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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Interactive statistical decision tools (GiSdT): An open source software integration of decision analysis and stakeholder judgment for sustainable waste management, disposal and remediation - 15650

The vast majority of remediation and waste management decisions are made without quantitative consideration of economic and socio-political factors. They are made instead based on quantitative metrics of human health risk. Sustainable decisions need to be made based on all three 'pillars of sustainability' (economics, environment and social), and require understanding and characterization of the costs and values associated with each pillar. In addition, such decisions need to conform to regulatory or other legal requirements, which often constrains the decision space of interest. Although efforts are often made to include factors across all three pillars (for example, for decisions made under CERCLA), these efforts are usually qualitative, and hence difficult to defend. They lack technical defensibility, transparency and traceability. The purpose of GiSdT (Guided Interactive Statistical Decision Tools) is to provide a quantitative framework whereby all aspects of a decision problem, such as remediation and waste management decisions, can be addressed quantitatively, and hence, defensibly, transparently and traceably. Most decision-makers do not currently have access to useful or usable methods and approaches when are presented with choices that have significant impacts across all three pillars of sustainability. The goal of GiSdT is to provide that access by identifying or developing effective and user-friendly decision methods and approaches that empower decision-makers to explicitly and routinely incorporate all aspects of sustainability into their decision-making. To identify and develop these methods and approaches, GiSdT provides the tools needed for decision-makers and stakeholders to understand and characterize their knowledge of their current decision-making processes, and what methods and approaches they need to pro-actively and quantitatively address all aspects of sustainability in their decisions. GiSdT is an open source interactive framework program that provides the tools needed to address the relevant components of a sustainability-based decision problem, including describing the decision landscape, translating that landscape into goals and objectives, valuing the measurable attributes that describe each objective, identifying decision options and addressing the uncertainty in the attributes (e.g., human health risk, institutional controls) through appropriate probabilistic models. In addition to addressing these quantitative technical components, the GiSdT program is aimed at facilitating stakeholder involvement so that the perspectives, values and objectives are all relevant stakeholders are addressed explicitly in the decision making process. The technical underpinnings of our software program, called Guided Interactive Statistical Decision Tools (GiSdT), are described. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Unsupervised learning approaches to characterizing heterogeneous samples using X-ray single-particle imaging

One of the outstanding analytical problems in X-ray single-particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and the fact that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. Proposed here are two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA), provides a rough classification which is essentially parameter free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs), can generate 3D structures of the objects at any point in the structural landscape. Both these methods are implemented in combination with the noise-tolerant expand–maximize–compress (EMC) algorithm and its utility is demonstrated by applying it to an experimental dataset from gold nanoparticles with only a few thousand photons per pattern. Both discrete structural classes and continuous deformations are recovered. These developments diverge from previous approaches of extracting reproducible subsets of patterns from a dataset and open up the possibility of moving beyond the study of homogeneous sample sets to addressing open questions on topics such as nanocrystal growth and dynamics, as well as phase transitions which have not been externally triggered.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Designing a graph-based approach to landscape ecological assessment of linear infrastructures

The development of major linear infrastructures contributes to landscape fragmentation and impacts natural habitats and biodiversity in various ways. To anticipate and minimize such impacts, landscape planning needs to be capable of effective strategic environmental assessment (SEA) and of supporting environmental impact assessment (EIA) decisions. To this end, species distribution models (SDMs) are an effective way of making predictive maps of the presence of a given species. In this paper, we propose to combine SDMs and graph-based representation of landscape networks to integrate the potential long-distance effect of infrastructures on species distribution. A diachronic approach, comparing distribution before and after the linear infrastructure is constructed, leads to the design of a species distribution assessment (SDA), taking into account population isolation. The SDA makes it possible (1) to estimate the local variation in probability of presence and (2) to characterize the impact of the infrastructure in terms of global variation in presence and of distance of disturbance. The method is illustrated by assessing the impact of the construction of a high-speed railway line on the distribution of several virtual species in Franche-Comté (France). The study shows the capacity of the SDA to characterize the impact of a linear infrastructure either as a research concern or as a spatial planning challenge. SDAs could be helpful in deciding among several scenarios for linear infrastructure routes or for the location of mitigation measures. -- Highlights: • Graph connectivity metrics were integrated into a species distribution model. • SDM was performed before and after the implementation of linear infrastructure. • The local variation of presence provides spatial indicators of the impact.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Regional Differences in the Structure of Juglans nigra Phytobiome Reflect Geographical Differences in Thousand Cankers Disease Severity

