanically coupled to the cell membrane, phase separates to a spatially modulated state. Experiments reveal this process occurring in living cells. We observe that in ~10% of extant species, this phase separation reaches equilibrium during development such that individual pollen grains are identical and perfectly reproducible. About 90% of species undergo an arrest of this process prior to equilibrium such that individual grains are similar but inexact copies. Equilibrium patterns have appeared multiple times during the evolution of seed plants, but selection does not favor these states. This framework for pattern development provides a route to rationalizing the surface textures of other secreted structures, such as cell walls and insect cuticle.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

The silicon citizen naturalist

Smartphone-wielding citizen scientists and an AI called FLORIST are transforming ecology at the continental scale. Here, in this issue of Cell, when Tibbs-Cortes et al. pair the crowdsourced data with controlled genetics, they discover how switchgrass times its flowering to outwit both frost and heat, depending on latitude.

Hudson, Matthew E. [University of Illinois at Urba↗
DOE OSTI2025

Component Identification of Solid Biomass Fuels Using Reflected Light Microscopy: Interlaboratory Study 2

As nations transition toward sustainable energy systems, biomass has become a vital component of global energy portfolios. Derived from organic materials such as wood, agricultural residues, forestry byproducts, and organic waste, biomass is a renewable energy source with significant environmental and economic benefits. Responsible biomass energy production can improve waste management, reduce emissions of greenhouse gases, and mitigate environmental pollution. However, as the diversity of biomass-derived fuels increases, robust quality assessment methods are essential to ensure their efficiency, safety, and minimal environmental impact. Reflected light microscopy (RLM) is one such technique with the potential to complement conventional physico-chemical analyses by enabling a rapid identification of material constituents and impurities. To refine this methodology and evaluate the reproducibility of solid biomass component identification using RLM, an interlaboratory study (ILS) was conducted. The study involved the recognition of 58 components across 45 photomicrographs, with the participation of 65 scientists and students from 25 countries. The participants faced high difficulty identifying some of the marked components, and as a result, the percentage of correct answers ranged from 19.0 % to 98.3 %, with an average correct identification rate of 62.7 %. The most challenging aspects of the identification process included distinguishing between woody and non-woody (agro) biomass, accurately identifying petroleum-derived materials, and differentiating agro biomass from inorganic matter. The results suggest that while RLM is an important tool for characterizing solid biomass, further development of methodology guidelines and training are necessary to enhance its effectiveness. Future research should prioritize preparing detailed, image-rich, microscopic morphological descriptions of biomass fuel components, which could improve the accuracy and reliability of using RLM in biomass fuel characterization.

09 BIOMASS FUELS↗
DOE OSTI2019

Cell-machine interfaces for characterizing gene regulatory network dynamics

Gene regulatory networks and the dynamic responses theyproduce offer a wealth of information about how biologicalsystems process information about their environment.Recently, researchers interested in dissecting these networkshave been outsourcing various parts of their experimentalworkflow to computers. Here we review how, using microfluidicor optogenetic tools coupled with fluorescence imaging, it isnow possible to interface cells and computers. These platformsenable scientists to perform informative dynamic stimulationsof genetic pathways and monitor their reaction. It is alsopossible to close the loop and regulate genes in real time,providing an unprecedented view of how signals propagatethrough the network. Finally, we outline new tools that can beused within the framework of cell–machine interfaces.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Dedication to James A. Miller

This special memorial issue pays tribute to James (Jim) A. Miller, a giant of combustion science who died in 2021, with a celebration of his enormous influence on the field. We were touched by the responses we received after we sent out the invitations for it. Jim inspired several generations of scientists, who viewed him as a mentor, a father figure, and a friend. Together with Nils Hansen and Peter Glarborg, we have written a detailed account on his life and work. Furthermore, it appeared in this journal shortly after his death; and so here we focus on the scientific areas he had interest in and influence on, and how they relate to the 34 papers in this issue. The topics of these papers span a variety of Jim's interests including nitrogen chemistry, polycyclic aromatic hydrocarbon (PAH) chemistry, oxidation chemistry, energy transfer, prompt dissociations, and codes to facilitate combustion chemistry simulations.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Deep learning approaches to semantic segmentation of fatigue cracking within cyclically loaded nickel superalloy

Improvements to synchrotron-based micro-computed tomography scanning capabilities have gifted researchers the ability to characterize 4D material thermomechanical responses more thoroughly than ever before. These advancements, however, have brought about new challenges in analyzing the resulting deluge of data. We report on a nickel-based superalloy specimen imaged 26 times in-situ during cyclic loading at Argonne National Laboratory Advanced Photon Source beamline 1ID, in order to monitor crack growth within the microstructure. Therefore, several deep learning approaches which utilize convolutional neural networks are implemented to segment crack features from reconstructed tomography scans. U-Net architecture implementations are found to be especially effective, achieving IoU = 0.995 +/- 0.004 and Matthews correlation coefficient scores of Φ = 0.826 +/- 0.085. These advancements broaden possibilities for scientists seeking to automate segmentation analyses of similar large datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Self-supervised learning of spatiotemporal thermal signatures in additive manufacturing using reduced order physics models and transformers

