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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

(abstract) A High Throughput 3-D Inner Product Processor

A particularily challenging image processing application is the real time scene acquisition and object discrimination. It requires spatio-temporal recognition of point and resolved objects at high speeds with parallel processing algorithms. Neural network paradigms provide fine grain parallism and, when implemented in hardware, offer orders of magnitude speed up. However, neural networks implemented on a VLSI chip are planer architectures capable of efficient processing of linear vector signals rather than 2-D images. Therefore, for processing of images, a 3-D stack of neural-net ICs receiving planar inputs and consuming minimal power are required. Details of the circuits with chip architectures will be described with need to develop ultralow-power electronics. Further, use of the architecture in a system for high-speed processing will be illustrated.

imaging parallel processing algorithms linear vect↗
DOE OSTI2020

Criticality Consideration for Geologic Disposal of DOE-Owned Spent Nuclear Fuels - 20255

Decisions must be made relative to packaging spent nuclear fuel (SNF) for storage, transportation, and eventual disposal. This paper evaluates potential criticality-related loading and packaging considerations for Department of Energy (DOE)-owned SNF that could be impacted favorably or unfavorably by disposal uncertainties and proposes approaches to minimize such impacts. Using Al-Based SNF as an example and drawing on analyses performed in support of the Yucca Mountain Repository License Application (LA), this paper explores the role of natural barriers, engineered barriers, and waste form characteristics relative to reducing the potential for postclosure criticality. Several parameters important to postclosure criticality are considered in order to determine which are useful discriminators to support packaging decisions. Using these parameters, a qualitative assessment is performed relative to three options for managing DOE-owned SNFs. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Tailored Graphene Micropatterns by Wafer-Scale Direct Transfer for Flexible Chemical Sensor Platform

2D materials, such as graphene, exhibit great potential as functional materials for numerous novel applications due to their excellent properties. The grafting of conventional micropatterning techniques on new types of electronic devices is required to fully utilize the unique nature of graphene. However, the conventional lithography and polymer-supported transfer methods often induce the contamination and damage of the graphene surface due to polymer residues and harsh wet-transfer conditions. In this work, a novel strategy to obtain micropatterned graphene on polymer substrates using a direct curing process is demonstrated. Employing this method, entirely flexible, transparent, well-defined self-activated graphene sensor arrays, capable of gas discrimination without external heating, are fabricated on 4 in. wafer-scale substrates. Finite element method simulations show the potential of this patterning technique to maximize the performance of the sensor devices when the active channels of the 2D material are suspended and nanoscaled. This study contributes considerably to the development of flexible functional electronic devices based on 2D materials.

2D materials, Graphene, Microscale patterning, Che↗
DOE OSTI2024

Molecularly Imprinted Polymer Sensor Empowered by Bound States in the Continuum for Selective Trace-Detection of TGF-beta

The integration of advanced materials and photonic nanostructures can lead to enhanced biodetection capabilities, crucial in clinical scenarios and point-of-care diagnostics, where simplified strategies are essential. Herein, a molecularly imprinted polymer (MIP) photonic nanostructure is demonstrated, which selectively binding to transforming growth factor-beta (TGF-β), in which the sensing transduction is enhanced by bound states in the continuum (BICs). The MIP operating as a synthetic antibody matrix and coupled with BIC resonance, enhances the optical response to TGF-β at imprinted sites, leading to an augmented detection capability, thoroughly evaluated through spectral shift and optical lever analogue readout. The validation underscores the MIP-BIC sensor capability to detect TGF-β in spiked saliva, achieving a limit of detection of 10 fM and a resolution of 0.5 pM at physiological concentrations, with a precision of two orders of magnitude above discrimination threshold in patients. The MIP tailored selectivity is highlighted by an imprinting factor of 52, showcasing the sensor resistance to interference from other analytes. The MIP-BIC sensor architecture streamlines the detection process eliminating the need for complex sandwich immunoassays and demonstrates the potential for high-precision quantification. This positions the system as a robust tool for biomarker detection, especially in real-world diagnostic scenarios.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Spatially Resolved Performance Analysis for Perovskite Solar Cells

