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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 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Diazirine-based photo-crosslinkers for defect free fabrication of solution processed organic light-emitting diodes

The fabrication of solution deposited OLEDs is fraught with difficulties, largely due to the interlayer mixing and surface erosion during sequential deposition of the layers. We demonstrate that these problems can be circumvented by using photopolymerizable diazirine-based cross-linker capable of converting soluble organic materials into highly cross-linked insoluble networks. 3-Trifluoromethyl(aryl)diazirines photolyze readily upon the 10–15 min exposure of 365 nm UV irradiation to generate carbenes, which react with polymers or small molecules via C–H bond insertion producing highly cross-linked materials. This photo-generated cross-linking does not require any catalyst, initiator or short-wavelength UV light and is performed at room temperature, releasing molecular nitrogen as the only byproduct. To show the cross-linked polymer layers do not display inter-layer mixing, we deposited red-emitting regioregular poly(3-hexylthiophene-2,5-diyl) (P3HT) over cross-linked (10% cross-linker) blue emitting dioctyl polyfluorene (PFO) layer. The overlaid layers showed clear and well-defined boundary with no interlayer mixing. The surface morphology of the solution deposited layers was investigated by AFM to show that the cross-linked layers exhibited significant decrease in surface roughness. This is also shown on the example of the hole transporting material 4,4'-bis[N-(1-naphthyl)-N-phenylamino]-biphenyl (NPB) which displayed roughness average to decrease from 6.4 nm to 1.0 nm. The effect of decreased surface roughness on the performance of phosphorescent OLEDs was investigated by fabricating devices with configuration of ITO/PEDOT:PSS/NPB:(0%/5%/10%) cross-linker/MCP:6% Ir(mppy) 3 /TPBI/CsF/Al. Furthermore, following the diazirine-mediated cross-linking, the OLEDs displayed a decrease in turn-on voltage from 3.8 V to 3.0 V along with a six-fold enhancement of external quantum efficiency (EQE max ) from 1.1% to 6.8% and maximum luminous efficiency increase from 3.8 cd A -1 to 22.9 cd A -1 . These results demonstrate that the simple diazirine mediated photo-cross-linking using mild conditions compatible with organic layers is a promising strategy for improving the performance of the solution-processed OLEDs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep learning-based quasi-continuum theory for structure of confined fluids

Predicting the structural properties of water and simple fluids confined in nanometer scale pores and channels is essential in, for example, energy storage and biomolecular systems. Classical continuum theories fail to accurately capture the interfacial structure of fluids. In this work, we develop a deep learning-based quasi-continuum theory (DL-QT) to predict the concentration and potential profiles of a Lennard-Jones (LJ) fluid and water confined in a nanochannel. The deep learning model is built based on a convolutional encoder–decoder network (CED) and is applied for high-dimensional surrogate modeling to relate the fluid properties to the fluid–fluid potential. The CED model is then combined with the interatomic potential-based continuum theory to determine the concentration profiles of a confined LJ fluid and confined water. Further, we show that the DL-QT model exhibits robust predictive performance for a confined LJ fluid under various thermodynamic states and for water confined in a nanochannel of different widths. The DL-QT model seamlessly connects molecular physics at the nanoscale with continuum theory by using a deep learning model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Attribution of the record-high 2023 SST using a deep-learning framework

Abstract The global-mean sea surface temperature (SST) reached a record high in 2023, exceeding the 2016 record by 0.14 °C. This unprecedented change in global-mean SST has major implications for our understanding of internal variability and the forced response in our changing climate. In this work, we use neural networks trained on simulated climate data to separate the contributions of internal variability and the forced response within observations. Performing attribution reveals that internal variability was responsible for +0.07 °C of the 2023 global mean SST, due to anomalously warm conditions in the Pacific, Atlantic, and Indian Ocean basins. Furthermore, these results provide a line of evidence for accelerated forced warming in recent years. Continued monitoring of the climate will be critical for understanding the drivers behind this unprecedented SST record.