Thousand cankers disease threatens Juglans nigra (Eastern Black Walnut) in urban and natural landscapes. Incidence and severity of thousand cankers disease is higher in the host’s introduced range in the western United States. We hypothesized that these differences are driven partly by geographical variation in the host phytobiome due to its roles in host stress tolerance, nutrient acquisition, and defense. To evaluate the role of the phytobiome in mediating thousand cankers disease, we characterized the J. nigra phytobiome of diseased and healthy trees in portions of its native (Indiana and Tennessee) and introduced (Washington) ranges. Grafted clones present in each state and open-pollinated populations were sampled. DNA was extracted from soil and branch (caulosphere) tissues and internal transcribed spacer and 16s regions were sequenced for characterization of fungal and bacterial communities. We found that microbial communities in the caulosphere and soil differ between native and introduced ranges of J. nigra and harbor different mutualistic and pathogenic microorganisms. Additionally, caulosphere microbial communities were more species rich and diverse in the native range of J. nigra, suggesting greater levels of functional redundancy and multifunctionality in the native-range phytobiome compared with the introduced range. We also found higher network complexity in the caulosphere of trees in the introduced range and evidence for two alternative stable community states associated with diseased and healthy trees. Our results provide support for the hypothesis that geographical variation in thousand cankers disease incidence and severity is partially driven by differences in the phytobiome of J. nigra in its introduced and native ranges.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Systematic and scalable genome-wide essentiality mapping to identify nonessential genes in phages

Phages are one of the key ecological drivers of microbial community dynamics, function, and evolution. Despite their importance in bacterial ecology and evolutionary processes, phage genes are poorly characterized, hampering their usage in a variety of biotechnological applications. Methods to characterize such genes, even those critical to the phage life cycle, are labor intensive and are generally phage specific. Here, we develop a systematic gene essentiality mapping method scalable to new phage–host combinations that facilitate the identification of nonessential genes. As a proof of concept, we use an arrayed genome-wide CRISPR interference (CRISPRi) assay to map gene essentiality landscape in the canonical coliphages λ and P1. Results from a single panel of CRISPRi probes largely recapitulate the essential gene roster determined from decades of genetic analysis for lambda and provide new insights into essential and nonessential loci in P1. We present evidence of how CRISPRi polarity can lead to false positive gene essentiality assignments and recommend caution towards interpreting CRISPRi data on gene essentiality when applied to less studied phages. Finally, we show that we can engineer phages by inserting DNA barcodes into newly identified inessential regions, which will empower processes of identification, quantification, and tracking of phages in diverse applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models

We introduce a workflow for the rapid delineation and microtopographic characterization of ice wedge polygons within high-resolution digital elevation models. At the core of the workflow is a convolutional neural network utilized to detect pixels representing polygon boundaries. A watershed transformation is subsequently used to segment imagery into discrete polygons. Fast training times ( <5 min) permit an iterative approach to improving skill as the routine is applied across broad landscapes. Results from study sites near Utqiagvik (formerly Barrow) and Prudhoe Bay, Alaska, demonstrate robus tperformance in diverse tundra settings, with manual validations demonstrating 70–96 % accuracy by area at the kilometer scale. The methodology permits precise, spatially extensive measurements of polygonal microtopography and trough network geometry.

58 GEOSCIENCES↗
NASA NTRS1991

The use of aircraft-based Thermal Infrared Multispectral Scanner (TIMS) data to measure surface energy budgets on a landscape scale

A series of Thermal Infrared Multispectral Scanner Data (TIMS) was collected over the H. J. Andrews experimental forest in western Oregon and at the Coweeta Hydrologic Laboratory in North Carolina. Flight lines were overlapped with an 8 to 28 minute time difference between flight lines. Concurrent radiosonde measurements of atmospheric profiles of air and dew point temperatures provided inputs to LOWTRAN6 for atmospheric radiance corrections of the TIMS data. Surface temperature differences over time between flight lines allowed the development of thermal response numbers (TRN) which characterized the thermal response of the different surface types. The polygons containing mostly soil and bare rock had the lowest TRN whereas the forested polygons were the highest. Results indicate that forest canopy temperatures measured by the TIMS are comparable to needle thermocouples temperatures. ET models developed from the TIMS data obtained similar ET rates as those using energy balance techniques.