This review explores the current state of the art in spatially resolved characterization of mixed-halide perovskite solar cells. As the size of perovskite cells and modules continues to grow, quantification of the spatial distribution of key cell parameters will become increasingly valuable in predicting ultimate cell-level performance and tracking process homogeneity. Here, both high resolution microscopic approaches using scanning techniques and camera-based methods for full-area cell and/or module analysis are discussed. The value of this local data in predicting performance losses at the cell level is particularly emphasized. Measurable physical parameters sensitive to losses of voltage, current, fill factor, and efficiency are discussed together with selected experimental results. It is demonstrated that a combination of spatially resolved cell parameter mapping/imaging can be used to quantitatively discriminate various loss contributions at high resolution. The impact and control of inhomogeneities become particularly important when upscaling from small devices to large formats compatible with industrial mass production.

14 SOLAR ENERGY↗
DOE OSTI2021

Deterministic symbolic regression with derivative information: General methodology and application to equations of state

Symbolic regression methods simultaneously determine the model functional form and the regression parameter values by generating expression trees. Symbolic regression can capture the complexity of real–world phenomena but the use of deterministic optimization for symbolic regression has been limited due to the complexity of the search space of existing formulations. Herein we present a novel deterministic mixed–integer nonlinear programming formulation for symbolic regression that incorporates derivative constraints through auxiliary expression trees. By applying the chain rule to mathematical operations, binary expression trees are capable of representing the calculation of first and second derivatives. We apply this formulation to illustrative examples using derivative information to show increased model discrimination capability. In addition, we perform a case study of a thermodynamic equation of state to gain insight on valid functional forms with thermodynamics–based constraints on the first and second derivatives.

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

Multimodal Imaging of Autofluorescent Sites Reveals Varied Chemical Speciation in SSZ-13 Crystals

Here, a multimodal imaging study of chabazite is used to show the distribution of and discriminate between different emissive deposits arising as a result of the detemplation process. 3D confocal imaging, fluorescence lifetime imaging, spectral imaging, and Raman mapping are used to show three different types of emissive behaviours each characterised by different spatial distributions, trends in lifetime, spectral signals, and Raman signatures. A notable difference is seen in the morphology of agglomerated surface deposits and larger subsurface deposits, which experience lifetime augmentation due to spatial confinement. The distribution of organic residue throughout the crystal volume is comparable to XRF mapping that shows Si enrichment on the outer edges and higher Al content through the centre, demonstrating that a fluorescence-based technique can also be used to indirectly comment on the compositional chemistry of the inorganic framework.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Temperature‐Programmed Separation of Hexane Isomers by a Porous Calcium Chloranilate Metal‐Organic Framework

Abstract The full separation of alkane isomers as a function of different degrees of branching remains a daunting challenge due to its stringent requirement with respect to pore dimensions of the adsorbents. In this work, we report a novel microporous coordination network built on calcium (II) and chloranilate. The compound has a flexible framework and exhibits temperature‐dependent adsorption behavior toward hexane isomers. At 30 °C, it accommodates substantial amounts of linear and monobranched hexanes but fully excludes their dibranched isomer, and at elevated temperatures such as 150 °C, it acts as a splitter for linear and branched alkanes. Its capability of efficient discrimination of hexane isomers as a function of branching is verified by experimental breakthrough measurements. Ab initio calculations have uncovered the underlying selective size‐exclusion separation mechanism.

Lin, Yuhan↗
DOE OSTI2020

Multimodal Imaging of Autofluorescent Sites Reveals Varied Chemical Speciation in SSZ-13 Crystals

In this report a multimodal imaging study of chabazite is used to analyse emissive deposits. A notable difference is seen in the morphology of agglomerated surface deposits and larger subsurface deposits. Comparison of the distribution of organic residue throughout the crystal volume and XRF mapping is demonstrating that a fluorescence-based technique can also be used to indirectly comment on the compositional chemistry of the inorganic framework. A multimodal imaging study of chabazite is used to show the distribution of and discriminate between different emissive deposits arising as a result of the detemplation process. Confocal imaging, 3D fluorescence lifetime imaging, 3D multispectral fluorescence imaging, and Raman mapping are used to show three different types of emissive behaviours each characterised by different spatial distributions, trends in lifetime, spectral signals, and Raman signatures. A notable difference is seen in the morphology of agglomerated surface deposits and larger subsurface deposits, which experience lifetime augmentation due to spatial confinement. The distribution of organic residue throughout the crystal volume is comparable to XRF mapping that shows Si enrichment on the outer edges and higher Al content through the centre, demonstrating that a fluorescence-based technique can also be used to indirectly comment on the compositional chemistry of the inorganic framework.

