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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 OSTI2021

REMBI: Recommended Metadata for Biological Images—enabling reuse of microscopy data in biology

Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. Here, we propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. We hope this publication and the proposed Recommended Metadata for Biological Images (REMBI) will stimulate discussions about their implementation and future extension.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy for rapid, label-free biological imaging

Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy is a label-free imaging approach that provides detailed chemical information at high spatial resolution in a sample through nonlinear, coherent excitation of molecular vibrations and detection of Raman spectra. While its utility for biological imaging has been demonstrated, many aspects of this technique must mature before it can be widely adopted. One of the areas of required improvement is imaging speed—most BCARS implementations involve sample rastering, which limits imaging speed. Beam scanning can provide faster BCARS imaging but presents some unique challenges. Here, we describe a beam-scanning BCARS microscopy system that improves spatial resolution twofold and imaging speed by fivefold over a previous beam-scanning implementation. These enhancements were enabled by an improvement in supercontinuum power and the use of a sCMOS camera for its high data transfer rate and low read noise. Implementation of the sCMOS camera required correction for the significant pixel-to-pixel background and photon response nonuniformity. Here, we report on the method that we implemented for calibrating and correcting the pixel-to-pixel differences in sCMOS camera noise.

Dixon, Jessica Z. [Georgia Institute of Technology↗
DOE OSTI2020

Photocaged functional nucleic acids for spatiotemporal imaging in biology

Imaging of species in living organisms with high spatiotemporal resolution is essential for understanding biological processes. While functional nucleic acids (FNAs), such as catalytic nucleic acids and aptamers, have emerged as effective sensors for a wide range of molecules, photocaged control of these FNAs has played a key role in translating them into bioimaging agents with high spatiotemporal control. In this paper, we summarize methods and results of photocaged FNAs based on photolabile modifications, photoisomerization, and photothermal activation. Future directions, including strategies to improve the performance of these photocaged FNAs, are also described.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images

Images are increasingly becoming the currency for documenting biodiversity on the planet, providing novel opportunities for accelerating scientific discoveries in the field of organismal biology, especially with the advent of large vision-language models (VLMs). We ask if pre-trained VLMs can aid scientists in answering a range of biologically relevant questions without any additional fine-tuning. In this paper, we evaluate the effectiveness of 12 state-of-the-art (SOTA) VLMs in the field of organismal biology using a novel dataset, VLM4Bio, consisting of 469K question8 answer pairs involving 30K images from three groups of organisms: fishes, birds, and butterflies, covering five biologically relevant tasks. We also explore the effects of applying prompting techniques and tests for reasoning hallucination on the performance of VLMs, shedding new light on the capabilities of current SOTA VLMs in answering biologically relevant questions using images

Maruf, M [Virginia Tech, Blacksburg]↗
DOE OSTI2020

In Liquid Infrared Scattering Scanning Near-Field Optical Microscopy for Chemical and Biological Nanoimaging

Imaging biological systems with simultaneous intrinsic chemical specificity and nanometer spatial resolution in their typical native liquid environment has remained a long-standing challenge. In this paper we demonstrate a general approach of chemical nanoimaging in liquid based on infrared scattering scanning near-field optical microscopy (IR $s$-SNOM). It is enabled by combining AFM operation in a fluid cell with evanescent IR illumination via total internal reflection, which provides spatially confined excitation for minimized IR water absorption, reduced far-field background, and enhanced directional signal emission and sensitivity. We demonstrate in-liquid IR $s$-SNOM vibrational nanoimaging and conformational identification of catalase nano-crystals and spatio-spectral analysis of biomimetic peptoid sheets with monolayer sensitivity and chemical specificity at the few zeptomole level. This work establishes the principles of in-liquid and in-situ IR $s$-SNOM spectroscopic chemical nano-imaging and its general applicability to biomolecular, cellular, catalytic, electrochemical, or other interfaces and nano-systems in liquids or solutions.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Toward implementing autonomous adaptive data acquisition for scanning hyperspectral imaging of biological systems

