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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Attractor comparisons based on density

Recognizing a chaotic attractor can be seen as a problem in pattern recognition. Some feature vector must be extracted from the attractor and used to compare to other attractors. The field of machine learning has many methods for extracting feature vectors, including clustering methods, decision trees, support vector machines, and many others. In this work, feature vectors are created by representing the attractor as a density in phase space and creating polynomials based on this density. Density is useful in itself because it is a one dimensional function of phase space position, but representing an attractor as a density is also a way to reduce the size of a large data set before analyzing it with graph theory methods, which can be computationally intensive. The density computation in this paper is also fast to execute. In this paper, as a demonstration of the usefulness of density, the density is used directly to construct phase space polynomials for comparing attractors. Comparisons between attractors could be useful for tracking changes in an experiment when the underlying equations are too complicated for vector field modeling.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Disordered glycometabolism involved in pathogenesis of Kashin–Beck disease, an endemic osteoarthritis in China

Kashin–Beck disease (KBD) is a chronic endemic osteoarthritis in China. Previous studies have suggested a role of metabolic dysfunction in causation of this disease. In this investigation, the metabolomics approach and cell experiments were used to discover the metabolic changes and their effects on KBD chondrocytes. Nuclear magnetic resonance ({sup 1}H NMR) spectroscopy was used to examine serum samples from both the KBD patients and normal controls. The pattern recognition multivariate analysis (OSC–PLS) and quantitative analysis (QMTLS iterator) revealed altered glycometabolism in KBD, with increased glucose and decreased lactate and citrate levels. IPA biological analysis showed the centric location of glucose in the metabolic network. Massive glycogen deposits in chondrocytes and increased uptake of glucose by chondrocytes further confirmed disordered glycometabolism in KBD. An in vitro study showed the effects of disordered glycometabolism in chondrocytes. When chondrocytes were treated with high glucose, expression of type II collagen and aggrecan were decreased, while TNF-α expression, the level of cellular reactive oxygen species and cell apoptosis rates all were increased. Therefore, our results demonstrated that disordered glycometabolism in patients with KBD was linked to the damage of chondrocytes. This may provide a new basis for understanding the pathogenesis of KBD. - Highlights: • Disordered glycometabolism in KBD was demonstrated by combining serum metabolomics and chondrocyte studies. • Glucose and TNF-α were key molecules linked to altered metabolism and inflammation in the pathophysiology of KBD. • The glycometabolism disorder was linked to expression of type II collagen and aggrecan, ROS and apoptosis of KBD chondrocytes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-F-CAMPUS-T-05: A Cloud-Based Monte Carlo Dose Calculation for Electron Cutout Factors

Purpose: For electron cutouts of smaller sizes, it is necessary to verify electron cutout factors due to perturbations in electron scattering. Often, this requires a physical measurement using a small ion chamber, diode, or film. The purpose of this study is to develop a fast Monte Carlo based dose calculation framework that requires only a smart phone photograph of the cutout and specification of the SSD and energy to determine the electron cutout factor, with the ultimate goal of making this cloud-based calculation widely available to the medical physics community. Methods: The algorithm uses a pattern recognition technique to identify the corners of the cutout in the photograph as shown in Figure 1. It then corrects for variations in perspective, scaling, and translation of the photograph introduced by the user’s positioning of the camera. Blob detection is used to identify the portions of the cutout which comprise the aperture and the portions which are cutout material. This information is then used define physical densities of the voxels used in the Monte Carlo dose calculation algorithm as shown in Figure 2, and select a particle source from a pre-computed library of phase-spaces scored above the cutout. The electron cutout factor is obtained by taking a ratio of the maximum dose delivered with the cutout in place to the dose delivered under calibration/reference conditions. Results: The algorithm has been shown to successfully identify all necessary features of the electron cutout to perform the calculation. Subsequent testing will be performed to compare the Monte Carlo results with a physical measurement. Conclusion: A simple, cloud-based method of calculating electron cutout factors could eliminate the need for physical measurements and substantially reduce the time required to properly assure accurate dose delivery.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Pam2 lipopeptides systemically increase myeloid-derived suppressor cells through TLR2 signaling