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

Temperature–Programmed Separation of Hexane Isomers by a Porous Calcium Chloranilate Metal–Organic Framework

The full separation of alkane isomers as a function of different degrees of branching remains a daunting challenge due to its stringent requirement with respect to pore dimensions of the adsorbents. In this work, we report a novel microporous coordination network built on calcium (II) and chloranilate. The compound has a flexible framework and exhibits temperature-dependent adsorption behavior toward hexane isomers. At 30 °C, it accommodates substantial amounts of linear and monobranched hexanes but fully excludes their dibranched isomer, and at elevated temperatures such as 150 °C, it acts as a splitter for linear and branched alkanes. Its capability of efficient discrimination of hexane isomers as a function of branching is verified by experimental breakthrough measurements. Furthermore, ab initio calculations have uncovered the underlying selective size-exclusion separation mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Linker Vacancy Engineering of a Robust ftw–type Zr–MOF for Hexane Isomers Separation

Discrimination of physically similar molecules by porous solids represents an important yet challenging task in industrially relevant chemical separations. Precisely controlled pore dimension and/or tailored pore surface functionality are crucial to achieve high-efficiency separation. Metal-organic frameworks (MOFs) are promising candidates for these challenging separations in light of their structural diversity as well as highly adjustable pore dimension/functionality. We report here a microporous, ftw-type Zr-based MOF structure, HIAM-410 (HIAM=Hoffmann Institute of Advanced Materials), built on hexanuclear Zr 6 cluster and pyrene-1,3,6,8-tetracarboxylate (ptc 4– ). Its crystallographic structure has been determined using continuous rotation electron diffraction (cRED) technique combined with Rietveld refinement against powder X-ray diffraction data, aided by low-dose high-resolution transmission electron microscopy (HRTEM) imaging. The compound features exceptional framework stability that is comparable to the prototype MOF UiO-66. Interestingly, the linker vacancies in the pristine MOF structure could be partially restored by post-synthetic linker insertion. Its separation capability of hexane isomers is enhanced substantially upon the linker vacancy engineering. As a result, the restored structure exhibits efficient splitting of monobranched and dibranched hexane isomers at both room temperature and industrially relevant temperature.

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

Super resolution for root imaging

Premise High‐resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above‐ground plant attributes. However, the acquisition of high‐resolution images of plant roots is more challenging than above‐ground data collection. An effective super‐resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors, reducing storage space requirements, and boosting the performance of subsequent analyses. Methods We propose an SR framework for enhancing images of plant roots using convolutional neural networks. We compare three alternatives for training the SR model: (i) training with non‐plant‐root images, (ii) training with plant‐root images, and (iii) pretraining the model with non‐plant‐root images and fine‐tuning with plant‐root images. The architectures of the SR models were based on two state‐of‐the‐art deep learning approaches: a fast SR convolutional neural network and an SR generative adversarial network. Results In our experiments, we observed that the SR models improved the quality of low‐resolution images of plant roots in an unseen data set in terms of the signal‐to‐noise ratio. We used a collection of publicly available data sets to demonstrate that the SR models outperform the basic bicubic interpolation, even when trained with non‐root data sets. Discussion The incorporation of a deep learning–based SR model in the imaging process enhances the quality of low‐resolution images of plant roots. We demonstrate that SR preprocessing boosts the performance of a machine learning system trained to separate plant roots from their background. Our segmentation experiments also show that high performance on this task can be achieved independently of the signal‐to‐noise ratio. We therefore conclude that the quality of the image enhancement depends on the desired application.

