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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 181 records · Page 10

Rheological Properties of Small-Molecular Liquids at High Shear Strain Rates

Molecular-scale understanding of rheological properties of small-molecular liquids and polymers is critical to optimizing their performance in practical applications such as lubrication and hydraulic fracking. We combine nonequilibrium molecular dynamics simulations with two unsupervised machine learning methods: principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), to extract the correlation between the rheological properties and molecular structure of squalane sheared at high strain rates (10 6 –10 10 s -1 ) for which substantial shear thinning is observed under pressures P ϵ 0.1–955 MPa at 293 K. Intramolecular atom pair orientation tensors of 435 × 6 dimensions and the intermolecular atom pair orientation tensors of 61 × 6 dimensions are reduced and visualized using PCA and t-SNE to assess the changes in the orientation order during the shear thinning of squalane. Dimension reduction of intramolecular orientation tensors at low pressures P = 0.1,100 MPa reveals a strong correlation between changes in strain rate and the orientation of the side-backbone atom pairs, end-backbone atom pairs, short backbone-backbone atom pairs, and long backbone-backbone atom pairs associated with a squalane molecule. At high pressures P ≥ 400 MPa, the orientation tensors are better classified by these different pair types rather than strain rate, signaling an overall limited evolution of intramolecular orientation with changes in strain rate. Dimension reduction also finds no clear evidence of the link between shear thinning at high pressures and changes in the intermolecular orientation. The alignment of squalane molecules is found to be saturated over the entire range of rates during which squalane exhibits substantial shear thinning at high pressures.

36 MATERIALS SCIENCE↗

Implementation of compound refractive lenses for large field-of-view x-ray phase-contrast imaging during hypervelocity impact experiments

Synchrotron x-ray phase-contrast imaging (XPCI) offers time-resolved visualization of dynamic compression phenomena, but its intrinsically small field-of-view (FOV) limits the time that key features remain in frame. A novel approach to enlarge the FOV is achieved by positioning a two-dimensional parabolic compound refractive lens (CRL) upstream of the sample to deliberately defocus the white beam. Ray-tracing simulations and XPCI measurements show that this CRL configuration can expand the beam by ∼50% vertically and ∼15% horizontally based on the full width at half-maximum of the beam. Implementing the CRL, however, attenuates the photon flux and lowers signal-to-noise ratio (SNR). Task-based analysis using a calibration grid (30 μm dots) showed that both setups fail to consistently meet the Rose criterion (SNR ≥ 5) for features of this size in single-bunch imaging. Extrapolating the measured SNR Rose values suggests that the minimum consistently detectable feature lies closer to 30–40 μm for the standard XPCI setup and above 40 μm for CRL-XPCI. Despite this limitation, the CRL configuration nearly doubles the illuminated area, enabling simultaneous tracking of front and rear observations of boron carbide targets subjected to rod and sphere impacts at 1.0–2.6 km/s. Image tracking algorithms and photonic Doppler velocimetry were used to measure penetration and rear-surface velocity histories. Together, these measurements capture crack fronts, penetration, and material breakout, offering new benchmark data for validating high-strain-rate constitutive models of ceramic materials.

Ceramic materials↗

Unsupervised anomaly detection in MeV ultrafast electron diffraction

MeV ultrafast electron diffraction (MUED) is a pump-probe technique used to study the dynamic structural evolution of materials. An ultrashort laser pulse triggers structural changes, which are then probed by an ultrashort relativistic electron beam. To overcome low signal-to-noise ratios, diffraction patterns are averaged over thousands of shots. However, shot-to-shot instabilities in the electron beam can distort individual patterns, introducing uncertainty. Improving MUED accuracy requires detecting and removing these anomalous patterns from large datasets. In this work, we developed a fully unsupervised methodology for the detection of anomalous diffraction patterns. Using a convolutional autoencoder, we calculate the reconstruction mean squared error of the diffraction patterns. Based on the statistical analysis of this error, we provide the user an estimation of the probability that the pattern is normal, which also allows a posterior visual inspection of the images that are difficult to classify. This method has been trained with only 100 diffraction patterns and tested on 1521 patterns, resulting in a false positive rate between 0.2% and 0.4%, with a training time of 10 s per image and a test time of about 1 s per image. Here, the proposed methodology can also be applied to other diffraction techniques in which large datasets are collected that include faulty images due to instrumental instabilities.