Myeloid-derived suppressor cells (MDSCs) are immature myeloid cells that exhibit potent immunosuppressive activity. They are increased in tumor-bearing hosts and contribute to tumor development. Toll-like receptors (TLRs) on MDSCs may modulate the tumor-supporting properties of MDSCs through pattern-recognition. Pam2 lipopeptides represented by Pam2CSK4 serve as a TLR2 agonist to exert anti-tumor function by dendritic cell (DC)-priming that leads to NK cell activation and cytotoxic T cell proliferation. On the other hand, TLR2 enhances tumor cell progression/invasion by activating tumor-infiltrating macrophages. How MDSCs respond to TLR2 agonists has not yet been determined. In this study, we found intravenous administration of Pam2CSK4 systemically up-regulated the frequency of MDSCs in EG7 tumor-bearing mice. The frequency of tumor-infiltrating MDSCs was accordingly increased in response to Pam2CSK4. MDSCs were not increased by Pam2CSK4 stimuli in TLR2 knockout (KO) mice. Adoptive transfer experiments using CFSE-labeled MDSCs revealed that the TLR2-positive MDSCs survived long in tumor-bearing mice in response to Pam2CSK4 treatment. Since the increased MDSC population sustained immune-suppressive properties, our study suggests that Pam2CSK4-triggered TLR2 activation enhances the MDSC potential and suppress antitumor immune response in tumor microenvironment. - Highlights: • Pam2CSK4 administration induces systemic accumulation of CD11b{sup +}Gr1{sup +} MDSCs. • TLR2 is essential for Pam2CSK4-induced accumulation of CD11b{sup +}Gr1{sup +} MDSCs. • Pam2CSK4 supports survival of CD11b{sup +}Gr1{sup +} MDSCs in vivo.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

MicroRNA-29a mitigation of toll-like receptor 2 and 4 signaling and alleviation of obstructive jaundice-induced fibrosis in mice

Highlights: • miR-29a overexpression in cholestatic mice obstructed TLR2 and TLR4 signaling in liver tissues. • miR-29a overexpression in cholestatic mice decreased IL-1β, MCP-1, TGF-β, TNF-α, as well as HMGB1, and BRD4 expression. • Overexpression of miR-29a downregulated α-SMA, TLR2, TLR4 and BRD4 expression in HSCs. Cholestasis and hepatitis can cause continuous liver damage that may ultimately result in liver fibrosis. In a previous study, we demonstrated that microRNA-29a (miR-29a) protects against liver fibrosis. Toll-like receptor 2 (TLR2) and TLR4 are pattern recognition receptors of bacterial lipoprotein and lipopolysaccharide, both of which participate in activating hepatic stellate cells and liver fibrosis. The purpose of this study is to characterize the biological influence of miR-29a on TLR2 and TLR4 signaling in livers injured with bile duct ligation (BDL). We performed BDL on both miR-29a transgenic mice (miR-29aTg) and wild-type mice to induce cholestatic liver injury. Primary HSCs were transfected with a miR-29a mimic and inhibitor. In the wild-type mice, the BDL demonstrated significant α-smooth muscle actin fibrotic matrix formation and hepatic high mobility group box-1 expression. However, in the miR-29aTg mice, these factors were significantly reduced. Furthermore, miR-29a overexpression reduced the BDL exaggeration of TLR2, TLR4, MyD88, bromodomain-containing protein 4 (BRD4), phospho-p65 as well as proinflammatory cytokines, IL-1β, MCP-1, TGF-β, and TNF-α. In vitro, miR-29a mimic transfection reduced α-SMA, BRD4,TLR2, and TLR4 expressions in HSCs. This study provides new molecular insight into the ability of miR-29a to inhibit TLR2 and TLR4 signaling, which thus slows the progression of cholestatic liver deterioration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Activation of TLR3 and its adaptor TICAM-1 increases miR-21 levels in extracellular vesicles released from human cells