Rader, Jamin K. (ORCID:0000000222045977)↗
DOE OSTI2022

Climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics

Predicted growth in world population will put unparalleled stress on the need for sustainable energy and global food production, as well as increase the likelihood of future pandemics. In this work, we identify high-resolution environmental zones in the context of a changing climate and predict longitudinal processes relevant to these challenges. We do this using exhaustive vector comparison methods that measure the climatic similarity between all locations on earth at high geospatial resolution relative to global-scale analyses. The results are captured as networks, in which edges between geolocations are defined if their historical climate similarities exceed a threshold. We apply Markov clustering and our novel Correlation of Correlations method to the resulting climatic networks, which provides unprecedented agglomerative and longitudinal views of climatic relationships across the globe. The methods performed here resulted in the fastest (9.37x10 18 operations/sec) and one of the largest (168.7x10 21 operations) scientific computations ever performed, with more than 100 quadrillion edges considered for a single climatic network. Our climatic analysis reveals areas of the world experiencing rapid environmental changes, which can have important implications for global carbon fluxes and zoonotic spillover events. Correlation and network analyses of this kind are widely applicable across computational and predictive biology domains, including systems biology, ecology, carbon cycles, biogeochemistry, and zoonosis research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Fast and accurate simulations of calorimeter showers with normalizing flows

In this study, we introduce caloflow, a fast detector simulation framework based on normalizing flows. For the first time, we demonstrate that normalizing flows can reproduce many-channel calorimeter showers with extremely high fidelity, providing a fresh alternative to computationally expensive geant4 simulations, as well as other state-of-the-art fast simulation frameworks based on generative adversarial networks (GANs) or variational autoencoders (VAEs). In addition to the usual histograms of physical features and images of calorimeter showers, we introduce a new metric for judging the quality of generative modeling: the performance of a classifier trained to differentiate real from generated images. We show that GAN-generated images can be identified by the classifier with nearly 100% accuracy, while images generated from caloflow are better able to fool the classifier. More broadly, normalizing flows offer several advantages compared to other state-of-the-art approaches (GANs and VAEs), including tractable likelihoods, stable and convergent training, and principled model selection. Normalizing flows also provide a bijective mapping between data and the latent space, which could have other applications beyond simulation, for example, to detector unfolding.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Pattern Discovery in Internet Background Radiation

Internet Background Radiation (IBR) is observed in empty network address spaces. No traffic should arrive there, but it does in overwhelming quantities, gathering evidences of attacks, malwares and misconfigurations. The study of IBR helps to detect spreading network problems, common vulnerabilities and attack trends. However, network traffic data evolves quickly and is of high volume and diversity, i.e., an outstanding big data challenge. When used to assist network security, it also requires the online classification of dynamic streaming data. In this paper, we introduce an AGgregation & Mode (AGM) vector to represent network traffic. The AGM format characterizes IP hosts by extracting aggregated and mode values of IP header fields, and without inspecting payloads. We performed clustering and statistical analysis to explore six months of IBR from 2012 with the AGM mapping. The discovered patterns allow building a classification of IBR, which identifies phenomena that have been actively polluting the Internet for years. The AGM representation is light and tailored for monitoring and pattern discovery. We show that AGM vectors are suitable to analyze large volumes of network traffic: they capture permanent operations, such as long term scanning, as well as bursty events from targeted attacks and short term incidents.

Iglesias, Felix↗
DOE OSTI2018

Scheduling of Collaborative Sequential Compressed Sensing Over Wide Spectrum Band

The cognitive radio (CR) technology holds promise to significantly increase spectrum availability and wireless network capacity. With more spectrum bands opened up for CR use, it is critical yet challenging to perform efficient wideband sensing. In this work, we propose an integrated sequential wideband sensing scheduling framework that concurrently exploits sequential detection and compressed sensing (CS) techniques for more accurate and lower-cost spectrum sensing. First, to ensure more timely detection without incurring high overhead involved in periodic recovery of CS signals, we propose smart scheduling of a CS-based sequential wideband detection scheme to effectively detect the PU activities in the wideband of interest. Second, to further help users under severe channel conditions identify the occupied sub-channels, we develop two collaborative strategies, namely, joint reconstruction of the signals among neighboring users and wideband sensing-map fusion. Third, to achieve robust wideband sensing, we propose the use of anomaly detection in our framework. Lastly, extensive simulations demonstrate that our approach outperforms peer schemes significantly in terms of sensing delay, accuracy and overhead.