43 PARTICLE ACCELERATORS↗

Search for Infrasound Signals in InSight Data Using Coupled Pressure/Ground Deformation Methods

The unprecedented quality and sampling rate of seismometer and pressure sensors of the InSight Mars mission allow us to investigate infrasound through its pressure and ground deformation signals. This study focuses on compliance effects induced by acoustic waves propagating almost horizontally close to the surface. The compliance of acoustic waves is first estimated using the compliance estimates from pressure perturbations moving at wind speed. Then, a marker of compliance events is used to detect events of ground deformation induced by pressure variations, in three frequency bands from 0.4 to 3.2 Hz, from InSight sol 180 to 690. Additional selection criteria are imposed on the detected events to focus on acoustic waves and to remove various noise sources (e.g., wind effects or seismometer artifacts). After an automated selection, the visual inspection of the records allows us to validate two infrasound candidates that cannot be related to pressure perturbations moving at wind speed nor to known noise sources. For our highest quality infrasound candidate, the relation between this event and a convective vortex occuring 10 s later is tested. The azimuth of the vortex position at the time of infrasound detection is not consistent with the arrival azimuth of the suspected infrasound inferred from the polarization of seismometer records, thus the link between these two phenomena cannot be demonstrated. Further investigations would require a better understanding of wind-related noise impacting InSight sensors and of the effects of lateral variations of subsurface mechanical properties on the ground deformations induced by atmospheric pressure variations.

58 GEOSCIENCES↗

Visualizing plasmons and ultrafast kinetic instabilities in laser-driven solids using X-ray scattering

Ultra-intense lasers that ionize atoms and accelerate electrons in solids to near the speed of light can lead to kinetic instabilities that alter the laser absorption and subsequent electron transport, isochoric heating, and ion acceleration. These instabilities can be difficult to characterize, but X-ray scattering at keV photon energies allows for their visualization with femtosecond temporal resolution on the few nanometer mesoscale. Here, we perform such experiment on laser-driven flat silicon membranes that shows the development of structure with a dominant scale of 60 nm in the plane of the laser axis and laser polarization, and 95 nm in the vertical direction with a growth rate faster than 0.1 fs -1 . Combining the XFEL experiments with simulations provides a complete picture of the structural evolution of ultra-fast laser-induced plasma density development, indicating the excitation of plasmons and a filamentation instability. Particle-in-cell simulations confirm that these signals are due to an oblique two-stream filamentation instability. These findings provide new insight into ultra-fast instability and heating processes in solids under extreme conditions at the nanometer level with possible implications for laser particle acceleration, inertial confinement fusion, and laboratory astrophysics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

An LCO-responsive homolog of NODULE INCEPTION positively regulates lateral root formation in Populus sp.

Abstract The transcription factor NODULE INCEPTION (NIN) has been studied extensively for its multiple roles in root nodule symbiosis within plants of the nitrogen-fixing clade (NFC) that associate with soil bacteria, such as rhizobia and Frankia. However, NIN homologs are present in plants outside the NFC, suggesting a role in other developmental processes. Here, we show that the biofuel crop Populus sp., which is not part of the NFC, contains eight copies of NIN with diversified protein sequence and expression patterns. Lipo-chitooligosaccharides (LCOs) are produced by rhizobia and a wide range of fungi, including mycorrhizal ones, and act as symbiotic signals that promote lateral root formation. RNAseq analysis of Populus sp. treated with purified LCO showed induction of the PtNIN2 subfamily. Moreover, the expression of PtNIN2b correlated with the formation of lateral roots and was suppressed by cytokinin treatment. Constitutive expression of PtNIN2b overcame the inhibition of lateral root development by cytokinin under high nitrate conditions. Lateral root induction in response to LCOs likely represents an ancestral function of NIN retained and repurposed in nodulating plants, as we demonstrate that the role of NIN in LCO-induced root branching is conserved in both Populus sp. and legumes. We further established a visual marker of LCO perception in Populus sp. roots, the putative sulfotransferase PtSS1 that can be used to study symbiotic interactions with the bacterial and fungal symbionts of Populus sp.

Irving, Thomas B. (ORCID:0000000330404543)↗

Soot and PAH formation in high pressure spray pyrolysis of gasoline and diesel fuels

Time-resolved soot and PAH formation from gasoline and diesel spray pyrolysis are visualized and quantified using diffuse back illumination (DBI) and laser induced fluorescence (LIF) at 355 nm, respectively, in a constant-volume vessel at 60 bar from 1400 to 1700 K for up to 30 ms. The delay, maximum formation rate, and yield of soot and PAHs are compared across fuels and temperatures and correlated with the yield sooting indices on either the mass or mole basis. The delays generally decrease with increasing temperature, and the formation rates of both PAHs and soot generally increase with temperature. The apparent PAH-LIF yield may decrease with temperature due to PAH growth and conversion into larger species, signal trapping, and thermal quneching. Soot yield generally increases with temperature. The mass-based YSI correlates reasonably well with soot delay, but YSI does not correlate well with soot yield. Here, the mass-based YSI is a more appropriate predictor of sooting propensity than the mole-based YSI.

