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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Effect of Aromatic Solvents Residuals on Electron Mobility of Organic Single Crystals

Balanced electron and hole transport properties are essential for various organic electrical/optoelectrical applications such as organic solar cells, complementary circuits, and light-emitting transistors. However, the electron transport in organic semiconductor lags far behind the hole side, making it with vital significance to seek the factors that limit the electron mobility. Here, the authors demonstrate that the π-conjugated solvents, as essential components in the widely-used solution processing techniques, can significantly suppress the electron transport if they are not properly removed and trapped as residuals. Single crystals of typical p-type materials exhibit a transition from p-type behavior to ambipolar conductance via reducing the amount of solvent residuals, and the same effect is confirmed by varied p-type materials and solvents. The highest electron mobility reaches 0.027 cm 2 V –1 s –1 and 0.029 cm 2 V –1 s –1 for 6,13-bis(triisopropylsilylethynyl)pentacene and 5,15-bis(triisopropylsilylethynyl)tetrabenzoporphyrin, respectively. Finally, this work discloses the non-negligible existence of solvent residuals even in high quality, long-range ordered organic single crystals, and further provides efficient strategies to harvest the n-type behaviors of organic semiconductor materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Enabling energy‐efficient manufacturing of pharmaceutical solid oral dosage forms via integrated techno‐economic analysis and advanced process modeling

Abstract The global pharmaceutical industry is a trillion‐dollar market. However, the pharmaceutical sector often lags in manufacturing innovation and automation which limits its potential to maximize energy efficiency. The integration of techno‐economic analysis (TEA) with advanced process models as part of an overarching smart manufacturing platform, can help industries create business models, which can be adapted for manufacturing to reduce energy consumption and operating costs while ensuring product quality which can further enable a more sustainable process operation. In this study, a rational design of experiment on three unit‐operations (wet granulation, drying, and milling) was performed on a batch (case 1) and continuous (case 2) pharmaceutical process to obtain experimental data. Process models for predicting product quality and energy efficiency of each of the three‐unit operations were developed. The experimental data were used to validate the models and good agreement was observed. The energy consumption of each unit operation was calculated using statistical models relating the power consumption and the process parameters. The developed process models and energy models were further integrated into a TEA framework, which quantified the energy and monetary cost of manufacturing for both batch and continuous manufacturing cases. With this integrated framework, energy costs savings of ~33% was obtained in the continuous manufacturing process (case 2) over the batch process (case 1).

Sampat, Chaitanya↗
DOE OSTI2021

Polaron Delocalization in Donor–Acceptor Polymers and its Impact on Organic Electrochemical Transistor Performance

Abstract Donor–acceptor (D‐A) polymers are promising materials for organic electrochemical transistors (OECTs), as they minimize detrimental faradaic side‐reactions during OECT operation, yet their steady‐state OECT performance still lags far behind their all‐donor counterparts. We report three D‐A polymers based on the diketopyrrolopyrrole unit that afford OECT performances similar to those of all‐donor polymers, hence representing a significant improvement to the previously developed D‐A copolymers. In addition to improved OECT performance, DFT simulations of the polymers and their respective hole polarons also reveal a positive correlation between hole polaron delocalization and steady‐state OECT performance, providing new insights into the design of OECT materials. Importantly, we demonstrate how polaron delocalization can be tuned directly at the molecular level by selection of the building blocks comprising the polymers’ conjugated backbone, thus paving the way for the development of even higher performing OECT polymers.

