Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “biological physics”

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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Fluid Physics and Macromolecular Crystal Growth in Microgravity

The molecular structure of biological macromolecules is important in understanding how these molecules work and has direct application to rational drug design for new medicines and for the improvement and development of industrial enzymes. In order to obtain the molecular structure, large, well formed, single macromolecule crystals are required. The growth of macromolecule crystals is a difficult task and is often hampered on the ground by fluid flows that result from the interaction of gravity with the crystal growth process. One such effect is the bulk movement of the crystal through the fluid due to sedimentation. A second is buoyancy driven convection close to the crystal surface. On the ground the crystallization process itself induces both of these flows. Buoyancy driven convection results from density differences between the bulk solution and fluid close to the crystal surface which has been depleted of macromolecules due to crystal growth. Schlieren photograph of a growing lysozyme crystal illustrating a 'growth plume' resulting from buoyancy driven convection. Both sedimentation and buoyancy driven convection have a negative effect on crystal growth and microgravity is seen as a way to both greatly reduce sedimentation and provide greater stability for 'depletion zones' around growing crystals. Some current crystal growth hardware however such as those based on a vapor diffusion techniques, may also be introducing unwanted Marangoni convection which becomes more pronounced in microgravity. Negative effects of g-jitter on crystal growth have also been observed. To study the magnitude of fluid flows around growing crystals we have attached a number of different fluorescent probes to lysozyme molecules. At low concentrations, less than 40% of the total protein, the probes do not appear to effect the crystal growth process. By using these probes we expect to determine not only the effect of induced flows due to crystal growth hardware design but also hope to optimize crystallization hardware so that destructive flows are minimized both on the ground and in microgravity.

Pusey, M.↗
DOE OSTI2022

Challenges of Future Accelerators for Particle Physics Research

For over half a century, high-energy particle accelerators have been a major enabling technology for particle and nuclear physics research as well as sources of X-rays for photon science research in material science, chemistry and biology. Particle accelerators for energy and intensity Frontier research in particle and nuclear physics continuously push the accelerator community to invent ways to increase the energy and improve the performance of accelerators, reduce their cost, and make them more power efficient. The accelerator community has demonstrated imagination and creativity in developing a plethora of future accelerator ideas and proposals. The technical maturity of the proposed facilities ranges from shovel-ready to those that are still largely conceptual. At this time, over 100 contributed papers have been submitted to the Accelerator Frontier of the US particle physics decadal community planning exercise known as Snowmass’2021. These papers cover a broad spectrum of topics: beam physics and accelerator education, accelerators for neutrinos, colliders for Electroweak/Higgs studies and multi-TeV energies, accelerators for Physics Beyond Colliders and rare processes, advanced accelerator concepts, and accelerator technology for Radio Frequency cavities (RF), magnets, targets and sources. This paper provides an overview of the present state of accelerators for particle physics and gives a brief description of some of the major facilities that have been proposed, their perceived advantages and some of the remaining challenges.

43 PARTICLE ACCELERATORS↗
NASA NTRS2003

Self-Supervised Dynamical Systems

Some progress has been made in a continuing effort to develop mathematical models of the behaviors of multi-agent systems known in biology, economics, and sociology (e.g., systems ranging from single or a few biomolecules to many interacting higher organisms). Living systems can be characterized by nonlinear evolution of probability distributions over different possible choices of the next steps in their motions. One of the main challenges in mathematical modeling of living systems is to distinguish between random walks of purely physical origin (for instance, Brownian motions) and those of biological origin. Following a line of reasoning from prior research, it has been assumed, in the present development, that a biological random walk can be represented by a nonlinear mathematical model that represents coupled mental and motor dynamics incorporating the psychological concept of reflection or self-image. The nonlinear dynamics impart the lifelike ability to behave in ways and to exhibit patterns that depart from thermodynamic equilibrium. Reflection or self-image has traditionally been recognized as a basic element of intelligence. The nonlinear mathematical models of the present development are denoted self-supervised dynamical systems. They include (1) equations of classical dynamics, including random components caused by uncertainties in initial conditions and by Langevin forces, coupled with (2) the corresponding Liouville or Fokker-Planck equations that describe the evolutions of probability densities that represent the uncertainties. The coupling is effected by fictitious information-based forces, denoted supervising forces, composed of probability densities and functionals thereof. The equations of classical mechanics represent motor dynamics that is, dynamics in the traditional sense, signifying Newton s equations of motion. The evolution of the probability densities represents mental dynamics or self-image. Then the interaction between the physical and metal aspects of a monad is implemented by feedback from mental to motor dynamics, as represented by the aforementioned fictitious forces. This feedback is what makes the evolution of probability densities nonlinear. The deviation from linear evolution can be characterized, in a sense, as an expression of free will. It has been demonstrated that probability densities can approach prescribed attractors while exhibiting such patterns as shock waves, solitons, and chaos in probability space. The concept of self-supervised dynamical systems has been considered for application to diverse phenomena, including information-based neural networks, cooperation, competition, deception, games, and control of chaos. In addition, a formal similarity between the mathematical structures of self-supervised dynamical systems and of quantum-mechanical systems has been investigated.