Highlights: • Activation of TLR3 increases miR-21 levels in extracellular vesicles. • TLR3 requires TICAM-1 adaptor to increase miR-21 levels in extracellular vesicles. • YBX-1 is involved in TLR3-mediated increase of miR-21 levels. Pattern-recognition receptors (PRRs) recognizes viral RNAs and trigger the innate immune responses. Toll-like receptor 3 (TLR3), a PRR, recognizes viral double-stranded RNA (dsRNA) in endolysosomes, whereas cytoplasmic dsRNA is sensed by another PRR, MDA5. TLR3 and MDA5 utilize TICAM-1 and MAVS, respectively, to trigger the signal for inducing innate immune responses. Extracellular vesicles (EVs) include the exosomes and microvesicles; an accumulating body of evidence has shown that EVs delivers functional RNA, such as microRNAs (miRNAs), to other cells and thus mediate intercellular communications. Therefore, EVs carrying miRNAs affect innate immune responses in macrophages and dendritic cells. However, the mechanism underlying the regulation of miRNA levels in EVs remains unclear. To elucidate the mechanism, we sought to reveal the pathway that control miRNA expression levels in EVs. Here, we found that TLR3 stimulation increased miR-21 levels in EVs released from various types of human cells. Ectopic expression of the TLR3 adaptor, TICAM-1, increased miR-21 levels in EVs but not intracellular miR-21 levels, suggesting that TICAM-1 augmented sorting of miR-21 to EVs. In contrast, the MDA5 adaptor, MAVS, did not increase miR-21 levels in EVs. The siRNA for TICAM-1 reduced EV miR-21 levels after stimulation of TLR3. Collectively, our data indicate a novel role of the TLR3-TICAM-1 pathway in controlling miR-21 levels in EVs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Biophysical characterization of Ca{sup 2+}-binding of S100A5 and Ca{sup 2+}-induced interaction with RAGE

S100A5 is a calcium-binding protein of S100 family, which represents a major ligand to the receptor for advanced glycation end product (RAGE), a pattern recognition receptor engaged in diverse pathological processes. Here we have characterized calcium binding of S100A5 and the complex formation between S100A5 and RAGE using calorimetry and NMR spectroscopy. S100A5 binds to calcium ions in a sequential manner with the equilibrium dissociation constants (K{sub D}) of 1.3 μM and 3.5 μM, which corresponds to the calcium-binding at the C-terminal and N-terminal EF-hands. Upon calcium binding, S100A5 interacts with the V domain of RAGE (RAGE-v) to form a heterotrimer (K{sub D} ∼5.9 μM) that is distinct among the S100 family proteins. Chemical shift perturbation data from NMR titration experiments indicates that S100A5 employs the periphery of the dimer interface to interact with RAGE-v. Distinct binding mode and stoichiometry of RAGE against different S100 family proteins could be important to modulate diverse RAGE signaling. - Highlights: • Thermodynamic of sequential Ca{sup 2+}-binding to S100A5 is determined quantitatively. • S100A5 binds to RAGE-v to form a distinct dynamic heterotrimer with K{sub D} ∼5.9 μM. • RAGE-v binds to the rim of the S100A5 dimer interface via hydrophobic interaction. • Different binding modes of S100 proteins to RAGE may modulate RAGE signaling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Operating displays - new concept: high performance displays - 118