Moser, Maximilian↗
DOE OSTI2025

Aqueous Zr/Hf IV ‐Oxo Cluster Speciation and Separation

Abstract Many industrial separations of chemically‐similar elements are achieved by solvent extraction, exploiting differences in speciation and solubility across aqueous‐organic interfaces. We recently identified [OM 4 (OH) 6 (SCN) 12 ] 4− (OM 4 , M=Zr/Hf IV ) tetrahedral oxoclusters as the main species in industrial processes that produce nuclear‐grade Zr and Hf from crude ore. However, isostructural/isoelectronic OM 4 ‐oxoclusters do not explain selective extraction of Hf into the organic phase. Here we have characterized heterometal Hf−Zr clusters in solution and the solid‐state yielding key information about their fundamentally different chemistry that engenders separation. Clusters prepared with both ammonium (industrial process) and tetramethylammonium counter cations revealed that 1) heterometal clusters (instead of a mixture of homometal clusters) assemble, and 2) Hf‐rich OM 4 selectively precipitates over Zr‐rich OM 4 , providing a separation process that does not require an organic extractant. Mass spectrometry, small‐angle X‐ray scattering, solution‐state 1 H nuclear magnetic resonance (NMR) spectroscopy, and solid‐state 17 O NMR evidence both mixed‐metal speciation and selective Hf‐precipitation. Raman spectroscopy suggests greater Zr‐ligand lability than Hf‐ligand lability, consistent with higher aqueous solubility of Zr‐rich clusters, enabling both extraction and precipitation‐based separation. Fundamentally, we also identify a key difference between these chemically similar elements that has enabled diversification of Zr‐polyoxocation chemistry over the last decade, while Hf‐polyoxocation chemistry lags.

Roseborough, Alexander [Department of Chemistry Or↗
DOE OSTI2020

Selective Enrichment Coupled with Proteomics to Identify S-Acylated Plasma Membrane Proteins in Arabidopsis

Protein S-acylation, predominately in the form of palmitoylation, is a reversible lipid post-translational modification (PTM) on cysteines that plays important roles in protein localization, trafficking, activity, and complex assembly. The functions and regulatory mechanisms of Sacylation have been extensively studied in mammals, owing to remarkable development of the high-resolution proteomics and the discovery of the S-acylation-related enzymes. However, the advance of S-acylation studies in plants lags farther behind than that in mammals, mainly due to the lack of knowledge about proteins responsible for this process, such as protein acyltransferases (PATs) and their substrates. In this article, a systematic protocol to study global S-acylation in Arabidopsis seedlings is described. The procedures are presented in detail, including preparation of Arabidopsis seedlings, enrichment of plasma membrane (PM) proteins, followed by enrichment of S-acylated proteins/peptides based on the acyl-biotin exchange (ABE) method, and large-scale identification of S-acylated proteins/peptides via mass spectrometry. This approach enables researchers to study S-acylation of PM proteins in plants in a systematic and straightforward way. Basic protocol 1: Preparation of Arabidopsis seedling materials Basic protocol 2: Isolation and enrichment of plasma membrane proteins Support protocol 1: Determination of protein concentration using BCA assay Basic Protocol 3: Enrichment of S-acylated proteins by ABE method Support protocol 2: Protein precipitation by methanol/chloroform method Basic Protocol 4: Trypsin digestion and proteomic analysis Alternate protocol 1: Pre-resin digestion and peptide level enrichment.

S-acylation, palmitoylation, plasma membrane prote↗
DOE OSTI2021

State changes: insights from the U.S. Long Term Ecological Research Network

Understanding the complex and unpredictable ways ecosystems are changing and predicting the state of ecosystems and the services they will provide in the future requires coordinated, long-term research. This paper is a product of a U.S. National Science Foundation funded Long Term Ecological Research (LTER)network synthesis effort that addressed anticipated changes in future populations and communities. Each LTER site described what their site would look like in50 or 100 yr based on long-term patterns and responses to global change drivers in each ecosystem. Common themes emerged and predictions were grouped into state change, connectivity, resilience, time lags, and cascading effects. Here, we report on the “state change” theme, which includes examples from the Georgia Coastal (coastal marsh), Konza Prairie (mesic grassland), Luquillo(tropical forest), Sevilleta (arid grassland), and Virginia Coastal (coastal grassland) sites. Ecological thresholds(the point at which small changes in an environmental driver can produce an abrupt and persistent state change in an ecosystem quality, property, or phenomenon) were most commonly predicted. For example, in coastal ecosystems, sea-level rise and climate change could convert salt marsh to mangroves and coastal barrier dunes to shrub thicket. Reduced fire frequency has converted grassland to shrubland in mesic prairie, whereas overgrazing combined with drought drive shrub encroachment in arid grasslands. Lastly, tropical cloud forests are susceptible to climate-induced changes in cloud base altitude leading to shifts in species distributions. Overall, these examples reveal that state change is a likely outcome of global environmental change across adverse range of ecosystems and highlight the need for long-term studies to sort out the causes and consequences of state change. The diversity of sites within the LTER network facilitates the emergence of overarching concepts about state changes as an important driver of ecosystem structure, function, services, and futures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Tidal frequencies and quasiperiodic subsurface water level variations dominate redox dynamics in a salt marsh system