42 ENGINEERING↗

Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos, and cosmic rays cover a wide range of wavelengths and timescales. Combining and processing these datasets that vary in volume, speed, and dimensionality requires new modes of instrument coordination, funding, and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal-processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets has powered the rise of artificial intelligence. This digital revolution now powers a multibillion dollar industry, with far-reaching implications in technology and society. In this chapter, we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in multi-messenger astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017 to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and data.

Artificial Intelligence↗

Fluorescent, phosphorescent, magnetic resonance contrast and radioactive tracer labelling of extracellular vesicles

This review focusses on the significance of fluorescent, phosphorescent labelling and tracking of extracellular vesicles (EVs) for unravelling their biology, pathophysiology, and potential diagnostic and therapeutic uses. Various labeling strategies, such as lipid membrane, surface protein, luminal, nucleic acid, radionuclide, quantum dot labels, and metal complex-based stains, are evaluated for visualizing and characterizing EVs. Direct labelling with fluorescent lipophilic dyes is simple but generally lacks specificity, while surface protein labelling offers selectivity but may affect EV-cell interactions. Luminal and nucleic acid labelling strategies have their own advantages and challenges. Here, each labelling approach has strengths and weaknesses, which require a suitable probe and technique based on research goals, but new tetranuclear polypyridylruthenium(II) complexes as phosphorescent probes have strong phosphorescence, selective staining, and stability. Future research should prioritize the design of novel fluorescent probes and labelling platforms that can significantly enhance the efficiency, accuracy, and specificity of EV labeling, while preserving their composition and functionality. It is crucial to reduce false positive signals and explore the potential of multimodal imaging techniques to gain comprehensive insights into EVs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Processes in DNA damage response from a whole-cell multi-omics perspective

Technological advances have made it feasible to collect multi-condition multi-omic time courses of cellular response to perturbation, but the complexity of these datasets impedes discovery due to challenges in data management, analysis, visualization, and interpretation. Here, we report a whole-cell mechanistic analysis of HL-60 cellular response to bendamustine. We integrate both enrichment and network analysis to show the progression of DNA damage and programmed cell death over time in molecular, pathway, and process-level detail using an interactive analysis framework for multi-omics data. Our framework, Mechanism of Action Generator Involving Network analysis (MAGINE), automates network construction and enrichment analysis across multiple samples and platforms, which can be integrated into our annotated gene-set network to combine the strengths of networks and ontology-driven analysis. Taken together, our work demonstrates how multi-omics integration can be used to explore signaling processes at various resolutions and demonstrates multi-pathway involvement beyond the canonical bendamustine mechanism.

59 BASIC BIOLOGICAL SCIENCES↗

Stroboscopic ultrafast imaging using RF strip-lines in a commercial transmission electron microscope

The development of ultrafast electron microscopy (UEM), specifically stroboscopic imaging, has brought the study of structural dynamics to a new level by overcoming the spatial limitations of ultrafast spectroscopy and the temporal restrictions of traditional TEM simultaneously. Combining the concepts governing both techniques has enabled direct visualization of dynamics with spatiotemporal resolutions in the picosecond-nanometer regime. Here, in this study, we push the limits of imaging using a pulsed electron beam via RF induced transverse deflection based on the newly developed 200 keV frequency-tunable strip-line pulser. We demonstrate 0.2 nm spatial resolution at high magnifications and elucidation of Lorentz imaging using the phase-microscopy method. We also present beam coherence measurements and expand our study using the breathing modes of a silicon interdigitated comb under RF excitation which achieves improved temporal synchronization between the electron pulse-train and electric field. A new RF holder has also been developed with impedance matching to the RF signal to minimize transmission power loss to samples and its performance is compared with a conventional sample holder.