Autonomous experimentation is an emerging area of research, primarily related to autonomous vehicles, scientific combinatorial discovery approaches in materials science and drug discovery, and iterative research loops of planning, experimentation, and analysis. However, autonomous approaches developed in these contexts are difficult to apply to high-dimensional mapping technologies, such as scanning hyperspectral imaging of biological systems, due to sample complexity and heterogeneity. We briefly cover the history of adaptive sampling algorithms and surrogate modeling in order to define autonomous adaptive data acquisition as an objective-based, flexible building block for future biological imaging experimentation driven by intelligent infrastructure. We subsequently summarize the recent implementations of autonomous adaptive data acquisition (AADA) for scanning hyperspectral imaging, assess how these address the difficulties of autonomous approaches in hyperspectral imaging, and highlight the AADA design variation from a goal-oriented perspective. Finally, we present a modular AADA architecture that embeds AADA-driven flexible building blocks to address the challenge of time resolution for high-dimensional scanning hyperspectral imaging of nonequilibrium dynamical systems. In our example research-driven experimental design case, we propose an AADA infrastructure for time-resolved, noninvasive, and label-free scanning hyperspectral imaging of living biological systems. This AADA infrastructure can accurately target the correct state of the system for experimental workflows that utilize subsequent expensive, high-information-content analytical techniques.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Three-Dimensional Mass Spectrometric Imaging of Biological Structures Using a Vacuum-Compatible Microfluidic Device

Three-dimensional (3D) molecular imaging of biological structures is important for a wide range of research. In recent decades, secondary ion mass spectrometry (SIMS) has been recognized as a powerful technique for both two-dimensional (2D) and 3D molecular imaging. Sample fixations (e. g., chemical fixation and cryogenic fixation methods) are necessary to adapt biological samples to the vacuum condition in the SIMS chamber, which has been demonstrated to be non-trivial and less controllable, thus limiting the wider application of SIMS on 3D molecular analysis of biological samples. Our group recently developed in situ liquid SIMS that offers great opportunities for the molecular study of various liquids and liquid interfaces. In this work, we demonstrate that a further development of the vacuum-compatible microfluidic device used in in situ liquid SIMS provides a convenient freeze-fixation of biological samples and leads to more controllable and convenient 3D molecular imaging. The special design of this new vacuum-compatible liquid chamber allows an easy determination of sputter rates of ice, which is critical for calibrating the depth scale of frozen biological samples. Sputter yield of a 20 keV Ar 1800 + ion on ice has been determined as 1500 (± 8%) water molecules per Ar 1800 + ion, consistent with our results from molecular dynamics simulations. Moreover, using the information of ice sputter yield, we successfully conduct 3D molecular imaging of frozen homogenized milk and observe network structures of interesting organic and inorganic species. Finally, taken together, our results will significantly benefit various research fields relying on 3D molecular imaging of biological structures.

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

Cryogenic electron tomography by the numbers: Charting underexplored lineages in structural cell biology

Imaging cells and their interactions across the whole biosphere with molecular-scale resolution is key for understanding structure–function relations. Cryogenic electron tomography (cryo-ET) is a powerful method for obtaining this critical information. However, cryo-ET studies are challenging and often limited to a small number of cell types per study. Here, we collate cryo-ET data from hundreds of cells and tissues across the biosphere to i) identify emerging methodological trends, ii) pinpoint strategies to reduce imaging time and costs, iii) quantitatively compare methods for cell freezing and sectioning, and iv) census cryo-ET species coverage across all domains of life. Comparing the fraction of cellular material within a single lamella across all domains of life reveals an order of magnitude difference between eukaryotes (1%) compared to bacteria (9%) and archaea (14%). We calculate the fraction of cellular material which can be imaged using distinct sectioning methods on multicellular communities and tissues—identifying serial lift-out as a powerful approach for obtaining more complete cellular depictions. Finally, we show that the biodiversity of current cryo-ET studies is 2 to 3 orders of magnitude lower than in sequence libraries and 4 to 5 lower than the total predicted on Earth. Our analyses reveal major evolutionary lineages which remain critically understudied and highlight where future cryo-ET research would be most impactful.