Salt marshes are hotspots of nutrient processing en route to sensitive coastal environments. Whilst our understanding of these systems has improved over the years, we still have limited knowledge of the spatiotemporal variability of critical biogeochemical drivers within salt marshes. Sea-level rise will continue to force change on salt marsh functioning, highlighting the urgency of filling this knowledge gap. Our study was conducted in a central California estuary experiencing extensive marsh drowning and relative sea-level rise, making it a model system for such an investigation. Here we instrumented three marsh positions subjected to different degrees of tidal inundation (6.7%, 8.9%, and 11.2% of the time for the upper, middle, and lower marsh positions, respectively), providing locations with varied biogeochemical characteristics and hydrological interactions at the site. We continuously monitored redox potential (Eh) at depths of 0.1, 0.3, and 0.5 m, subsurface water levels (WL), and temperature at 0.7 m depth at each marsh position. To understand how drivers of subsurface biogeochemical processes fluctuate across tidal cycles, we used wavelet analyses to explain the interactions between Eh and WL. We found that tidal forcing significantly affects key drivers of biogeochemical processes by imparting controls on Eh variability, likely driving subsurface hydro-biogeochemistry of the salt marsh. Wavelet coherence showed that the Eh-WL relationship is nonlinear, and their lead–lag relationship is variable. We found that precipitation events perturb Eh at depth over timescales of hours, even though WL shows relatively minimal change during events. Furthermore, this work highlights the importance of high frequency in situ measurements, such as Eh, to help explain factors that govern subsurface biogeochemistry and hydrological processes in salt marshes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Low synchronization Gram–Schmidt and generalized minimal residual algorithms

The Gram–Schmidt process uses orthogonal projection to construct the A = QR factorization of a matrix. When Q has linearly independent columns, the operator P = I - Q(QTQ)-1QT defines an orthogonal projection onto Q⊥. In finite precision, Q loses orthogonality as the factorization progresses. A family of approximate projections is derived with the form P = I - QTQT, with correction matrix T. When T = (QTQ)-1, and T is triangular, it is postulated that the best achievable orthogonality is $\mathcal{O}(ε)\mathcal{K}(A)$. We present new variants of modified (MGS) and classical Gram–Schmidt algorithms that require one global reduction step. An interesting form of the projector leads to a compact WY representation for MGS. In particular, the inverse compact WY MGS algorithm is equivalent to a lower triangular solve. Our main contribution is to introduce a backward normalization lag into the compact WY representation, resulting in a $\mathcal{O}(ε)\mathcal{K}[r_0, AV_m])$ stable Generalized Minimal Residual Method (GMRES) algorithm that requires only one global reduce per iteration. Finally, further improvements in performance are achieved by accelerating GMRES on GPUs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Interface flux recovery framework for constructing partitioned heterogeneous time-integration methods

A common approach for the development of partitioned schemes employing different time integrators on different subdomains is to lag the coupling terms in time. This can lead to accuracy issues, especially in multistage methods. Here, in this article, we present a novel framework for partitioned heterogeneous time-integration methods, which allows the coupling of arbitrary multistage and multistep methods without reducing their order of accuracy. At the core of our approach are accurate estimates of the interface flux obtained from the Schur complement of an auxiliary monolithic system. We use these estimates to construct a polynomial-in-time approximation of the interface flux over the current time coupling window. This approximation provides the interface boundary conditions necessary to decouple the subdomain problems at any point within the coupling window. In so doing our framework enables a flexible choice of time-integrators for the individual subproblems without compromising the time-accuracy at the coupled problem level. This feature is the main distinction between our framework and other approaches. To demonstrate the framework, we construct a family of partitioned heterogeneous time-integration methods, combining multistage and multistep methods, for a simplified tracer transport component of the coupled air-sea system in Earth system models. We report numerical tests evaluating accuracy and flux conservation for different pairs of time-integrators from the explicit Runge-Kutta and Adams-Moulton families.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Permeation of CO 2 and N 2 through glassy poly(dimethyl phenylene) oxide under steady- and presteady-state conditions