New control rooms in Nuclear Power Plants using digital Distributed Control System (DCS) operated with software displays, are providing thousands of signals to the operating crew. When an event happens, the Flood of information provided to operators could obscure the initiator event increasing the operator response time, work load and complicating the selection of the actions to be performed. There are some initiatives that are trying to minimize the 'avalanche effect' as the alarm engineering filtering and prioritization or diagnostic displays. Likewise, new control rooms are lost the availability to know the plant status just with a quick glance to the panel indicators. Regarding Small Modular Reactors, where in some cases four operators should monitor and control twelve reactors, these problems are even more significant. The challenge is to find a solution, based on current digital systems, that supply information to operators in order to prevent events and take early actions to minimize or avoid any unexpected incident. In order to minimize human errors, Tecnatom has developed a specific set of displays, called 'High Performance Displays' (HPD) based on objectives, work environment limitation, and non-anticipated events. These displays are being design following the HFE standards: applying the NUREG-0711 elements to identify the information that they have to content and the NUREG-0700 to design the interface. The function of these displays is monitoring the plant situation in a way that the operators are able to recognize any potential event at a simple glance but controlling thousands of signals through pattern recognition, symmetries, regular polygons and colors. The objective of the HPD is to provide an integrated function minimizing the tunnel effect and reduce the workload during the diagnosis phase. Moreover, HPD enhance operators to take the right actions to anticipate the response to any event, even avoiding it, if possible This paper analyzes how HPD's have been design as well as presents the results of a preliminary validation of their capabilities. The preliminary validation is a reduce validation in a full scope simulator running two representative scenarios of abnormal and emergency operation. The analysis is focused on the first stage of the event, i.e. the detection phase, HPD's have been installed in a generic full scope simulator of a nuclear power plant, digital controlled. Both scenarios have been performed by two different operator crews. The first operator shift has worked with the conventional human machine interface, that is, displays similar to the ones currently installed in existing nuclear power plant with digital control. The second crew, reproduce the same scenarios but with HPD available. The studio collects preliminary data in order to compare results, draw lesson learned from the experience to improve HPD design, and find solutions in terms of reducing human errors, human workload, and increase the availability of the plant. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

A Study of Physics-Informed Deep Learning for System Fluid Dynamics Closures

The sub-grid-scale (SGS) physics models, or so-called closure relations (CR), are essential in thermal-fluid modeling and simulation codes, ranging from large-eddy simulation to system-level thermo-fluid dynamic problems. While details vary, all thermo-fluid codes solve coupled mass-momentum-energy conservation partial differential equations (PDE). Traditionally, the analytical form of closure models has to be known, and then we calibrate the model using data from a set of applicable experiments. Characteristically, it takes long effort to gain insights and mechanistic understanding needed for developing closure models. Such a model is a compact form of data. The long time required for new model development limits the pace of model application while dealing with new geometries, new coolants or system conditions, particularly in newly designed systems. Alternative to mechanistic and semi-analytical models, it is possible to discover the pattern of closure relations by applying the modern machine learning methods to the body of relevant experimental data. Deep learning (DL) is a relatively new branch in the machine learning family, and a major breakthrough has been achieved since Hinton introduces a fast algorithm to efficiently train a feed-forward neural network (NN) with multilayer perceptrons. Generally, any NN with more than two layers is considered as deep learning, and it contains a large number of hyper-parameters to achieve pattern recognition or regression for complex datasets. Hornik shows that multilayer NN is compatible with universal approximation theorem. There is no constraint for a multilayer NN to capture the properties of any measurable information. This capability makes DL attractive for closure models in a system thermal-hydraulic code. As system geometry and/or operating conditions change, new physical experiments (increasingly sophisticated) are conducted to support evaluation and revision of closure models. Such a so-called data-driven modeling relaxes the structural form of system codes to directly assimilate data instead of hard-wiring the model to pre-calibrated correlations. The objective of this research is to enable cost-effective development of a DL-based closure that reflects the data. The previous work on applying machine learning techniques to fluid dynamics focused on statistical learning with basic NN from direct numerical simulations (DNS) dataset. In the present work, we show that DL-based closure models can cause the fluid calculation to diverge if multilayer NN is used. To address the issue, we investigate conditions by which the DL-based closure models work compatibly, stably, and effectively with PDE-constrained forward prediction problems. Optimally meeting the compatibility conditions constitutes a physics-informed deep learning (PIDL) strategy. We refer to this process as PDE-constrained machine learning or PDE-ML. To develop the technical basis for PDE-ML using DL-based fluid dynamics closure, numerical experiments are performed for manufactured problems of increasing complexity. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

First results of the silicon telescope using an 'artificial retina' for fast track finding