Zak, Michail↗
NASA NTRS1993

Potential health effects of space radiation

Crewmembers on missions to the Moon or Mars will be exposed to radiation belts, galactic cosmic rays, and possibly solar particle events. The potential health hazards due to these space radiations must be considered carefully to ensure the success of space exploration. Because there is no human radioepidemiological data for acute and late effects of high-LET (Linear-Energy-Transfer) radiation, the biological risks of energetic charged particles have to be estimated from experimental results on animals and cultured cells. Experimental data obtained to date indicate that charged particle radiation can be much more effective than photons in causing chromosome aberrations, cell killing, mutation, and tumor induction. The relative biological effectiveness (RBE) varies with biological endpoints and depends on the LET of heavy ions. Most lesions induced by low-LET radiation can be repaired in mammalian cells. Energetic heavy ions, however, can produce large complex DNA damages, which may lead to large deletions and are irreparable. For high-LET radiation, therefore, there are less or no dose rate effects. Physical shielding may not be effective in minimizing the biological effects on energetic heavy ions, since fragments of the primary particles can be effective in causing biological effects. At present the uncertainty of biological effects of heavy particles is still very large. With further understanding of the biological effects of space radiation, the career doses can be kept at acceptable levels so that the space radiation environment need not be a barrier to the exploitation of the promise of space.

Yang, Chui-Hsu↗
DOE OSTI2021

Rapid Automated Annotation and Analysis of N-Glycan Mass Spectrometry Imaging Data Sets Using NGlycDB in METASPACE

Imaging N-glycan spatial distribution in tissues using mass spectrometry imaging (MSI) is emerging as a promising tool in biological and clinical applications. However, there is currently no high throughput tool for visualization and molecular annotation of N-glycans in MSI data, which significantly slows down data processing and hampers the applicability of this approach. In this work, we present how METASPACE, an open-source cloud engine for molecular annotation of MSI data, can be used to automatically annotate, visualize, analyze, and interpret high-resolution mass spectrometry-based spatial N-glycomics data. METASPACE is an emerging tool in spatial metabolomics, but the lack of compatible glycan databases has limited its application for comprehensive N-glycan annotations from MSI datasets. We created NGlycDB, a public database of N-glycans, by adapting available glycan databases. We demonstrate the applicability of NGlycDB in METASPACE by analyzing MALDI-MSI data from formalin-fixed paraffin-embedded (FFPE) human kidney and mouse lung tissue sections. We added NGlycDB to METASPACE for public use, thus facilitating applications of MSI in glycobiology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Generating Protein Structures for Pathway Discovery Using Deep Learning

Resolving the intricate details of biological phenomena at the molecular level is fundamentally limited by both length- and time scales that can be probed experimentally. Molecular dynamics (MD) simulations at various scales are powerful tools frequently employed to offer valuable biological insights beyond experimental resolution. However, while it is relatively simple to observe long-lived, stable configurations of, for example, proteins, at the required spatial resolution, simulating the more interesting rare transitions between such states often takes orders of magnitude longer than what is feasible even on the largest supercomputers available today. One common aspect of this challenge is pathway discovery, where the start and end states of a scientific phenomenon are known or can be approximated, but the mechanistic details in between are unknown. Here, we propose a representation-learning-based solution that uses interpolation and extrapolation in an abstract representation space to synthesize potential transition states, which are automatically validated using MD simulations. The new simulations of the synthesized transition states are subsequently incorporated into the representation learning, leading to an iterative framework for targeted path sampling. Our approach is demonstrated by recovering the transition of a RAS-RAF protein domain (CRD) from membrane-free to interacting with the membrane using coarse-grain MD simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Sample Identifiers and Metadata to Support Data Management and Reuse in Multidisciplinary Ecosystem Sciences