Glassy polymers are often used for gas separations because of their high selectivity. Although the dual-mode permeation model correctly fits their sorption and permeation isotherms, its physical interpretation is disputed, and it does not describe permeation far from steady state, a condition expected when separations involve intermittent renewable energy sources. To develop a more comprehensive permeation model, we combine experiment, molecular dynamics, and multiscale reaction–diffusion modeling to characterize the time-dependent permeation of N 2 and CO 2 through a glassy poly(dimethyl phenylene oxide) membrane, a model system. Simulations of experimental time-dependent permeation data for both gases in the presteady-state and steady-state regimes show that both single- and dual-mode reaction–diffusion models reproduce the experimental observations, and that sorbed gas concentrations lag the external pressure rise. The results point to environment-sensitive diffusion coefficients as a vital characteristic of transport in glassy polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits

Abstract Plant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency of genetic improvement in future breeding cycles. However, generating the genetic marker data required to apply genomic prediction at the early stages of a breeding program remains costly for many public‐sector breeding programs as well as for many plant breeders operating in developing countries. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties requires plant breeders to make selection decisions without knowing the future environments those crop varieties will encounter in farmers’ fields. Therefore, both lower cost and higher accuracy methods for prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the 21 st century. To address this challenge, we conducted linked yield trials of 752 public maize ( Zea mays ) genotypes in two distinct environments. We developed and trained a phenotypic prediction model to predict yield from manually scored plant traits. The phenotypic prediction approach we employed outperformed genomic prediction in predicting yields in a second environment, with 8.7%–63% higher R 2 and 4%–13% less root mean square error than the genomic prediction. The phenotypic prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations, such as programs in the developing world, breeding programs for specialty crops, and public sector programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Warming of the lower Columbia River , 1853 to 2018

Abstract Water temperature is a critical ecological indicator; however, few studies have statistically modeled century‐scale trends in riverine or estuarine water temperature, or their cause. Here, we recover, digitize, and analyze archival temperature measurements from the 1850s onward to investigate how and why water temperatures in the lower Columbia River are changing. To infill data gaps and explore changes, we develop regression models of daily historical Columbia River water temperature using time‐lagged river flow and air temperature as the independent variables. Models were developed for three time periods (mid‐19th, mid‐20th, and early 21st century), using archival and modern measurements (1854–1876; 1938–present). Daily and monthly averaged root‐mean‐square errors overall are 0.89°C and 0.77°C, respectively for the 1938–2018 period. Results suggest that annual averaged water temperature increased by 2.2°C ± 0.2°C since the 1850s, a rate of 1.3°C ± 0.1°C/century. Increased water temperatures are seasonally dependent. An increase of approximately 2.0°C ± 0.2°C/century occurs in the July–Dec time‐frame, while springtime trends are statistically insignificant. Rising temperatures change the probability of exceeding ecologically important thresholds; since the 1850s, the number of days with water temperatures over 20°C increased from ~5 to 60 per year, while the number below 2°C decreased from ~10 to 0 days/per year. Overall, the modern system is warmer, but exhibits less temperature variability. The reservoir system reduces sensitivity to short‐term atmospheric forcing. Statistical experiments within our modeling framework suggest that increased water temperature is driven by warming air temperatures (~29%), altered river flow (~14%), and water resources management (~57%).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Pyrrole Modified 3,4‐Propylenedioxythiophene Conjugated Polymer as Hole Transport Layer for Efficient and Stable Perovskite Solar Cells

Despite the outstanding electric properties and cost-effectiveness of poly(3,4-ethylenedioxythiophene) (PEDOT) and its derivatives, their performance as hole transport layer (HTL) materials in conventional perovskite solar cells (PSCs) has lagged behind that of widely used spirobifluorene-based molecules or poly(triaryl amine). This gap is mainly from their poor solubility and energy alignment mismatch. In this work, the design and synthesis of a pyrrole-modified HTL (PPr) based on 3,4-propylenedioxythiophene (ProDOT) are presented for efficient and stable PSCs. As a result of the superior defects passivation ability, excellent contact with perovskite, enhanced hole extraction, and high hydrophobicity, the unencapsulated PPr-based PSCs showed the peak PCE of 21.49% and outstanding moisture stability (over 4000 h). This work highlights the potential application of ProDOT-based materials as HTL for PSCs and underscores the importance of the rational design of PEDOT and its derivatives.