We present the first results of the prototype of a silicon tracker with trigger capabilities based on a novel approach for fast track finding. The working principle of the 'artificial retina' is inspired by the processing of visual images by the brain and it is based on extensive parallelization of data distribution and pattern recognition. The algorithm has been implemented in commercial FPGAs in three main logic modules: a switch for the routing of the detector hits, a pool of engines for the digital processing of the hits, and a block for the calculation of the track parameters. The architecture is fully pipelined and allows the reconstruction of real-time tracks with a latency less then 100 clock cycles, corresponding to 0.25 microsecond at 400 MHz clock. The silicon telescope consists of 8 layers of single-sided silicon strip detectors with 512 strips each. The detector size is about 10 cm x 10 cm and the strip pitch is 183 μm. The detectors are read out by the Beetle chip, a custom ASICs developed for LHCb, which provides the measurement of the hit position and pulse height of 128 channels. The 'artificial retina' algorithm has been implemented on custom data acquisition boards based on FPGAs Xilinx Kintex 7 lx160. The parameters of the tracks detected are finally transferred to host PC via USB 3.0. The boards manage the read-out ASICs and the sampling of the analog channels. The read-out is performed at 40 MHz on 4 channels for each ASIC that corresponds to a decoding of the telescope information at 1.1 MHz. We report on the first results of the fast tracking device and compare with simulations. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

PTEN and PI-3 kinase inhibitors control LPS signaling and the lymphoproliferative response in the CD19+ B cell compartment

Pattern recognition receptors (PRRs), e.g. toll receptors (TLRs) that bind ligands within the microbiome have been implicated in the pathogenesis of cancer. LPS is a ligand for two TLR family members, TLR4 and RP105 which mediate LPS signaling in B cell proliferation and migration. Although LPS/TLR/RP105 signaling is well-studied; our understanding of the underlying molecular mechanisms controlling these PRR signaling pathways remains incomplete. Previous studies have demonstrated a role for PTEN/PI-3K signaling in B cell selection and survival, however a role for PTEN/PI-3K in TLR4/RP105/LPS signaling in the B cell compartment has not been reported. Herein, we crossed a CD19cre and PTEN{sup fl/fl} mouse to generate a conditional PTEN knockout mouse in the CD19+ B cell compartment. These mice were further crossed with an IL-14α transgenic mouse to study the combined effect of PTEN deletion, PI-3K inhibition and expression of IL-14α (a cytokine originally identified as a B cell growth factor) in CD19+ B cell lymphoproliferation and response to LPS stimulation. Targeted deletion of PTEN and directed expression of IL-14α in the CD19+ B cell compartment (IL-14+PTEN-/-) lead to marked splenomegaly and altered spleen morphology at baseline due to expansion of marginal zone B cells, a phenotype that was exaggerated by treatment with the B cell mitogen and TLR4/RP105 ligand, LPS. Moreover, LPS stimulation of CD19+ cells isolated from these mice display increased proliferation, augmented AKT and NFκB activation as well as increased expression of c-myc and cyclinD1. Interestingly, treatment of LPS treated IL-14+PTEN-/- mice with a pan PI-3K inhibitor, SF1126, reduced splenomegaly, cell proliferation, c-myc and cyclin D1 expression in the CD19+ B cell compartment and normalized the splenic histopathologic architecture. These findings provide the direct evidence that PTEN and PI-3K inhibitors control TLR4/RP105/LPS signaling in the CD19+ B cell compartment and that pan PI-3 kinase inhibitors reverse the lymphoproliferative phenotype in vivo. - Highlights: • First genetic evidence that PTEN controls LPS/TLR4 signaling in B lymphocytes. • Evidence that PTEN regulates LPS induced lymphoproliferation in vivo. • PI-3 kinase inhibitors block LPS induced lymphoproliferation in vivo.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