47 OTHER INSTRUMENTATION↗

NIRDye 812: A molecular platform tailored for multimodal bioimaging applications of targeted fluorescence- and photoacoustic-guided surgery

The primary treatment for malignant tumors remains to be surgical removal of the diseased tissue. The presence or absence of residual diseased tissue at the tumor margin is the strongest predictor of postoperative prognosis and recurrence. Accordingly, reliance on the ability of surgeons to visually distinguish diseased tissue from healthy tissue unambiguously in real time is crucial. Near infrared-I (NIRsingle bondI) fluorescence-emitting targeting biomolecular constructs such as anticancer antibody-fluorophore conjugates, namely cetuximab-IRDye® 800CW (CTB-IRDye® 800CW), are FDA-approved for clinical trial usage in the fluorescence-guided resection of diseased tissue due to affording improved direct visualization of tumor tissue when compared to the use of either the unaided eye under standard white light illumination (WLI) surgical techniques or non-targeting fluorophores. Unfortunately, though helpful, CTB-IRDye® 800CW affords limited (i) identification of diseased tissue and (ii) tumor margin delineation, because the immunoconjugate generates suboptimal tumor-to-background ratios (TBRs) as a result of its spectral/photophysical profiles poorly aligning with the fixed optical windows of pre-/clinical setups. As such, CTB-IRDye® 800CW is more prone to affording incomplete resection compared to if TBRs were higher due to otherwise. Here, to aid in accurately identifying deep-seated diseased tissue, photoacoustic (PA) tomography has been implemented alongside CTB-IRDye® 800CW to achieve PA signals that could result in higher TBRs. However, in clinical trial practice, using IRDye® 800CW for PA imaging also yields subpar TBRs due to it affording low PA signals. To overcome such limitations, we developed NIRDye 812, a structurally-modified topological equivalent of IRDye® 800CW, to confer it the capability to yield both higher TBRs and superior PA signal than that of the equivalent CTB-conjugate and fluorophore IRDye® 800CW itself, respectively. To do so, we substituted the oxygen atom at its meso-position with a sulfur atom. CTB-NIRDye 812 demonstrated a red-shifted absorption wavelength at 796 nm and a peak NIR-I fluorescence emission wavelength at 820 nm, which better dovetails with the fixed windows of preinstalled fixed emission filters within commercial pre-/clinical NIR-I fluorescence imaging instruments. Overall, CTB-NIRDye 812 provided a ~ 2-fold increase in TBRs compared to those of CTB-IRDye® 800CW in vivo. Also, NIRDye 812 displayed an ~60% higher PA signal than that of IRDye® 800CW. Collectively, we achieved our goal of improving upon the spectral/photophysical and PA properties of IRDye® 800CW via introducing a subtle modification to its electronic core such that its CTB immunoconjugate could potentially allow for fast track or breakthrough designation by the FDA due to its near-identical structure displaying considerably improved efficacy.

59 BASIC BIOLOGICAL SCIENCES↗

Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data

We present an automatic method based on machine-learning convolutional neural network (CNN) architecture to detect Lyman alpha emitters (LAE) hidden in the Data Release 1 spectroscopic dataset of the Dark Energy Spectroscopic Instrument (DESI). Those LAEs mostly have incorrect redshift estimations because the current DESI pipeline is not designed to detect and measure the redshifts of galaxies at $z>2$. To uncover those sources, we first visually inspect thousands of DESI spectra and construct a sample, consisting of both LAEs and non-LAEs, for training and testing the CNN-based model to (1) detect LAEs in DESI spectra and (2) determine their Ly$α$ redshifts. The final model yields $95.2\%$ purity and $95.9\%$ completeness for detecting LAEs. We apply this model to approximately $2\times10^{6}$ spectra of sources targeted as emission-line galaxies and detect 19,685 LAEs from $z\sim2$ to $3.5$ within 12 minutes with a single GPU, illustrating the high efficiency of this model for identifying LAEs. The detected LAEs are mostly at the bright end of the luminosity function with Ly$α$ luminosity $L_{\rm Lyα} \gtrsim 10^{43}$ erg/s. The high signal-to-noise composite spectrum of the detected LAEs further shows various spectral features, including P-Cygni profiles of metal lines and MgII emission lines, possible indicators of Lyman continuum escape fraction, revealing the rich astrophysical information in this LAE sample. Finally, this sample can be used to train and validate the pipelines for redshift determination of LAEs for the preparation of the DESI-II survey.

Chan, Jui-Kuan [Taiwan, Natl. Taiwan U.] (ORCID:00↗

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗

Inactivation of Fluorescent Lipid Bilayers by Irradiation With 300 keV Electrons Using Liquid Cell Transmission Electron Microscopy

Liquid cell transmission electron microscopy allows for imaging of samples in a fully hydrated state at high resolution and has the potential for visualizing static or dynamic biological structures. However, the ionizing nature of the electron beam makes it difficult to discern real physiological dynamics from radiation induced artifacts within liquid cell samples. Electron flux thresholds for achieving high resolution structures from biological samples frozen in ice have been described extensively by the cryo-electron microscopy field, while electron flux thresholds which do not result in a functional change for biological samples within the hydrated environment of a transmission electron microscope liquid cell is less clear. Establishing these functional thresholds for biologically relevant samples is important for accurate interpretation of results from liquid cell experiments. Here we demonstrate the electron damage threshold of fluorescently tagged lipid bilayers by quantifying the change in fluorescence before and after electron exposure. We observe the reduction of fluorescent signal in bilayers by 25% after only 0.0005 e − /Å 2 and a reduction of over 90% after 0.01 e − /Å 2 . These results indicate that the loss of function occurs at irradiation thresholds far below a typical single high resolution (scanning) transmission electron microscopy image and orders of magnitude below fluxes used for preserving structural features with cryo-electron microscopy.