Physical samples are foundational entities for research across biological, Earth, and environmental sciences. Data generated from sample-based analyses are not only the basis of individual studies, but can also be integrated with other data to answer new and broader-scale questions. Ecosystem studies increasingly rely on multidisciplinary team-science to study climate and environmental changes. While there are widely adopted conventions within certain domains to describe sample data, these have gaps when applied in a multidisciplinary context. In this study, we reviewed existing practices for identifying, characterizing, and linking related environmental samples. We then tested practicalities of assigning persistent identifiers to samples, with standardized metadata, in a pilot field test involving eight United States Department of Energy projects. Participants collected a variety of sample types, with analyses conducted across multiple facilities. We address terminology gaps for multidisciplinary research and make recommendations for assigning identifiers and metadata that supports sample tracking, integration, and reuse. Furthermore, our goal is to provide a practical approach to sample management, geared towards ecosystem scientists who contribute and reuse sample data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Chapter 4: What We Can Learn from Measuring Metabolic Fluxes in Cyanobacteria

Cyanobacteria with the capability of oxygen-evolving photosynthesis play a vital role in the global carbon, oxygen, and nitrogen cycles. From a biochemical perspective, all phenotypic traits and functions of cyanobacteria are derived from the potential of their metabolism and mirrored in metabolic fluxes through an integrated metabolic network. Metabolic flux, the rate of turnover of molecules through a metabolic pathway, is therefore of great importance for an in-depth understanding of the role of cyanobacteria in the biosphere. Recent interest in cyanobacteria metabolic fluxes is also motivated by the prospect to utilize their light-driven CO2 fixation for sustainable production of valuable chemicals and biofuels. Valorization of cyanobacterial metabolism will require engineering of the native metabolic network - a challenge that can benefit from a quantitative understanding of metabolic reactions taking place intracellularly. In this chapter, we will discuss some intriguing questions relevant to metabolic fluxes in cyanobacteria: How are metabolic fluxes predicted or measured? What do we know about the fluxome inside various cyanobacteria thus far? How are they different between strains as well as in different conditions? And how can we use fluxomics information to guide metabolic engineering and synthetic biology? This chapter will highlight updated knowledge on cyanobacterial metabolism. Specifically, we will focus on providing readers a quantitative vision to understand how this photosynthetic model works.

BASIC BIOLOGICAL SCIENCES,INORGANIC, ORGANIC, PHYS↗
DOE OSTI2024

Spatiotemporal Reaction Dynamics Control in Two‐Photon Polymerization for Enhancing Writing Characteristics

Since 2001, 3D microfabrication based on two‐photon polymerization (TPP) has drawn extensive attention and interest in biology, optics, photonics, material science, and high‐energy physics. The in‐volume fabrication capability due to the threshold behavior of two‐photon absorption enables TPP higher flexibility compared with other nanofabrication techniques. However, as determined by the in‐volume fabrication feature as well as various reaction dynamics, the writing characteristics of TPP, such as throughput, accuracy, surface quality, and fabrication capability, are still limited. Herein, a comprehensive study is performed on the spatiotemporal behavior of reaction dynamics during TPP fabrication, mainly focusing on spatiotemporal characteristics of radical diffusion, photothermal effect, microscale mechanics, and voxel stacking process. Based on the study, a nonsequential fabrication method is established to simultaneously improve key writing characteristics of TPP and realize sharp features, high speeds, large overhang structure, and smooth surfaces. The method established in this work can be applied to improve the performance of functional devices for various fields.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Chiral Spectroscopy of Nanostructures

Chirality is ubiquitous in the universe and in living creatures over detectable length scales from the subatomic to the galactic, as exemplified in the two extremes by subatomic particles (neutrinos) and spiral galaxies. Between them are living creatures that display multiple levels of chirality emerging from hierarchically assembled asymmetric building blocks. Not too far from the bottom of this pyramid are the foundational building blocks with chiral atomic centers on sp 3 carbon atoms exemplified by L-amino acids and D-sugars that are self-assembled into higher-order structures with increasing dimensions forming highly complex, amazingly functional, and energy-efficient living systems. The organization and materials employed in their construction inspired scientists to replicate complex living systems via the self-assembly of chiral components. Multiple studies pointed to unexpected and unique electromagnetic properties of chiral structures with nanoscale and microscale dimensions, including giant circular dichroism and collective circularly polarized scattering that their constituent units did not possess. To address the wide variety of chiral geometries observed in continuous materials, singular particles, and their complex systems, multiple analytic techniques are needed. Simultaneously, their spectroscopic properties create a pathway to multiple applications. For example, mirror-asymmetric vibrations at chiral centers formed by sp 3 carbon atoms lead to optical activity for the infrared (IR) wavelength regions. At the same time, understanding the optical activity in, for example, the IR region enables biomedical applications because multiple modalities of biomedical imaging and vibrational optical activity (VOA) of biomolecules are known for IR range. In turn, VOA can be realized in both absorption and emission modalities due to large magnetic transition moments, as vibrational circular dichroism (VCD) or Raman optical activity (ROA) spectroscopy. In addition to the VOA, in the range of longer wavelengths, lattice vibrational mode or phononic behavior occurs in chiral crystals and nanoassemblies, which can be readily detected by terahertz circular dichroism (TCD) spectroscopy. Meanwhile, chiral self-assembly can induce circularly polarized light emission (CPLE) regardless of the existence of chirality in coassembled fluorophores. The CPLE from self-assembled chiral materials is particularly interesting because the CPLE can originate from both circularly polarized luminescence and circularly polarized scattering (CPS). Furthermore, because self-assembled nanostructures often exhibit stronger optical activity than their building blocks owing to dimension and resonance effects, the optical activity of single assembled nanostructures can be investigated by using microscopic technology combined with chiral optics. Here, we describe the state of the art for spectroscopic methods for the comprehensive analysis of chiral nanomaterials at various photon wavelengths, addressed with special attention given to new tools emerging both for materials with self-organized hierarchical chirality and single-particle spectroscopy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Free Energy and Flexibility Analysis of Autoinhibited Human BRAF