X-ray diagnostic developments in the perspective of DEMO

Soft X-ray diagnostics at present are not adequate for a burning plasma experiment, neither in term of hardware nor as diagnostic conception. Detectors have to be radiation tolerant, easily shielded, with low sensitivity to neutrons and gammas and with energy discrimination. Layout and viewing capability should be more flexible, thanks to the use also of optical devices, going toward a configuration intermediate between discrete tomography and pure imaging. The general conception of these diagnostics should therefore evolve in the direction of pattern recognition for a real time feedback. This work is focused on the diagnostic developments undertaken at the ENEA- Frascati X-ray Laboratory, following in particular three directions: gas detector for fast and advanced high density tomography, C-MOS solid state imaging detectors for slow control and X-ray polycapillary optics. GEM gas detectors in photon counting mode (noise free) were developed in the range 1-30 keV having high efficiency, high time resolution (up to microseconds), energy discrimination in bands and optical flexibility. Discrimination of X-rays, neutrons and gammas has been demonstrated, thanks to the combination of intrinsic gain and discrimination thresholds, at neutron fluxes (10{sup 7} n/s*cm{sup 2}) comparable with the expected ones at the ports of ITER. GEM detectors are also extremely flexible in the design, allowing optimization of the measurements and solutions for shielding or minimization of the effect of background radiation. Two solid state C-MOS imagers working in photon counting mode, one based on Si semiconductor (Medipix-2, range 5-30 keV) and the other one having a CdTe sensor (Pixirad, range 2-100 keV) have been characterized in laboratory. C-MOS imagers have features and performances thoroughly complementary to the GEM detector and, thanks to their higher pixel density, could be used as remote imaging detectors coupled to optics. X-ray polycapillary lenses have been therefore studied in the laboratory both as imaging optical device (full lens) or to define a line of sight (cylinder lens); the preliminary results are encouraging toward the goal of using these lenses to transport X-ray radiation far from the reactor. Reflective or diffractive X-ray optics can be also an option for tomography. A GEM detector has been installed at KSTAR and in the next future hopefully the other approaches will be tested there: once their feasibility will be demonstrated, the issue of the improvement of the radiation tolerance will be faced and the development of algorithms for data analysis as well.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/c charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm 0.6$% and 84.1$\pm 0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Automated assignment of rotational spectra using artificial neural networks

Here, a typical broadband rotational spectrum may contain several thousand observable transitions, spanning many species. While these spectra often encode troves of chemical information, identifying and assigning the individual spectra can be challenging. Traditional approaches typically involve visually identifying a pattern. A more modern approach is to apply an automated fitting routine. In this approach, combinations of 3 transitions are searched by trial and error, to fit the A , B , and C rotational constants in a Watson-type Hamiltonian. In this work, we develop an alternative approach-to utilize machine learning to train a computer to recognize the patterns inherent in rotational spectra. Broadband high-resolution rotational spectra are perhaps uniquely suited for pattern recognition, assignment, and species identification using machine learning. Repeating patterns of transition frequencies and intensities are now routinely recorded in broadband chirped-pulse Fourier transform microwave experiments in which both the number of resolution elements and the dynamic range surpass 10 4 . At the same time, these high-resolution spectra are extremely sensitive to molecular geometry with each polar species having a unique rotational spectrum. Here we train the feed forward neural network on thousands of rotational spectra that we calculate, using the rules of quantum mechanics, from randomly generated sets of rotational constants and other Hamiltonian parameters. Reasonable physical constraints are applied to these parameter sets, yet they need not belong to existing species. A trained neural network presented with a spectrum identifies its type (e.g., linear molecule, symmetric top, or asymmetric top) and infers the corresponding Hamiltonian parameters (rotational constants, distortion, and hyperfine constants). The classification and prediction times, about 160 μs and 50 μs, respectively, seem independent of the spectral complexity or the number of molecular parameters. We describe how the network works, provide benchmarking results, and discuss future directions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Advancing Biocapture Substrates via Chemical Lift-Off Lithography