Luvall, Jeffrey C.↗
DOE OSTI2019

Soil Stratigraphic Characterization of Soils at Selected Sites at Fort Hunter Liggett and Panamint Valley, California

Soil data were collected from five unique soil profiles at the U.S. Army Fort Hunter Liggett (CA) between April 21-26, 2019. Study sites were selected to allow characterization of river terraces and alluvial fan surfaces at Fort Hunter Liggett (FHL) that reflect past periods of high river discharge or runoff (Figure 1). The selected sites represent five landform units that were deposited during periods of enhanced flow and alluvial sedimentation. These surfaces have been correlated locally along the San Antonio River and are distinguished on the basis of relative landscape position (i.e., height above the valley bottom). The dataset includes field observations of morphological properties and laboratory analyses of both physical and chemical attributes of each soil type that are used to document soil characteristics and inform dating techniques.

58 GEOSCIENCES↗
DOE OSTI2023

Identification of Colletotrichum aenigma as the new causal agent of leaf blight disease on Aucuba japonica Thunb., and screenings of effective fungicides for its sustainable management

Aucuba japonica Thunb is an evergreen woody ornamental plant with significant economic and ecological values. It also produces aucubin, showing a variety of biological activities. It is widely planted in the southwest region of China, including karst landscape areas in Guizhou Province. In January 2022, a serious leaf blight disease was observed on the leaves of A. japonica in the outdoor gardens of Guizhou University, Guiyang, Guizhou, China. The causal agent was identified as Colletotrichum aenigma through amplification and sequencing of the internal transcribed spacer (ITS) region, translation of the chitin synthase ( CHS ) and actin ( ACT ) genes, and morphological characterizations. Koch’s postulates were confirmed by its pathogenicity on healthy leaves, including re-isolation and identification. To our knowledge, this is the first report of C. aenigma causing leaf blight on A. japonica worldwide. To identify pathogen characteristics that could be utilized for future disease management, the effects of temperature and light on mycelial growth, conidia production, and conidial germination, and the effects of humidity on conidial germination were studied. Optimal temperatures for mycelial growth of C. aenigma BY827 were 25–30°C, while 15°C and 35°C were favorable for conidia production. Concurrently, alternating 10-h light and 14-h dark, proved to be beneficial for mycelial growth and conidial germination. Additionally, conidial germination was enhanced at 90% humidity. In vitro screenings of ten chemical pesticides to assess their efficacy in suppressing C. aenigma representative strain BY827. Among them, difenoconazole showed the best inhibition rate, with an EC 50 (concentration for 50% of maximal effect) value of 0.0148 μg/ml. Subsequently, field experiment results showed that difenoconazole had the highest control efficiency on A. japonica leaf blight (the decreasing rate of disease incidence and decreasing rate of disease index were 44.60 and 47.75%, respectively). Interestingly, we discovered that C. aenigma BY827 may develop resistance to mancozeb, which is not reported yet among Colletotrichum spp. strains. In conclusion, our study provided new insights into the causal agent of A. japonica leaf blight, and the effective fungicides evaluated provided an important basis and potential resource for the sustainable control of A. japonica leaf blight caused by C. aenigma in the field.

Fan, Ruidong↗
DOE OSTI2019

A distributed temperature profiling method for assessing spatial variability in ground temperatures in a discontinuous permafrost region of Alaska