Ruiz‐Munoz, Jose F.↗
DOE OSTI2020

A nomogram for prediction of stage III/IV gastric cancer outcome after surgery: A multicenter population‐based study

Abstract Most patients with gastric cancer (GC) are first diagnosed at stage III‐IV and surgery resection remains the primary therapeutic modality for these patients. However, clinical staging used for prediction of those patients provides limited information. We collected clinicopathological data and disease‐progression information from 508 patients with stage III‐IV GC at three Chinese hospitals and 1298 patients from the Surveillance, Epidemiology, and End Results database. Based on the stepwise multivariate regression model, we constructed a novel nomogram to predict overall survival (OS). The performance of discrimination for this model was measured using Harrell's concordance index (C‐index) and receiver‐operating characteristic curve (ROC), and was validated using calibration plots. Multivariate Cox regression analyses showed that tumor size, age at diagnosis, N stage, tumor grade, and distant metastases were outstanding independent prognostic factors of stage III‐IV GC. We developed a nomogram based on these five prognostic predictors. In the training set, the C‐index of the nomogram was 0.645 (95% CI: 0.611‐0.679), which was higher than that of the American Joint Committee on Cancer TNM system alone (sixth TNM: 0.544; seventh TNM: 0.575; eighth TNM: 0.568). Similar results were observed in validation cohort. Moreover, calibration blots demonstrated good consistency between the actual and predicted OS probabilities. According to the nomogram, GC individuals could be classified into three groups (low‐, middle‐, and high‐risk) ( P < .001). Our nomogram complements the current staging system for prediction of individual prognosis with stage III‐IV GC, and may be helpful for making individualized treatment decisions.

Gao, Zhiying↗
DOE OSTI2025

Pyrrole‐Imine Macrocycle: Self‐Organizing Cross‐Reactive Anion Receptor and Sensor

Self-organizing macrocyclic receptor-sensors for phosphorus oxyanions, phosphates, and phosphonates comprising imine moieties were prepared by condensation of dipyrrolylmethane dicarbaldehyde with diethylene triamine. The incorporation of flexible ethylene moieties endows the macrocycle with unprecedented flexibility and ability to accommodate numerous phosphorus oxyanions from orthophosphate to large anions such as ATP or phosphonate glyphosate. The anion binding was elucidated by NMR titrations, low-temperature NMR, and NOESY NMR. The incorporation of dansyl fluorophore enables sensing of anions using the fluorescence signal, whereas the changes in fluorescence intensity, width of the fluorescence band, and position of the maxima are analyte-specific and useful in recognition and identification of eleven different P-oxyanions in water. The affinity (K assoc ) for Na + salts was H 2 PO 4 − ≈ Methylphosphonate > H 2 P 2 O 7 2− > Phenylphosphonate- > Glyphosate 2− > AMP 2− > ADP 2− > ATP 2− . Interestingly, phosphonates, including methylphosphonate and glyphosate anions, were also found to display a strong affinity (K assoc ∼10 6 M −1 ) while halides, nitrate, carbonates, or hydrogen sulfate did not show a significant affinity. The determined fluorescence spectral parameters were used to classify the 12 analytes (11 anions and water) using Linear Discriminant Analysis (LDA). Quantification was performed using LDA and Support Vector Machine (SVM), and the phosphonate concentrations in unknown samples were determined with an error of 3.5% or lower.

anions↗
DOE OSTI2020

Ecohydrology of wetland plant communities along an estuarine to tidal river gradient

Abiotic filters that interact with wetland plant communities along tidal–fluvial gradients are highly dynamic, and understanding their quantitative thresholds and relationships to interspecific competition is important during an era of sea-level rise and watershed hydrologic change. Yet, landscape-scale studies of major coastal rivers from the river mouth to the head of tide, such as this study, remain rare. Here, we develop a new predictive framework for estuarine–tidal river research and management using a river-specific low-water datum and the wetland inundation indicator SEVg, the growing-season sum exceedance value of hourly surface-water depth. The distribution and variability of the wetland species pool (n = 203) on the 234 river kilometer (rkm) lower Columbia River and estuary floodplain are described for the first time. 4,940 quadrats at 50 marshes were surveyed (2005–2016). Throughout the estuarine–tidal river system, SEVg was well suited to describe the wetland inundation regime and its variability based on the combination of longitudinal river position and elevation. SEVg increased significantly landward. Two primary wetland inundation regimes were identified: the seaward-tidal, usually greatest during the winter months, and landward-fluvial, greatest during the growing season. Nearest the ocean, salinity is the abiotic factor limiting species richness and non-native species. Farther upriver, the daily, seasonal, and interannual variability of the wetting and drying cycle encourage disturbance-tolerant species and non-natives and limit the number of hydrophytes and total vegetative cover. Hence, the average between-year similarity of site-scale areal cover significantly decreased landward. Hierarchical cluster analysis indicated five vegetative groups and five ecohydrologic zones between rkm 0 and 234 were discriminated with 76 significant species–zone associations. All zones had unique indicator species. Species with high indicator values were Carex lyngbyei throughout the estuarine zones, and Eleocharis palustris, Sagittaria latifolia, and the invasive non-native Phalaris arundinacea in the upper estuarine and lower, middle, and upper tidal river zones (IV > 0.90). Competition from C. lyngbyei nearest the ocean and P. arundinacea in the tidal river was associated with reduced species richness when total cover was >65%. This framework of filters informs the design and prediction of future wetland plant communities on coastal river floodplains.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Test–retest reliability for a social discounting of personal information task