42 ENGINEERING↗
DOE OSTI2026

Comprehensive framework for assessing and optimizing existing research networks

Conservation, monitoring, and research networks, or collections of ecological research sites unified under a common mission of data collection or a research mission, are essential infrastructure for understanding large landscapes. However, most networks developed opportunistically over decades rather than through systematic design, creating potential limitations in the ability to address conservation challenges across entire regions. We developed a framework to evaluate how well an existing research network represents the environmental conditions its members study and devised an approach to rank sites of priority for strategic expansion. Our approach measures performance through environmental representativeness, geographic coverage, and adequacy for scientific inference and thus optimizes limited monitoring resources to maximize scientific impact. We demonstrated this approach with the U.S. Department of Agriculture (USDA) Forest Service Experimental Forests and Ranges Network (EFRN), a 79‐site network across the United States that grew opportunistically over a century. At the national scale, the network effectively captured high‐biomass forests important for carbon cycle research; 82% of forest biomass was in well‐represented areas. Some areas in Texas, Florida, the Rocky Mountains, and the West Coast had no relevant EFRN sites, which limits the ability to make regional inferences. A fundamental challenge for the EFRN was that sites improving regional extent coverage sometimes provided minimal national benefits, which can create conflicts between local and global priorities. Adding the highest‐ranked candidate site provided a relevant site for 17% of currently poorly represented 1‐km pixel cells nationally, but regional and national site rankings varied considerably due to nested spatial inference. This framework provides quantitative tools for strategic infrastructure decision‐making, ensures that limited monitoring resources maximize conservation impact, and can be applied broadly to address the widespread challenge of optimizing conservation and monitoring networks worldwide.

additional site↗
DOE OSTI2023

Large-scale cascading of first-order FBG array in a highly multimode coreless fiber using femtosecond laser for distributed thermal sensing

This research focuses on the performance analysis and characterization of a fiber Bragg gratings (FBGs) array, consisting of 10 first-order FBGs inscribed by a femtosecond (FS) laser in a highly multimode coreless fiber. The study evaluates the FBG array's ability to function as a distributed thermal sensing (DTS) platform, with the coreless fiber chosen as the sensing element due to its immunity to dopant migration at high temperatures. The design of a large cascaded first-order FBG array effectively eliminates unwanted harmonic peaks across a wide spectrum range. In contrast, higher-order FBGs introduce limitations due to the overlapping of Bragg peaks with harmonics. The FBG array's performance is evaluated by measuring the reflection spectrum of each grating at different temperatures, showing a high temperature sensitivity of 15.05 pm/°C at a Bragg wavelength of 1606.3 nm, with a linear response in the temperature range of 24 - 1100 °C. The FBG array was designed for a spatial resolution of 5 mm. A mode scrambler in the sensing network is employed, which suppresses multimodal interference, characterizes FBG peak visibility, and stabilizes the interference spectrum. The stability of the FBG array is also assessed over 24 hrs at 1100 °C, and it is observed to be stable during thermal treatment. Heat treatment at 1100°C improves the signal to noise ratio of the FBG array, demonstrating the robustness and suitability of the proposed FBG array on highly multimode coreless fiber as a potential sensing platform for DTS applications in harsh environmental conditions, overcoming the issues of dopant migration presented by dopes silica optical fibers at high temperatures.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Reduced-Order Modeling of Multigroup Neutron Cross Sections for High-Temperature Gas-cooled Reactors

Abstract – Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.

42 - ENGINEERING↗
DOE OSTI2024

Advanced Cross Section Library Generation using Reduced Order Models

Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.