HPF↗
DOE OSTI2024

Monte Carlo Simulation of Electron Interactions in an MeV-STEM for Thick Frozen Biological Sample Imaging

A variety of volume electron microscopy techniques have been developed to visualize thick biological samples. However, the resolution is limited by the sliced section thickness (>30–60 nm). To preserve biological samples in a hydrated state, cryo-focused ion beam scanning electron microscopy has been developed, providing nm resolutions. However, this method is time-consuming, requiring 15–20 h to image a 10 μm thick sample with an 8 nm slice thickness. There is a pressing need for a method that allows the rapid and efficient study of thick biological samples while maintaining nanoscale resolution. The remarkable ability of mega-electron-volt (MeV) electrons to penetrate thick biological samples, even exceeding 10 μm in thickness, while maintaining nanoscale resolution, positions MeV-STEM as a suitable microscopy tool for such applications. Our research delves into understanding the interactions between MeV electrons and frozen biological specimens through Monte Carlo simulations. Single elastic scattering, plural elastic scattering, single inelastic scattering, and plural inelastic scattering events have been simulated. The electron trajectories, the beam profile, and the intensity change of electrons in each category have been investigated. Additionally, the effects of the detector collection angle and the focal position of the electron beam were investigated. As electrons penetrated deeper into the specimen, single and plural elastic scattered electrons diminished, and plural inelastic scattered electrons became dominant, and the beam profile became wider. Even after 10 μm of the specimen, 42% of the MeV electrons were collected within 10 mrad. This confirms that MeV-STEM can be employed to study thick biological samples.

electron specimen interaction↗
DOE OSTI2023

Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time

Abstract Noninvasive X-ray imaging of nanoscale three-dimensional objects, such as integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of the complex electromagnetic field through the IC; combined with a tomographic scan, which collects these complex field projections from multiple angles. Here, we present Attentional Ptycho-Tomography (APT), an approach to drastically reduce the amount of angular scanning, and thus the total acquisition time. APT is machine learning-based, utilizing axial self-Attention for Ptycho-Tomographic reconstruction. APT is trained to obtain accurate reconstructions of the ICs, despite the incompleteness of the measurements. The training process includes regularizing priors in the form of typical patterns found in IC interiors, and the physics of X-ray propagation through the IC. We show that APT with ×12 reduced angles achieves fidelity comparable to the gold standard Simultaneous Algebraic Reconstruction Technique (SART) with the original set of angles. When using the same set of reduced angles, then APT also outperforms Filtered Back Projection (FBP), Simultaneous Iterative Reconstruction Technique (SIRT) and SART. The time needed to compute the reconstruction is also reduced, because the trained neural network is a forward operation, unlike the iterative nature of these alternatives. Our experiments show that, without loss in quality, for a 4.48 × 93.2 × 3.92 µm 3 IC (≃6 × 10 8 voxels), APT reduces the total data acquisition and computation time from 67.96 h to 38 min. We expect our physics-assisted and attention-utilizing machine learning framework to be applicable to other branches of nanoscale imaging, including materials science and biological imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Use of computer vision for analysis of image datasets from high temperature plasma experiments

Great strides have been made in improving the quality of x-ray radiographs in high energy density plasma experiments, enabled in part by innovations in engineering and manufacturing of integrated circuits and materials. As a consequence, the radiographs of today are filled with a great deal of detail, but few of these features are extracted in a systematic way. Analysis techniques familiar to plasma physicists tend toward brittle 1D lineout or Fourier transform type analyses. The techniques applied to process our data have not kept pace with improvements in the quality of our data. Fortunately, the field of computer vision has a wealth of tools to offer, which have been widely used in industrial imaging and, more recently, adopted in biological imaging. We demonstrate the application of computer vision techniques to the analysis of x-ray radiographs from high energy density plasma experiments, as well as give a brief tutorial on the computer vision techniques themselves. These tools robustly extract 2D contours of shocks, boundaries of inhomogeneities, and secondary flows, thereby allowing for increased automation of analysis, as well as direct and quantitative comparisons with simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Above the Energy Gap Law: Heavy Chalcogenide Substitution in NIR II-Emissive Diradicaloid Qubits