Increasing cybercrime rates means identifying potential victims is critically important. Social discounting tasks show that individuals share less personally identifying information as social distance increases. However, the test–retest reliability and uniqueness of this measure is unclear. The current study assessed social discounting for personally identifying information (SDPII), delay discounting, risk taking, and personality at two measurement waves 30 days apart for 64 undergraduate students. Test–retest reliability was statistically significant for the SDPII and all other measures, replicating previous studies. SDPII rates were not significantly correlated with other measures during both measurement waves, showing discriminant validity. SDPII rates were lower than those reported in a previous study but were still well described by a hyperbolic discounting function, suggesting replicability across studies. Furthermore, the high test–retest reliability, uniqueness, and replicability of the SDPII suggests that it may quantitatively identify cybercrime victimization. Future research should test which measure or combination of measures can accurately predict scam and cybercrime victimization to inform data-based interventions.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Southwest Pacific tropical cyclone development classification utilizing machine learning and synoptic composites

This study evaluates the ability of machine learning algorithms to classify tropical depressions (TDs) and tropical storms (TSs) in the western region of the southwest Pacific Ocean (SWPO). Decision rules are generated to predict the environment required for a depression to fully develop into a mature storm, and the most influential predictors in the classification decision are ranked. TD and TS are discriminated based on a maximum sustained wind speed threshold (≥17 ms -1 ). Various aerosol, thermodynamic, and dynamic parameters are extracted closest to the initiation point of each non-developing and developing sample. The covariates associated with each labelled sample are used to train a decision tree and random forest model. Results using a testing dataset suggest the random forest approach more accurately distinguishes between non-developing and developing samples. The classification accuracy of the decision tree and random forest are 72% and 91%, respectively. Random forest outperformed the decision tree by providing higher accuracy in test data. The most important variables for binary classification are sea salt aerosol optical depth (AOD), 1,000 mb relative humidity, and sea surface temperature. AOD is a quantitative estimate of the aerosols presents in the air through the extinction of a ray of light as it passes through the atmosphere. Mean composite maps constructed in an unsupervised manner have been created for the most important variables identified by the random forest classifier during TD and TS events to highlight the difference in geophysical and aerosol variables' climatology during the two different classifications. This work will advance the risk management strategies for northeastern Australia and other SWPO basin islands to control their tropical cyclone related losses through prioritizing forecasting variables that are the strongest predictors of the strengthening of tropical depressions into tropical cyclones.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Molecularly engineering polymeric membranes for H 2 / CO 2 separation at 100–300 °C

Over the last two decades, polymers with superior H 2 /CO 2 separation properties at 100–300 °C have gathered significant interest for H 2 purification and CO 2 capture. This timely review presents various strategies adopted to molecularly engineer polymers for this application. We first elucidate the Robeson's upper bound at elevated temperatures for H 2 /CO 2 separation and the advantages of high-temperature operation (such as improved solubility selectivity and absence of CO 2 plasticization), compared with conventional membrane gas separations at ~35 °C. Second, we describe commercially relevant membranes for the separation and highlight materials with free volumes tuned to discriminate H 2 and CO 2 , including functional polymers (such as polybenzimidazole) and engineered polymers by cross-linking, blending, thermal treatment, thermal rearrangement, and carbonization. Third, we succinctly discuss mixed matrix materials containing size-sieving or H 2 -sorptive nanofillers with attractive H 2 /CO 2 separation properties.

42 ENGINEERING↗