42 - ENGINEERING↗
DOE OSTI2024

Synthesis of Red, Green, and Blue Carbon Quantum Dots and Construction of Multicolor Cellulose-Based Light-Emitting Diodes

Light-emitting diodes (LEDs) are widely used in lighting and display applications. Carbon quantum dots (CQDs), which have high biocompatibility, high resistance to photobleaching, and full-spectrum luminescence, have inherent advantages as fluorescent materials for LED devices. Herein, multicolor CQDs are prepared by a new reagent engineering strategy due to the difference of effective conjugate length and the surface electron-withdrawing groups of CQDs. White CQDs are realized by mixing blue, green, and red CQDs proportionally. Then, the aggregation-caused quenching phenomenon of CQDs is suppressed through the hydrogen-bonding network of cellulose nanofibrils (CNFs). Multicolor fluorescent films are prepared from CQDs and CNFs by simple mixing and casting methods. Finally, thin-film encapsulation based on the photosensitive resin ABPE-10 coating can be realized and rapidly assembles into fluorescent films with different light-emitting colors into LED devices, leading to have superior thermal performance compared with conventional LEDs. White LEDs have excellent white-light illumination performance, with Commission Internationale de L’Eclairage color coordinates of (0.33, 0.37), a correlated color temperature of 5688 K, and a color rendering index of 86. This strategy provides a convenient and scalable pathway for low-cost, environmentally friendly, and high-performance CQDs-based LEDs.

42 ENGINEERING↗
DOE OSTI2018

Asparagine synthetase gene regulatory network and plant nitrogen metabolism

This DOE project concerns the molecular mechanisms by which plants coordinate carbon (C) and nitrogen (N) metabolism. Specifically, it focuses on a histone methyltransferase protein that targets C/N-metabolism genes for chromatin modifications. How the dynamic changes of chromatin regulate energy metabolism in response to developmental and environmental cues is a new area of study. We have filled this knowledge-gap with our studies of SDG8, a histone methyltransferase, that targets light, C and N metabolism genes. We aimed to understand how plants allocate C resources to assimilate inorganic N onto C-skeletons -- the most energy-intensive pathway in plants – in order to balance energy production and consumption in a changing environment. In order to achieve more C-efficient N-storage under low energy conditions, plants convert assimilated N from “reactive” Gln (C5:N2) into “inert” Asn (C4:N2) through the enzyme ASN1. To identify regulatory factors in this pathway, we used the ASN1 promoter in a genetic selection and discovered SDG8, a histone methyltransferase. We discovered that histone modification by SDG8 coordinates the expression of genes involved in energy generation (light-harvesting and C fixation) with energy consumption (N assimilation and sulfur metabolism). We used this opportunity to explore how histone modifications control energy metabolism genome-wide. Our aims focus on various levels of the regulation of energy related genes at the level of: 1) Chromatin; 2) Transcription; and 3) Metabolites of the C/N regulatory network. Specifically, we asked: 1) How does histone methylation regulate energy metabolism during the N response? 2) How does histone methylation control transcription of the energy metabolism genes? And 3) how does the histone methyltransferase SDG8 and other TF regulators affect C/N metabolism and N use efficiency? Our study indicates that N supply triggers genome-wide changes in chromatin modification via the histone methyltransferase SDG8 which impacts on gene expression, RNA processing, chlorophyll synthesis, N uptake and metabolism. Our study thus revealed a previously overlooked layer of chromatin regulation during nutrient signaling. In broad terms, our study provides a framework to understand how chromatin modifications influence responses to environmental changes and provides unique opportunities for breeding and engineering resilient crops with better performance in the current climate model. Accomplishments AIM 1 CHROMATIN: Role of histone methylation in the ASN1 energy metabolism network. This aim focuses on understanding the role of chromatin modification in response to the environment. We studied the targeting mechanism of histone methylation by SDG8, in particular the role of the CW domain in reading histone methylation status. We also began to study the effect of nitrogen-treatment on genome-wide chromatin modifications in WT and sdg8 mutant plants. AIM 2 TRANSCRIPTION: Transcription control of the ASN1 energy metabolism network. This aim investigated whether SDG8 and the histone modification it mediates affects mRNA processing. We performed genome-wide profiling that uncovered that histone methylation by SDG8 affects the co-transcriptional RNA processing of downstream genes involved in energy and photosynthesis. AIM 3 METABOLISM: A gene regulatory network controlling energy, C/N metabolism and N use efficiency. In Aim 3A, we showed that SDG8 affects the chromatin modification and expression of a transcription factor, CCT, which in turn affects the regulation of carbon responsive genes. In Aim 3B we showed that C and N metabolism is perturbed in the sdg8 mutant through differences in the chlorophyll content, free amino acids, and N uptake. Project activities In Aim 1, we uncovered that the histone methyltransferase SDG8 affects chromatin modification in response to N treatments. The N treatments causes changes in the H3K36me3 distribution off ~200 genes in WT plants, while the N-triggered changes in H3K36me3 of these genes is abrogated in the sdg8-5 mutant. The functional annotation of these 200 genes shows significant enrichment of N-related pathways. Overall, our study suggests that SDG8 mediates chromatin-level adaption of functionally relevant genes in response to changes in N. In Aim 2, we tested if SDG8 plays a role in transcriptional elongation and/or RNA processing. We detected more N-triggered isoform shifting events in the sdg8-5 mutant than in WT, which provides supports for SDG8 being required for proper RNA processing in response to N signaling. In Aim 3, we identified the gene network controlled by a CCT transcription factor under the epigenetic control of SDG8. Isoform abundance of the CCT protein is regulated by SDG8, which leads to an altered response of energy related genes to N level changes. Using physiological, metabolic and genetic approaches, we found that responses to N, including chlorophyll accumulation, N uptake and free amino acids, are altered in sdg8 mutants. Finally, this project provided training to a high school student who performed experiments on the complex interaction of photosynthesis and N availability. The student was named a semi-finalist in both the Regeneron and Siemens National Science competitions for her work.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