Microstructure control via additive manufacturing has enormous potential as manufacturers, materials scientists, and designers alike seek to exploit novel fabrication technologies to improve component performance. Recent works have demonstrated the feasibility of producing materials with controlled microstructures across various length scales. However, the experimental approach towards exploring the process-structure space can be laborious and costly. This is particularly true if also considering scan pattern optimization which is well suited for processes such as powder bed fusion electron beam melting. In this work we propose an approach for encoding additive manufacturing layer-wise thermal response signatures using self-supervised representation learning. Thermal simulations from a reduced order model are utilized to estimate the spatiotemporal response during printing. A machine learning framework, using video-transformers, is utilized to efficiently distill spatiotemporal patterns into a compact latent space representation. This latent state representation encodes the relevant physics which is then utilized to establish a data-driven process-structure model for an additively manufactured Ni-based superalloy. In conclusion, the proposed methodology could potentially be used towards in-situ process monitoring, scan pattern experimental design, and component qualification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Matrix assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) for direct visualization of plant metabolites in situ

Direct visualization of plant tissues by matrix assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) has revealed key insights into the localization of metabolites in situ . Recent efforts have determined the spatial distribution of primary and secondary metabolites in plant tissues and cells. Strategies have been applied in many areas of metabolism including isotope flux analyses, plant interactions, and transcriptional regulation of metabolite accumulation. Technological advances have pushed achievable spatial resolution to subcellular levels and increased instrument sensitivity by several orders of magnitude. Furthermore, it is anticipated that MALDI-MSI and other MSI approaches will bring a new level of understanding to metabolomics as scientists will be encouraged to consider spatial heterogeneity of metabolites in descriptions of metabolic pathway regulation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Standards, dissemination, and best practices in systems biology

In this study, the reproducibility of scientific research is crucial to the success of the scientific method. Here, we review the current best practices when publishing mechanistic models in systems biology. We recommend, where possible, to use software engineering strategies such as testing, verification, validation, documentation, versioning, iterative development, and continuous integration. In addition, adhering to the Findable, Accessible, Interoperable, and Reusable modeling principles allows other scientists to collaborate and build off of each other’s work. Existing standards such as Systems Biology Markup Language, CellML, or Simulation Experiment Description Markup Language can greatly improve the likelihood that a published model is reproducible, especially if such models are deposited in well-established model repositories. Where models are published in executable programming languages, the source code and their data should be published as open-source in public code repositories together with any documentation and testing code. For complex models, we recommend container-based solutions where any software dependencies and the run-time context can be easily replicated.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

ESPResSo++ 2.0: Advanced methods for multiscale molecular simulation

We report that molecular simulation is a scientific tool used in many fields including material science and biology. This requires constant development and enhancement of algorithms within molecular simulation software packages. Here, we present computational tools for multiscale modeling developed and implemented within the ESPResSo++ package. These include the latest applications of the adaptive resolution scheme, the hydrodynamic interactions through a lattice Boltzmann solvent coupled to particle-based molecular dynamics, the implementation of the hierarchical strategy for equilibrating long-chained polymer melts and a heterogeneous spatial domain decomposition. The software design of ESPResSo++ has kept its highly modular C++ kernel with a Python user interface. Furthermore, it has been enhanced by automatic scripts that parse configurations from other established packages, providing scientists with the ability to rapidly set up their simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Past and future climate change in the context of memorable seasonal extremes

It is thought that direct personal experience of extreme weather events could result in greater public engagement and policy response to climate change. Based on this premise, we present a set of future climate scenarios for Ireland communicated in the context of recent, observed extremes. Specifically, we examine the changing likelihood of extreme seasonal conditions in the long-term observational record, and explore how frequently such extremes might occur in a changed Irish climate according to the latest model projections. Over the period (1900–2014) records suggest a greater than 50-fold increase in the likelihood of the warmest recorded summer (1995), whilst the likelihood of the wettest winter (1994/95) and driest summer (1995) has respectively doubled since 1850. The most severe end-of-century climate model projections suggest that summers as cool as 1995 may only occur once every ~7 years, whilst winters as wet as 1994/95 and summers as dry as 1995 may increase by factors of ~8 and ~10 respectively. Contrary to previous research, we find no evidence for increased wintertime storminess as the Irish climate warms, but caution that this conclusion may be an artefact of the metric employed. It is hoped that framing future climate scenarios in the context of extremes from living memory will help communicate the scale of the challenge climate change presents, and in so doing bridge the gap between climate scientists and wider society.