The RAF serine/threonine protein kinases function as direct effectors of RAS in the intracellular transmission of extracellular growth signals, and they are key targets for drug discovery, given the high incidence of oncogenic mutations in RAF and other components of this signaling pathway. In its inactive state, RAF is held in an autoinhibited conformation in the cytosol through a combination of intramolecular interactions and binding to a regulatory 14−3−3 protein dimer. Activation of RAF is initiated by its interaction with membrane-localized GTP-bound RAS, which induces conformational changes that release RAF from its autoinhibited state. However, the molecular mechanisms governing RAF activation remain incomplete, largely due to the challenges in experimentally capturing the intermediate conformational states in this process. To address this gap, we developed a comprehensive all-atom model of BRAF based on existing cryo-EM structures. Using this model, we performed extensive molecular dynamics simulations to evaluate the stability and free energy landscape of autoinhibited BRAF in solution. Our analysis reveals conformational flexibility within the autoinhibited complex, suggesting that this dynamic behavior may play a role in facilitating BRAF activation upon engagement with the membrane-bound RAS.

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

Atomistic Characterization of Gramicidin Channel Formation

We investigated gramicidin A (gA) subunit dimerization in lipid bilayers using microsecond-long replica-exchange umbrella sampling simulations, millisecond-long unbiased molecular dynamics simulations, and machine learning. Our simulations led to a dimer structure that is indistinguishable from the experimentally determined gA channel structures, with the two gA subunits joined by six hydrogen bonds (6HB). The simulations also uncovered two additional dimer structures, with different gA–gA stacking orientations that were stabilized by four or two hydrogen bonds (4HB or 2HB). When examining the temporal evolution of the dimerization, we found that two bilayer-inserted gA subunits can form the 6HB dimer directly, with no discernible intermediate states, as well as through paths that involve the 2HB and 4HB dimers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Prediction of Self-Diffusion in Binary Fluid Mixtures Using Artificial Neural Networks

Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for individual components in binary fluid mixtures. The ANNs were tested on an experimental database of 4328 self-diffusion constants from 131 mixtures containing 75 unique compounds. The presence of strong hydrogen bonding molecules may lead to clustering or dimerization resulting in non-linear diffusive behavior. To address this, self- and binary association energies were calculated for each molecule and mixture to provide information on intermolecular interaction strength and were used as input features to the ANN. An accurate, generalized ANN model was developed with an overall average absolute deviation of 4.1%. Forward input feature selection reveals the importance of critical properties and self-association energies along with other fluid properties. Additional ANNs were developed with subsets of the full input feature set to further investigate the impact of various properties on model performance. The results from two specific mixtures are discussed in additional detail: one providing an example of strong hydrogen bonding and the other an example of extreme pressure changes, with the ANN models predicting self-diffusion well in both cases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Database of Nonaqueous Proton-Conducting Materials

This work presents the assembly of 48 papers, representing 74 different compounds and blends, into a machine-readable database of nonaqueous proton-conducting materials. SMILES was used to encode the chemical structures of the molecules, and we tabulated the reported proton conductivity, proton diffusion coefficient, and material composition for a total of 3152 data points. The data spans a broad range of temperatures ranging from -70 to 260 °C. To explore this landscape of nonaqueous proton conductors, DFT was used to calculate the proton affinity of 18 unique proton carriers. The results were then compared to the activation energy derived from fitting experimental data to the Arrhenius equation. It was found that while the widely recognized positive correlation between the activation energy and proton affinity may hold among closely related molecules, this correlation does not necessarily apply across a broader range of molecules. This work serves as an example of the potential analyses that can be conducted using literature data combined with emerging research tools in computation and data science to address specific materials design problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