On the Performance of Detecting Injection of Fabricated Messages into the CAN Bus

There have been several public demonstrations of attacks on connected vehicles showing the ability of an attacker to take control of a targeted vehicle by injecting messages into their Controller Area Network (CAN) bus. In this article, using injected speed reading and Revolutions Per Minute (RPM) reading messages in in-motion vehicle, we examine the ability of the Pearson correlation and the unsupervised learning methods k-means clustering and Hidden Markov Model (HMM) to differentiate ’no-attack’ and ’under-attack’ states of the given vehicle. We found that the Pearson correlation distinguishes the two states, the k-means clustering method has an acceptable accuracy but high false positive rate and HMM detects attacks with acceptable detection rate but has a high false positive in detecting attacks from speed readings when there is no attack. The accuracy of these unsupervised learning methods are comparable to the ones of the supervised learning methods used by CAN bus Intrusion Detection System (IDS) suppliers. In addition, the article shows that studying CAN anomaly detection techniques using off-vehicle test facilities may not properly evaluate the performance of the detection techniques. Finally, the results suggest using other features besides the data content of the CAN messages and integrate knowledge about how the Electronic Control Units (ECUs) collaborate in building effective techniques for the detection of injection of fabricated message attacks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Classification of Local Seismic Events in the Utah Region: A Comparison of Amplitude Ratio Methods with a Spectrogram-Based Machine Learning Approach

Abstract The capability to discriminate low-magnitude earthquakes from low-yield anthropogenic sources, both detectable only at local distances, is of increasing interest to the event monitoring community. Here, we used a dataset of seismic events in Utah recorded during a 14-day period (1–14 January 2011) by the University of Utah Seismic Stations network to perform a comparative study of event classification at local scale using amplitude ratio (AR) methods and a machine learning (ML) approach. The event catalog consists of 7377 events with magnitudes M C ranging from –2 and lower up to 5.8. Events were subdivided into six populations based on location and source type: tectonic earthquakes (TEs), mining-induced events (MIEs), and mining blasts from four known mines (WMB, SMB, LMB, and CQB). The AR approach jointly exploits Pg-to-Sg phase ARs and Rg-to-Sg spectral ARs in multivariate quadratic discriminant functions and was able to classify 370 events with high signal quality from the three groups with sufficient size (TE, MIE, and SMB). For that subset of the events, the method achieved success rates between about 80% and 90%. The ML technique used trained convolutional neural network (CNN) models to classify the populations. The CNN approach was able to classify the subset of events with accuracies between about 91% and 98%. Because the neural network approach does not have a minimum signal quality requirement, we applied it to the entire event catalog, including the abundant extremely low-magnitude events, and achieved accuracies of about 94%–100%. Furthermore, we compare the AR and ML methodologies using a broad set of criteria and conclude that a major advantage to ML methods is their robustness to low signal-to-noise ratio data, allowing them to classify significantly smaller events.