Moser, Trevor↗

Sparse Symmetric Format for Tucker Decomposition

Tensor-based methods are receiving renewed attention in recent years due to their prevalence in diverse real-world applications. There is considerable literature on tensor representations and algorithms for tensor decompositions, both for dense and sparse tensors. Many applications in hypergraph analytics, machine learning, psychometry, and signal processing result in tensors that are both sparse and symmetric, making them an important class for further study. Similar to the critical Tensor Times Matrix chain operation (TTM c ) in general sparse tensors, the $\underline{S}$ parse $\underline{S}$ ymmetric $\underline{T}$ ensor $\underline{T}$ imes $\underline{S}$ ame $\underline{M}$ atrix $\underline{c}$ hain (S 3 TTM c ) operation is compute and memory intensive due to high tensor order and the associated factorial explosion in the number of non-zeros. We present the novel Compressed Sparse Symmetric (CSS) format for sparse symmetric tensors, along with an efficient parallel algorithm for the S 3 TTM c operation. We theoretically establish that S 3 TTM c on CSS achieves a better memory versus run-time trade-off compared to state-of-the-art implementations, and visualize the variation of the performance gap over the parameter space. We demonstrate experimental findings that confirm these results and achieve up to 2.72× speedup on synthetic and real datasets. The scaling of the algorithm on different test architectures is also showcased to highlight the effect of machine characteristics on algorithm performance.

42 ENGINEERING↗

A Bright, Photostable, and Far-Red Dye That Enables Multicolor, Time-Lapse, and Super-Resolution Imaging of Acidic Organelles

Lysosomes have long been known for their acidic lumens and efficient degradation of cellular byproducts. In recent years, it has become clear that their function is far more sophisticated, involving multiple cell signaling pathways and interactions with other organelles. Unfortunately, their acidic interior, fast dynamics, and small size make lysosomes difficult to image with fluorescence microscopy. Here we report a far-red small molecule, HMSiR 680 -Me, that fluoresces only under acidic conditions, causing selective labeling of acidic organelles in live cells. HMSiR 680 -Me can be used alongside other far-red dyes in multicolor imaging experiments and is superior to existing lysosome probes in terms of photostability and maintaining cell health and lysosome motility. We demonstrate that HMSiR 680 -Me is compatible with overnight time-lapse experiments as well as time-lapse super-resolution microscopy with a frame rate of 1.5 fps for at least 1000 frames. HMSiR 680 -Me can also be used alongside silicon rhodamine dyes in a multiplexed super-resolution microscopy experiment to visualize interactions between mitochondria and lysosomes with only a single excitation laser and simultaneous depletion. We envision this dye permitting a more detailed study of the role of lysosomes in dynamic cellular processes and disease.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Dynamic compressed sensing for real-time tomographic reconstruction

Electron tomography has achieved higher resolution and quality at reduced doses with recent advances in compressed sensing. Compressed sensing (CS) exploits the inherent sparse signal structure to efficiently reconstruct three-dimensional (3D) volumes at the nanoscale from undersampled measurements. However, the process bottlenecks 3D reconstruction with computation times that run from hours to days. Here we demonstrate a framework for dynamic compressed sensing that produces a 3D specimen structure that updates in real-time as new specimen projections are collected. Researchers can begin interpreting 3D specimens as data is collected to facilitate high-throughput and interactive analysis. Using scanning transmission electron microscopy (STEM), we show that dynamic compressed sensing accelerates the convergence speed by ~3-fold while also reducing its error by 27% for a Au/SrTiO 3 nanoparticle specimen. Before a tomography experiment is completed, the 3D tomogram has interpretable structure within ~33% of completion and fine details are visible as early as ~66%. Upon completion of an experiment, a high-fidelity 3D visualization is produced without further delay. Additionally, we report reconstruction parameters that tune data fidelity can be manipulated throughout the computation without re-running the entire process.

47 OTHER INSTRUMENTATION↗