Creating small-molecule-functionalized platforms for high-throughput screening or biosensing applications requires precise placement of probes on solid substrates and the ability to capture and to sort targets from multicomponent samples. Here, chemical lift-off lithography was used to fabricate large-area, high-fidelity patterns of small-molecule probes. Lift-off lithography enables biotin–streptavidin patterned recognition with feature sizes ranging from micrometers to below 30 nm. Subtractive patterning via lift-off facilitated insertion of a different type of molecule and, thus, multiplexed side-by-side placement of small-molecule probes such that binding partners were directed to cognate probes from solution. Small molecules mimicking endogenous neurotransmitters were patterned using lift-off lithography to capture native membrane-associated receptors. Here, we characterized patterning of alkanethiols that self-assemble on Au having different terminal functional groups to expand the library of molecules amenable to lift-off lithography enabling a wide range of functionalization chemistries for use with this simple and versatile patterning method.

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

Central alarm station interface for situation awareness

A method for analyzing security alarms is provided. The method comprising receiving alarm data from a number of security sensors generating alarms, wherein the alarm data includes spatio-temporal data regarding where and when an alarm is triggered. The alarm data is displayed on a user interface. The alarm data is analyzed for spatio-temporal relationships among the alarms according to a number of pattern recognition algorithms. The alarms are identified as genuine alarms if a discernable pattern is recognized among the alarms.

Speed, Ann E.↗
DOE OSTI2018

Fluorescence Color by Data-Driven Design of Genomic Silver Clusters

DNA nucleobase sequence controls the size of DNA-stabilized silver clusters, leading to their well-known yet little understood sequence-tuned colors. The enormous space of possible DNA sequences for templating clusters has challenged the understanding of how sequence selects cluster properties and has limited the design of applications that employ these clusters. Here, we investigate the genomic role of DNA sequence for fluorescent silver clusters using a data-driven approach. Employing rapid parallel silver cluster synthesis and fluorimetry, we determine the fluorescence spectra of silver cluster products stabilized by 1432 distinct DNA oligomers. By applying pattern recognition algorithms to this large experimental data set, we discover certain DNA base patterns, or “motifs,” that correlate to silver clusters with similar fluorescence spectra. These motifs are employed in machine learning classifiers to predictively design DNA template sequences for specific fluorescence color bands. Our method improves selectivity of templates by 330% for silver clusters with peak emission wavelengths beyond 660 nm. The discovered base motifs also provide physical insights into how DNA sequence controls silver cluster size and color. Finally, this predictive design approach for color of DNA-stabilized silver clusters exhibits the potential of machine learning and data mining to increase the precision and efficiency of nanomaterials design, even for a soft-matter-inorganic hybrid system characterized by an extremely large parameter space.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Boost of the Bio-memristor Performance for Artificial Electronic Synapses by Surface Reconstruction

Biomaterial-based memristors (bio-memristors) are often adopted to emulate biological synapse functions and applied to construct neural computing networks in brain-inspired chip systems. However, the randomness of conductive filament formation in bio-memristors inhibits their switching performance by causing the dispersion of the device-switching parameters. In this case, a facile porous silk fibroin (p-SF) memristor was obtained through a protein surface reconstruction strategy, in which the size of the hole can be adjusted by the density of hybrid nanoseeds. Here, the porous SF memristors exhibit greatly enhanced electrical characteristics, including uniform I-V cycles, centralized distribution of the switching voltages, and both high and low resistances, compared to devices without pores. The results of three-dimensional (3D) simulations based on classical density functional theory (cDFT) suggest that the reconstructed pores in the SF layers guide the formation and fracture of Ag filaments under an electric field and enhance the overall conductivity by separating Ag + ion and electron diffusion pathways. Ag + ions are predicted to preferentially diffuse through pores, whereas electrons diffuse through the SF network. Interestingly, the device conductance can be bidirectionally modulated gradually by positive and negative voltages, can faithfully simulate short-term and long-term plasticity, and can even realize the triplet-spike-timing-dependent plasticity (triplet-STDP) rule, which can be used for pattern recognition in biological systems. The simulation results reveal that a memristor network of this type has an accuracy of ~95.78% in memory learning and the capability of pattern learning. This work provides a facile technology route to improve the performance of bionic-material memristors.

36 MATERIALS SCIENCE↗