58 GEOSCIENCES↗
DOE OSTI2022

Machine learning prediction on the fractional free volume of polymer membranes

Fractional free volume (FFV) characterizes the microstructural level features of polymers and affects their properties including thermal, mechanical, and separation performance. Experimental measurements and theoretical analyses have been used to quantify the FFV of polymers, but challenges remain because of their limitations. Experimental measurements are laborious and based on semi empirical equations, while Bondi’s group contribution theory involves ambiguities like the determination of van der Waals volume and the choice of factor values in the theoretical equation. To efficiently evaluate the FFV of polymers, this study utilizes high-throughput molecular dynamics (MD) simulations to build a large dataset regarding polymer’s FFV. Based on this large dataset, we further build machine learning (ML) models to establish the composition-structure relation. Inspired by group contribution theory which correlates polymer’s functional groups to FFV, our ML models correlate polymer’s substructures or physico-chemical indexes to FFV. Here, our study first benchmarks the MD simulation protocol to obtain reliable FFV of polymers and then carries out high-throughput MD simulations for more than 6,500 homopolymers and 1,400 polyamides. Such a large and diverse dataset makes the well-trained ML models more generalizable, compared with the group contribution theory. The efficiency of a feed forward neural network model is further demonstrated by applying it to a hypothetical polyimide dataset of more than 8 million chemical structures. The predicted FFVs of hypothetical polyimides are further validated by MD simulations. The obtained FFVs of the 8 million polymers, plus their previously reported gas separation performances, demonstrate the promising capability of ML virtual screening for the discovery of polymer membranes with exceptional permeability/selectivity.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Microphase-Separated Morphologies and Molecular Network Topologies in Multiblock Copolymer Gels

Strong physical gels derived from thermoplastic elastomeric ABA triblock copolymers solvated with a midblock-selective oil continue to find use in increasingly diverse applications requiring highly elastic and mechanically robust soft materials with tunable properties. Here, in this study, we first investigate the morphological characteristics of thermoplastic elastomer gels (TPEGs) derived from a homologous series of linear A(BA) n multiblock copolymers composed of styrene and hydrogenated isoprene repeat units and possessing comparable molecular weight but varying in the number of B-blocks: 1 (triblock), 2 (pentablock), and 3 (heptablock). Small-angle X-ray scattering performed at ambient temperature confirms that (i) increasing hydrogenation reduces the microdomain periodicity of the neat copolymers and (ii) increasing the oil concentration of the TPEGs tends to swell the nanostructure (increasing the periodicity), but concurrently decreases the size of the styrenic micelles, to different extents depending on the molecular architecture. Complementary dissipative particle dynamics simulations reveal the level to which midblock bridging, which is primarily responsible for the elasticity in this class of materials, is influenced by both oil concentration and molecular architecture. Since constrained topological complexity increases with increasing block number, we introduce a midblock conformation index that facilitates systematic classification of the different topologies involved in nearest-micelle bridge formation. Lastly, those possessing at least one bridged and one looped midblock with no dangling ends are found to be the most predominant topologies in the pentablock and heptablock networks.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Thermoelectric Figure of Merit of a Superatomic Crystal Re 6 Se 8 I 2 Monolayer

Superatomic materials are newly emerging candidates for high-performance thermoelectric (TE) devices due to their intrinsic ultralow thermal conductivities. However, the low TE power factor becomes a huge obstacle to reaching the required the dimensionless figure of merit (ZT) values for practical applications. Here, motivated by the recently synthesized superatomic Re 6 Se 8 I 2 monolayer [J. Am. Chem. Soc. 144, 74 (2022)], we study its superior thermoelectric properties by using density functional theory combined with phonon Boltzmann transport theory and deep potential molecular dynamics. We show that the large mass and anharmonic Re-I bonds introduce strong phonon scattering and result in low lattice thermal conductivity of 1.20 W m -1 K -1 at 300 K, while the strong and harmonic Re-Se network ensures the high TE power factor of 4344 μW m -1 K -2 in b direction for n-type doping. This is an order of magnitude higher than that of other cluster-based materials. Due to the low thermal conductivity and high TE power factor, Re 6 Se 8 I 2 exhibits high ZT values of 1.20 (500 K) and 1.43 (900 K) with n-type doping along the b direction among all the cluster-based thermoelectric materials reported so far.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