Soil temperature has been recognized as a property that strongly influences a myriad of hydro-biogeochemical processes and reflects how various properties modulate the soil thermal flux. In spite of its importance, our ability to acquire soil temperature data with high spatial and temporal resolution and coverage is limited because of the high cost of equipment, the difficulties of deployment, and the complexities of data management. Here we propose a new strategy that we call distributed temperature profiling (DTP) for improving the characterization and monitoring near-surface thermal properties through the use of an unprecedented number of laterally and vertically distributed temperature measurements. We developed a prototype DTP system, which consists of inexpensive, low-impact, low-power, and vertically resolved temperature probes that independently and autonomously record soil temperature. The DTP system concept was tested by moving sequentially the system across the landscape to identify near-surface permafrost distribution in a discontinuous permafrost environment near Nome, Alaska, during the summertime. Results show that the DTP system enabled successful acquisition of vertically resolved profiles of summer soil temperature over the top 0.8 m at numerous locations. DTP also enabled high-resolution identification and lateral delineation of near-surface permafrost locations from surrounding zones with no permafrost or deep permafrost table locations overlain by a perennially thawed layer. The DTP strategy overcomes some of the limitations associated with – and complements the strengths of – borehole-based soil temperature sensing as well as fiber-optic distributed temperature sensing (FO-DTS) approaches. Combining DTP data with co-located topographic and vegetation maps obtained using unmanned aerial vehicle (UAV) and electrical resistivity tomography (ERT) data allowed us to identify correspondences between surface and subsurface property distribution and in particular between topography, vegetation, shallow soil properties, and near-surface permafrost. Finally, the results highlight the considerable value of the newly developed DTP strategy for investigating the significant variability in and complexity of subsurface thermal and hydrological regimes in discontinuous permafrost regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Support for Next-Generation Ecosystem Experiments (NGEE Arctic) Field Campaign Report

An important challenge for Earth System Models (ESMs) is to represent land surface and subsurface processes and their complex interactions in a warming climate. This is especially important for arctic ecosystems where permafrost extent, topography, hydrology, vegetation, disturbance, and biogeochemistry are inextricably linked. The implications of such linkages include permafrost thaw and deepening of the active layer, microbial decomposition of vulnerable soil organic matter, altered productivity and migration of tall woody shrubs, and watershed-scale changes in surface and groundwater transport and storage. Although ESMs describe some of these interactions for high-latitude ecosystems, their representation requires extensive confrontation with field and laboratory observations to test and improve models, and to use those models to inspire new observations and experiments. The Next-Generation Ecosystem Experiments (NGEE Arctic) is a 10-year project (2012 to 2022) to improve our predictive understanding of carbon-rich arctic system processes and feedbacks to climate. This is achieved through experiments, observations, and synthesis of existing data sets that strategically inform model process representation and parameterization, and that enhance the knowledge base required for model initialization, calibration, and evaluation. One question of special interest to the NGEE Arctic project addresses how above- and below-ground plant functional traits might change across environmental gradients, and what are the consequences for arctic ecosystem C, water, and nutrient fluxes? Arctic plant traits, and their variation in response to changing environmental conditions, will play a key role in the response of tundra ecosystems to warming, permafrost thaw, and the wetter or drier conditions expected in the future. The appropriate representation of these functional traits in models is necessary to accurately represent ecosystem C, water, and nutrient cycling in tundra ecosystems, now and in the future. We characterized the variation in above- and below-ground plant traits in response to varying edaphic and environmental conditions in Utqia&gdot;vik (formerly Barrow), Alaska. These new data, combined with remote-sensing and synthesis activities across the Arctic and the globe, have been used to inform model structure and parameterization of key processes such as photosynthesis, nutrient dynamics, and trait variation across the landscape.

North Slope of Alaska, arctic processes (meteorolo↗
DOE OSTI2020

Antecedent conditions determine the biogeochemical response of coastal soils to seawater exposure

Coastal landscapes are increasingly exposed to seawater due to sea level rise and extreme weather events. The biogeochemical responses of these vulnerable ecosystems are poorly understood, limiting our ability to predict how their role in local and global biogeochemical cycles will shift under future conditions. Here we evaluate how antecedent conditions influence the biogeochemical response of soil to seawater inundation events based on a 42-day laboratory incubation experiment with soils collected from a natural salinity gradient across a coastal floodplain. We quantified influences of seawater inundation on intact soil cores through high-frequency carbon dioxide (CO 2 ) and methane (CH 4 ) gas fluxes measurements as well as ultrahigh resolution characterization of organic matter chemistry and metabolites. Mean CO 2 and CH 4 fluxes were higher after inundation compared to control cores for soils that had low in situ salinity. Soils with low in situ salinity also exhibited significant shifts in organic matter profiles after inundation, with surficial soils (0-7.5 cm) becoming more enriched in phenolic compounds, compared to deeper soils (7.5-15 cm). The number of biochemical transformations inferred from mass spectrometry increased significantly after inundation for soils with low in situ salinity, and a trend of decreasing thermodynamic favorability with increasing salinity was observed, suggesting an increase in microbial activity following inundation. Our results suggest that seawater inundation of low-salinity terrestrial environments can lead to increased microbial activity, preferential depletion of more thermodynamically favorable compounds, and increased soil carbon release. Finally, we conclude that the biogeochemical impacts of future seawater exposure will be modulated by antecedent conditions associated with landscape position within coastal watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