14 SOLAR ENERGY↗
DOE OSTI2024

Autonomous Synthesis of Thin Film Materials with Pulsed Laser Deposition Enabled by In Situ Spectroscopy and Automation

Autonomous systems that combine synthesis, characterization, and artificial intelligence can greatly accelerate the discovery and optimization of materials, however platforms for growth of macroscale thin films by physical vapor deposition techniques have lagged far behind others. Here this study demonstrates autonomous synthesis by pulsed laser deposition (PLD), a highly versatile synthesis technique, in the growth of ultrathin WSe 2 films. Further, by combing the automation of PLD synthesis and in situ diagnostic feedback with a high-throughput methodology, this study demonstrates a workflow and platform which uses Gaussian process regression and Bayesian optimization to autonomously identify growth regimes for WSe 2 films based on Raman spectral criteria by efficiently sampling 0.25% of the chosen 4D parameter space. With throughputs at least 10x faster than traditional PLD workflows, this platform and workflow enables the accelerated discovery and autonomous optimization of the vast number of materials that can be synthesized by PLD.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optimizing Vertical Crystallization for Efficient Perovskite Solar Cells by Buried Composite Layers

Planar-heterojunction perovskite solar cells (PSCs) have experienced rapid evolution in recent years because of the low-temperature processing, suitable alignment, and high mobility of the tin oxide buried contact layer. However, improper SnO 2 surface states and poor crystallinity of the top perovskite films are still the main obstacles for the planar PSCs in which performance always lags behind their mesoporous counterparts. Herein, a new buried contact is reported by introducing graphitic carbon nitride (g-C 3 N 4 ) into the commonly used SnO 2 which performs outstanding transmittance, conductivity, and surface states for a high-quality electron-transporting layer. Moreover, the vertical composition and crystallinity of the top perovskite film are manipulated by rich amino groups on the edge of the g-C 3 N 4 nanosheets which induce the prenucleation of the lead-rich species at the buried interface. Benefiting from the high-quality buried contacts and the optimized perovskite layers, the resultant PSCs achieve a champion efficiency of 21.5% with all photovoltaic parameters enhanced in comparison with their control counterparts (<20%).

planar-heterojunction perovskite solar cells↗
DOE OSTI2026

Influence of plateau, slope, and valley on soil hydrology during the dry season in a Central Amazon old‐growth forest

Soil moisture regulates plant water supply and drought sensitivity in tropical forests, yet its vertical and topographic variation remains poorly characterized. We combined high-frequency time-domain reflectometry measurements from 5 to 100 cm across plateau, slope, and valley landforms at the Zona Florestal 2 research site north of Manaus, Central Amazonia, to quantify how soil moisture memory, timing of responses to rainfall, dry-down rates (τ), and soil–water depletion vary across these contrasting landforms. Landform-specific soil moisture calibration curves ensured accurate volumetric water content estimates in these highly weathered soils. During the 2023 dry-to-wet transition (August–November), soil moisture memory showed strong topographic contrasts, with valley profiles increasing from ∼47 h at 5 cm to ∼154 h at 100 cm, while plateaus exhibited higher near-surface persistence (∼124 h at 5 cm) but weaker memory at depth. Dry-down behavior reinforced these differences as valley soils exhibited τ values exceeding ∼200 h, more than double the characteristic τ of plateau soils (∼90 h). Rainfall–soil moisture correlations indicated immediate responses at shallow depths in valleys and progressively longer lags with depth on plateaus and slopes. These hydrologic patterns were mirrored in depletion profiles, which declined sharply below 30 cm on plateaus but remained high and sustained throughout the upper meter in slopes and valleys. Together, these findings provide the first depth-resolved field measurements of soil moisture memory, rainfall coupling, dry-down constants, and depletion dynamics across major upland landforms in Central Amazonia and offer clear observational benchmarks for improving land-surface and ecosystem model representations of soil–water processes.

Hillslope↗
DOE OSTI2023

Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing

Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their development in the lab. This means that material and process development proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning's unparalleled pattern matching capability to pursue data-driven experimental design. DPC takes two possible experiment parameter sets and outputs a prediction of which will produce a material with a more desirable property specified by the operator. We demonstrate the success of DPC on AA7075 tube manufacturing process and mechanical property data using shear assisted processing and extrusion (ShAPE), an emerging solid phase processing technology. We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.

advanced manufacturing, machine learning, ShAPE↗