Climate change communication↗
DOE OSTI2025

Fungal endophytes

Organisms are commonly grouped into ecological guilds that reflect their shared resource use and similar ecological roles. The guild concept has been used to categorize the vast diversity of the fungal kingdom (estimated at 2.2–5 million species) into groups of fungi with common lifestyles, such as mycorrhizal symbionts, pathogens of animals and plants, and the group that is the focus of this primer — fungal endophytes of plants. Fungal endophytes have resisted scientists’ attempts to silo organisms into neat assemblages. In conclusion, scattered across the fungal tree of life and lacking few diagnostic characteristics, they are defined less by what they are than by what they are not.

Cosner, Julian [Oak Ridge National Laboratory (ORN↗
DOE OSTI2025

TPCpp-10M: Simulated proton-proton collisions in a time projection chamber for AI foundation models

Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field is often limited by the lack of openly available large scale datasets, as well as standardized evaluation tasks and metrics. Furthermore, the specialized knowledge and software typically required to process particle physics data pose significant barriers to interdisciplinary collaboration with the broader machine learning community. This work introduces a large, openly accessible dataset of 10 million simulated proton-proton collisions, designed to support self-supervised training of foundation models. To facilitate ease of use, the dataset is provided in a common NumPy format. In addition, it includes 70,000 labeled examples spanning three well defined downstream tasks: track finding, particle identification, and noise tagging, to enable systematic evaluation of the foundation model's adaptability. The simulated data are generated using the Pythia Monte Carlo event generator at a center of mass energy of $\sqrt{s}$ = 200 GeV and processed with Geant4 to include realistic detector conditions and signal emulation in the sPHENIX Time Projection Chamber at the Relativistic Heavy Ion Collider, located at Brookhaven National Laboratory. This dataset resource establishes a common ground for interdisciplinary research, enabling machine learning scientists and physicists alike to explore scaling behaviors, assess transferability, and accelerate progress toward foundation models in nuclear and high energy physics. The complete simulation and reconstruction chain is reproducible with the sPHENIX software stack. All data and code locations are provided under Data Accessibility.

Data Analysis, Statistics and Probability (physics↗
DOE OSTI2018

The confluences of ideas leading to, and the flow of ideas emerging from, individual-based modeling of riverine fishes

In this review article, we trace the history of events leading to the development of individual-based models (IBMs) to represent aquatic organisms in rivers and streams. As a metaphor, we present this history as a series of confluences between individual scientists (tributaries) sharing ideas. We describe contributions of these models to science and management. One iconic feature of river IBMs is the linkage between flow and the physical habitat experienced by individual animals, and the first model that focused on this linkage is briefly described. We continue by reviewing the contributions of riverine IBMs to eight broad areas of scientific inquiry. The first four areas include research to understand the effects of flow regimes on fish populations, species interactions (e.g., size-mediated competition and predation), fish movement and habitat selection, and contaminant and water quality impacts on populations. Next, we review research using IBMs to guide conservation biology of imperiled taxa through population viability analysis, including research to understand river fragmentation by dams and reconnection, to understand genetic outcomes for riverine metapopulations, and to anticipate the future effects of temperature and climate change. This rich body of literature has contributed to both theoretical insights (e.g., about animal behavior and life history) and applied insights (e.g., population-level effects of flow regimes, temperature, and the effects of hydropower and other industries that share rivers with aquatic biota). Lastly, we finish by exploring promising branches that lie ahead in the braided, downstream channel that represents future river modeling research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Numerical Simulation of Carbon Dioxide Injection in the Western Section of the Farnsworth Unit

Numerical simulation is an invaluable analytical tool for scientists and engineers in making predictions about of the fate of carbon dioxide injected into deep geologic formations for long-term storage. Current numerical simulators for assessing storage in deep saline formations have capabilities for modeling strongly coupled processes involving multifluid flow, heat transfer, chemistry, and rock mechanics in geologic media. Except for moderate pressure conditions, numerical simulators for deep saline formations only require the tracking of two immiscible phases and a limited number of phase components, beyond those comprising the geochemical reactive system. The requirements for numerically simulating the utilization and storage of carbon dioxide in partially depleted petroleum reservoirs are more numerous than those for deep saline formations. The minimum number of immiscible phases increases to three, the number of phase components may easily increase fourfold, and the coupled processes of heat transfer, geochemistry, and geomechanics remain. Public and scientific confidence in the ability of numerical simulators used for carbon dioxide sequestration in deep saline formations has advanced via a natural progression of the simulators being proven against benchmark problems, code comparisons, laboratory-scale experiments, pilot-scale injections, and commercial-scale injections. This paper describes a new numerical simulator for the scientific investigation of carbon dioxide utilization and storage in partially depleted petroleum reservoirs, with an emphasis on its unique features for scientific investigations; and documents the numerical simulation of the utilization of carbon dioxide for enhanced oil recovery in the western section of the Farnsworth Unit and represents an early stage in the progression of numerical simulators for carbon utilization and storage in depleted oil reservoirs.

carbon sequestration↗