The landscape of transposable elements in the finished genome of the fungal wheat pathogen Mycosphaerella graminicola

Background: In addition to gene identification and annotation, repetitive sequence analysis has become an integral part of genome sequencing projects. Identification of repeats is important not only because it improves gene prediction, but also because of the role that repetitive sequences play in determining the structure and evolution of genes and genomes. Several methods using different repeat-finding strategies are available for whole-genome repeat sequence analysis. Four independent approaches were used to identify and characterize the repetitive fraction of the Mycosphaerella graminicola (synonym Zymoseptoria tritici) genome. This ascomycete fungus is a wheat pathogen and its finished genome comprises 21 chromosomes, eight of which can be lost with no obvious effects on fitness so are dispensable. Results: Using a combination of four repeat-finding methods, at least 17% of the M. graminicola genome was estimated to be repetitive. Class I transposable elements, that amplify via an RNA intermediate, account for about 70% of the total repetitive content in the M. graminicola genome. The dispensable chromosomes had a higher percentage of repetitive elements as compared to the core chromosomes. Distribution of repeats across the chromosomes also varied, with at least six chromosomes showing a non-random distribution of repetitive elements. Repeat families showed transition mutations and a CpA → TpA dinucleotide bias, indicating the presence of a repeat-induced point mutation (RIP)-like mechanism in M. graminicola. One gene family and two repeat families specific to subtelomeres also were identified in the M. graminicola genome. A total of 78 putative clusters of nested elements was found in the M. graminicola genome. Several genes with putative roles in pathogenicity were found associated with these nested repeat clusters. This analysis of the transposable element content in the finished M. graminicola genome resulted in a thorough and highly curated database of repetitive sequences. Conclusions: This comprehensive analysis will serve as a scaffold to address additional biological questions regarding the origin and fate of transposable elements in fungi. Future analyses of the distribution of repetitive sequences in M. graminicola also will be able to provide insights into the association of repeats with genes and their potential role in gene and genome evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Airborne measurements of isoprene and monoterpene emissions from southeastern U.S. forests

Isoprene and monoterpene emission rates are essential inputs for atmospheric chemistry models that simulate atmospheric oxidant and particle distributions. Process studies of the biochemical and physiological mechanisms controlling these emissions are advancing our understanding and the accuracy of model predictions but efforts to quantify regional emissions have been limited by a lack of constraints on regional distributions of ecosystem emission capacities. We used an airborne wavelet-based eddy covariance measurement technique to characterize isoprene and monoterpene fluxes with high spatial resolution during the 2013 SAS (Southeast Atmosphere Study) in the southeastern United States. The fluxes measured by direct eddy covariance were comparable to emissions independently estimated using an indirect inverse modeling approach. Isoprene emission factors based on the aircraft wavelet flux estimates for high isoprene chemotypes (e.g., oaks) were similar to the MEGAN2.1 biogenic emission model estimates for landscapes dominated by oaks. Aircraft flux measurement estimates for landscapes with fewer isoprene emitting trees (e.g., pine plantations), were about a factor of two lower than MEGAN2.1 model estimates. The tendency for high isoprene emitters in these landscapes to occur in the shaded understory, where light dependent isoprene emissions are diminished, may explain the lower than expected emissions. This result demonstrates the importance of accurately representing the vertical profile of isoprene emitting biomass in biogenic emission models. Airborne measurement-based emission factors for high monoterpene chemotypes agreed with MEGAN2.1 in landscapes dominated by pine (high monoterpene chemotype) trees but were more than a factor of three higher than model estimates for landscapes dominated by oak (relatively low monoterpene emitting) trees. This results suggests that unaccounted processes, such as floral emissions or light dependent monoterpene emissions, or vegetation other than high monoterpene emitting trees may be an important source of monoterpene emissions in those landscapes and should be identified and included in biogenic emission models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A composite electrodynamic mechanism to reconcile spatiotemporally resolved exciton transport in quantum dot superlattices