Near-infrared (NIR, 700–1700 nm)- and telecom (∼1260–1625 nm)-emissive molecules are good candidates for biological imaging and quantum sensing applications, respectively; however, bright low energy emission is rare due to exponentially increasing nonradiative decay rates in these regions, a phenomenon known as the energy gap law. Recent literature has emphasized the importance of minimizing skeletal modes to prevent increased nonradiative decay rates, but most organic lumiphores in these regions utilize large, conjugated scaffolds containing many C═C modes. Here we report a compact, telecom-emissive scaffold, tetrathiafulvalene-2,3,6,7-tetraselenate, or TTFts, that displays remarkable air, water, and acid stability, exhibits record quantum yields and brightness values, and retains quantum coherence under ambient conditions. These properties are enabled through methodical selenium substitution, which bathochromically shifts emission while shifting skeletal vibrations to lower energies. This new scaffold validates heavy heteroatom substitution strategies and establishes a new class of bright telecom emitters and robust qubits.

biological imaging↗
DOE OSTI2025

Quantitative Modeling of High-Energy Electron Scattering in Thick Samples Using Monte Carlo Techniques

Cryo-electron microscopy (cryo-EM) is a powerful tool for imaging biological samples but is typically limited by sample thickness, which is restricted to a few hundred nanometers depending on the electron energy. However, there is a growing need for imaging techniques capable of studying biological samples up to 10 µm in thickness while maintaining nanoscale resolution. This need motivates the use of mega-electron-volt scanning transmission electron microscopy (MeV-STEM), which leverages the high penetration power of MeV electrons to generate high-resolution images of thicker samples. In this study, we employ Monte Carlo simulations to model electron–sample interactions and explore the signal decay of imaging electrons through thick specimens. By incorporating material properties, interaction cross-sections for energy loss, and experimental parameters, we investigate the relationship between the incident and transmitted beam intensities. Key factors such as detector collection angle, convergence semi-angle, and the material properties of samples were analyzed. Our results demonstrate that the relationship between incident and transmitted beam intensities follows the Beer–Lambert law over thicknesses ranging from a few microns to several tens of microns, depending on material composition, electron energy, and collection angles. The linear depth of silicon dioxide reaches 3.9 µm at 3 MeV, about 6 times higher than that at 300 keV. Meanwhile, the linear depth of amorphous ice reaches 17.9 µm at 3 MeV, approximately 11.5 times higher than that at 300 keV. These findings are crucial for advancing the study of thick biological and semiconductor samples using MeV-STEM.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Deep Learning Prediction of Interspecies Interactions from Self-organized Spatiotemporal Patterns of Co-evolving Organisms

Microorganisms colonizing natural habits such as soils co-evolve to form specific spatial patterns through interspecies interactions. These self-organized patterns are a key ecological phenotype, which provides critical information on their interaction mechanisms. However, conventional network inference techniques that analyze species population data in bulk have yet to be extended to account for such spatial heterogeneity. Here we proposed supervised deep learning as a new network inference tool for predicting interspecies interactions from spatiotemporal patterns of microbial evolution. Due to lack of biological imaging data that can be used for training deep learning networks, we used in silico data generated from high-fidelity agent-based models to determine model structure and parameters. Even though networks were trained under simple configurations where interaction coefficients are assumed to be spatially invariant, we demonstrated that the resulting model can be utilized to successfully predict spatial variation of interactions in more complex domains (i.e., configured with a context-dependent mixture of interaction coefficients) as well as in simple domains without further training. In the further test against real biological data obtained through imaging experiments of a binary consortium (Pseudomonas fluorescens and a mutant of Escherichia coli), our model also predicted the dramatic shifts in interactions of the two organisms across different environmental contexts. Through various successful demonstrations in this work, the combined use of the agent-based model and machine learning algorithm provides a means to use new type of data - microscopic images - for extracting microbial interactions, therefore presenting itself as a useful tool for the analysis of more complex microbial community interactions.

Lee, Joon-Yong↗
DOE OSTI2021

The Role of Quantum Science Concepts in Enhancing Sensing and Imaging Technologies: Applications for Biology: Proceedings of a Workshop

Quantum concepts hold the potential to enable significant advances in sensing and imaging technologies that could be vital to the study of biological systems. The workshop Quantum Science Concepts in Enhancing Sensing and Imaging Technologies: Applications for Biology, held online March 8–10, 2021, was organized to examine the research and development needs to advance biological applications of quantum technology. Hosted by the National Academies of Sciences, Engineering, and Medicine, the event brought together experts working on state-of-the-art, quantum-enabled technologies and scientists who are interested in applying these technologies to biological systems. Through talks, panels, and discussions, the workshop facilitated a better understanding of the current and future biological applications of quantum-enabled technologies in fields such as microbiology, molecular biology, cell biology, plant science, mycology, and many others. The workshop was organized around three main themes. The first, quantum in biology, examined quantum concepts that are hypothesized to be important for life processes and that researchers are working to observe through biological imaging and sensing. The second, quantum for biology, addressed ways to use quantum concepts to enhance technologies for biological imaging and sensing. The third, biology for quantum, offered a wider discussion of how the frontiers of biological imaging and sensing could enable future study using quantum concepts, tools, or technologies. Throughout the workshop, participants identified a wide range of emerging approaches and opportunities at the intersection of quantum physics and biological sensing and imaging. During the workshop, there were some differences in how each speaker defined the term quantum. During one of the panels, Prem Kumar offered thoughts on what phenomena are classical versus quantum, explaining that techniques get progressively more quantum as you move from just having superposition to having superposition with measurement and entanglement. Another explanation from Clarice Aiello delineates the definition into several levels. This includes a base level of “quantum-ness,” which reflects that all matter is made of atoms, and when these particles are isolated they behave based on quantum mechanical principles. A second level is related to quantum coherence, where a single quantum object might be found in a coherent superposition state. A final level, which she described as the quantum-entangled level, involves multiple quantum systems which are entangled among themselves. Overall, the workshop touched on concepts such as superposition, entanglement, and squeezing and their potential implications for communication, computing, and simulation, in addition to the workshop’s main focal area, biological sensing and imaging. At the opening of the workshop, Thorsten Ritz of the University of California, Irvine, identified two questions at the heart of quantum biology: Is the machinery of life quantum mechanical, and can quantum mechanics be used to study the machinery of life in new ways? Participants highlighted systems in which researchers have explored these questions, from the vast array of molecular interactions involved in biological processes such as photosynthesis, to the mechanics involved in cellular functions such as differentiation and aggregation, to the role of oscillating magnetic fields in flight orientation among birds. Sensing and imaging technologies are crucial to biological research; these technologies could both enhance the study of quantum effects and be enhanced by quantum concepts. A critical challenge in biological research is to develop imaging and sensing tools that do not damage or interfere with the often fragile and fleeting systems being studied. Attendees discussed a variety of established and emerging technologies that could enhance noninvasive biological imaging, including single- and two-photon spectroscopy, single-molecule spectroscopy, quantum illumination, ghost imaging, and cryo-electron microscopy. One example came from Marlan Scully who gave a keynote address on the first day of the workshop. Scully emphasized the use of different laser technologies, which exhibit coherence and other quantum properties, in moving toward real-world biological applications, such as the detection of SARS-CoV-2. Both tools and theory will play an important role in advancing quantum biology research and applications. Several participants suggested theorists and experimentalists should work in tandem to understand and model biological processes. While physics often reduces systems to their simplest forms for fundamental insights, participants also noted the value of observing and understanding biological systems in all their “messiness,” capturing both the inner workings of biological systems and the complex interactions that occur within and between organisms. In discussions among participants, several attendees stressed the