Quantum dot (QD) solids are promising optoelectronic materials; further advancing their device functionality requires understanding their energy transport mechanisms. The commonly invoked near-field Förster resonance energy transfer (FRET) theory often underestimates the exciton hopping rate in QD solids, yet no consensus exists on the underlying cause. In response, we use time-resolved ultrafast stimulated emission depletion (STED) microscopy, an ultrafast transformation of STED to spatiotemporally resolve exciton diffusion in tellurium-doped cadmium selenide–core/cadmium sulfide–shell QD superlattices. We measure the concomitant time-resolved exciton energy decay due to excitons sampling a heterogeneous energetic landscape within the superlattice. The heterogeneity is quantified by single-particle emission spectroscopy. This powerful multimodal set of observables provides sufficient constraints on a kinetic Monte Carlo simulation of exciton transport to elucidate a composite transport mechanism that includes both near-field FRET and previously neglected far-field emission/reabsorption contributions. Uncovering this mechanism offers a much-needed unified framework in which to characterize transport in QD solids and additional principles for device design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Assessment of the Biological Impact of Engineered Nanomaterials Using Mass Spectrometry-based MultiOmics Approaches

The widespread use of engineered nanomaterials (ENMs) has expanded further than our understanding of their toxicity, prompting research into the biological responses against exposure to such materials. Genomics and transcriptomics have been extensively used to evaluate the biological effects of exposure to various ENMs. By determining gene activities, these studies provided valuable information to infer how cells respond to the toxicological effects of many ENMs. The application of mass spectrometry (MS)-based omics tools, such as proteomics, lipidomics, and metabolomics, offer post-genomic perspectives of what cellular processes are altered. Individually, these technologies have revealed the proteome, lipidome, and metabolome landscape upon exposure to ENMs. Together, these approaches demonstrate the ENM-induced adaptation in a broad range of cellular processes at multiple levels and the complexity of ENM-cell interactions. As a result, integrating multiple layers of MS-based omics data is trending to complement genomics data. In this review chapter, we discuss the applications of these tools for a comprehensive systems-level characterization of the biological responses induced by engineered nanomaterials.

Engineered nanomaterials, Proteomics, Metabolomics↗
DOE OSTI2021

Thermodynamics and kinetics of H adsorption and intercalation for graphene on 6 H -SiC(0001) from first-principles calculations

Previous experimental observations for H intercalation under graphene on SiC surfaces motivate the clarification of configuration stabilities and kinetic processes related to intercalation. From first-principles density-functional-theory calculations, we analyze H adsorption and intercalation for graphene on a 6H-SiC(0001) surface, where the system includes two single-atom-thick graphene layers: the top-layer graphene (TLG) and the underling buffer-layer graphene (BLG) above the terminal Si layer. Our chemical potential analysis shows that in the low-H coverage regime (described by a single H atom within a sufficiently large supercell), intercalation into the gallery between TLG and BLG or into the gallery underneath BLG is more favorable thermodynamically than adsorption on top of TLG. However, intercalation into the gallery between TLG and BLG is most favorable. We obtain energy barriers of about 1.3 and 2.3 eV for a H atom diffusing on and under TLG, respectively. From an additional analysis of the energy landscape in the vicinity of a step on the TLG, we assess how readily one guest H atom on the TLG terrace can directly penetrate the TLG into the gallery between TLG and BLG versus crossing a TLG step to access the gallery. Finally, we also perform density functional theory calculations for higher H coverages revealing a shift in favorability to intercalation of H underneath BLG and characterizing the variation with H coverage in interlayer spacings.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Computational Analysis of the Energetic Stability of High-Entropy Structures of a Prototypical Lanthanide-Based Metal–Organic Framework

High-entropy materials are characterized by their complex compositions, typically comprising five or more elements in near-equiatomic proportions. Applying this concept to metal ions in metal−organic frameworks (MOFs) has paved the way for exploring a new class of high-entropy MOFs. While the compositional strategy of high-entropy materials leverages configurational entropy to aid thermodynamic stability, it also poses significant analytical challenges due to the vast compositional landscape and diverse phases that these materials can adopt. We present a computational study of several complexities associated with selecting potential high-entropy versions of a prototype lanthanidebased MOF. We compute the energetics of metal mixing of these heterometallic MOFs using density functional theory (DFT) and machine learning interatomic potential (MLIP) methods. The use of MLIP methods allows a systematic exploration of the convex hull of thermodynamically stable MOF structures containing up to 5 distinct metals.

Chemical structure↗