need to match emerging tools with the right scientific questions. Rather than developing quantum technologies as “a hammer looking for a nail,” participants emphasized a focus on exploring the problems these technologies are best suited to address. For example, it is important to consider the size of the phenomenon being studied, the timescales that are important in answering the scientific question, and other relevant considerations. Every tool along the spectrum from classical to quantum involves its own set of trade-offs. For example, Ted Laurence of the Lawrence Livermore National Laboratory said that quantum measurements, such as single photon counting, fluorescence transitions, and lasers, can take longer to produce the same results as classical measurements. These quantum measurements, however, do not require calibration and can enable new research questions to be answered. Prem Kumar, Northwestern University, noted that, despite their promise, quantum approaches should not be used simply for the sake of using quantum, especially in situations where classical approaches better meet the needs of the researcher. Understanding and applying quantum concepts could enable advances in a wide range of application areas including energy, synthetic biology, medicine, and sustainability. For example, Michelle O’Malley, University of California, Santa Barbara, described how improved noninvasive imaging approaches could help capture the complex interactions and functions involved in the breakdown of organic matter by microbial communities and lead to new technologies for capturing valuable products from plant waste. Several other participants discussed needs in tracking the movement of metabolites and molecules in microbial communities for insights into nutrient cycling in environments such as soil. Margaret Ahmad, Sorbonne University, discussed potential opportunities to leverage the magnetic properties of cryptochromes to advance new treatment approaches for diseases such as COVID-19 and cancer. Looking toward the future development of the field, participants discussed challenges to advancing quantum biology that arise from disciplinary disconnects between physicists and biologists. The siloing of academic research disciplines represents a significant barrier to progress. Disconnects in terminology, motivations and priorities, and structural barriers to collaborative work underscore the need for concerted efforts to bridge these divides. Attendees and speakers offered suggestions for resolving these divergences, establishing a shared language, moving the field forward, and fostering meaningful feedback between disciplines. Overall, participants stressed a need for balance, open communication, collaboration, unity, and clear dialogue on trade-offs between quantum and classical approaches. Keiko Torii, The University of Texas at Austin, said that the best collaborations happen when the project provides mutual advantages that can show off everyone’s talents, each team finds the work interesting, and partners develop a camaraderie to pursue new knowledge. To enable near- and long-term opportunities in this space, participants suggested that exploratory, high-risk funding could improve existing instrumentation to explore quantum enhancement collaboratively. They also emphasized the need for collaboration between quantum physicists and sensing/imaging scientists, which could be advanced through a dedicated quantum biology investigator program. People, even more than technology, will be crucial to the future of quantum biology. Participants explored training, education, and workforce needs to further develop this burgeoning field and cultivate the next generation of scientists. While many programs are still in their nascent stages, participants highlighted examples of approaches and programs being developed at various types of institutions to engage students and professional scientists in quantum biology research. Several participants stressed the need for an inclusive approach, spanning disciplines as well as communities to foster a diverse field fueled by the intellectual contributions of a wide range of people, including historically under-resourced schools and students. To increase awareness and excitement about quantum physics and related areas of biology, attendees suggested capitalizing on the “buzz” around quantum. Several participants emphasized the need to start early, introducing students to quantum concepts and their appealing “weirdness” in K–12 education. Engaging students early—before they become entrenched in traditional disciplinary siloes as typically happens in graduate school—could help to galvanize interest in the area and foster a generation of scientists with the interdisciplinary mindset and skills needed to advance this interdisciplinary field. While these efforts could be advanced at many levels and across multiple sectors, several participants suggested a national quantum biology center could be a valuable hub to coordinate and support quantum biology education and workforce development across academia, industry, and government.

59 BASIC BIOLOGICAL SCIENCES↗