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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Indications of a Decrease in the Depth of Deep Convective Cores with Increasing Aerosol Concentration during the CACTI Campaign

Abstract An aerosol indirect effect on deep convective cores (DCCs), by which increasing aerosol concentration increases cloud-top height via enhanced latent heating and updraft velocity, has been proposed in many studies. However, the magnitude of this effect remains uncertain due to aerosol measurement limitations, modulation of the effect by meteorological conditions, and difficulties untangling meteorological and aerosol effects on DCCs. The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign in 2018–19 produced concentrated aerosol and cloud observations in a location with frequent DCCs, providing an opportunity to examine the proposed aerosol indirect effect on DCC depth in a rigorous and robust manner. For periods throughout the campaign with well-mixed boundary layers, we analyze relationships that exist between aerosol variables (condensation nuclei concentration > 10 nm, 0.4% cloud condensation nuclei concentration, 55–1000-nm aerosol concentration, and aerosol optical depth) and meteorological variables [level of neutral buoyancy (LNB), convective available potential energy, midlevel relative humidity, and deep-layer vertical wind shear] with the maximum radar-echo-top height and cloud-top temperature (CTT) of DCCs. Meteorological variables such as LNB and deep-layer shear are strongly correlated with DCC depth. LNB is also highly correlated with three of the aerosol variables. After accounting for meteorological correlations, increasing values of the aerosol variables [with the exception of one formulation of aerosol optical depth (AOD)] are generally correlated at a statistically significant level with a warmer CTT of DCCs. Therefore, for the study region and period considered, increasing aerosol concentration is mostly associated with a decrease in DCC depth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Freshwater Input and Vertical Mixing in the Canada Basin’s Seasonal Halocline: 1975 versus 2006–12

Abstract The Arctic seasonal halocline impacts the exchange of heat, energy, and nutrients between the surface and the deeper ocean, and it is changing in response to Arctic sea ice melt over the past several decades. Here, we assess seasonal halocline formation in 1975 and 2006–12 by comparing daily, May–September, salinity profiles collected in the Canada Basin under sea ice. We evaluate differences between the two time periods using a one-dimensional (1D) bulk model to quantify differences in freshwater input and vertical mixing. The 1D metrics indicate that two separate factors contribute similarly to stronger stratification in 2006–12 relative to 1975: 1) larger surface freshwater input and 2) less vertical mixing of that freshwater. The larger freshwater input is mainly important in August–September, consistent with a longer melt season in recent years. The reduced vertical mixing is mainly important from June until mid-August, when similar levels of freshwater input in 1975 and 2006–12 are mixed over a different depth range, resulting in different stratification. These results imply that decadal changes to ice–ocean dynamics, in addition to freshwater input, significantly contribute to the stronger seasonal stratification in 2006–12 relative to 1975. These findings highlight the need for near-surface process studies to elucidate the impact of lateral processes and ice–ocean momentum exchange on vertical mixing. Moreover, the results may provide insight for improving the representation of decadal changes to Arctic upper-ocean stratification in climate models that do not capture decadal changes to vertical mixing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine Learning–Adjusted WRF Forecasts to Support Wind Energy Needs in Black Start Operations

Abstract The push for increased capacity of renewable sources of electricity has led to the growth of wind-power generation, with a need for accurate forecasts of winds at hub height. Forecasts for these levels were uncommon until recently, and that, combined with the nocturnal collapse of the well-mixed boundary layer and daytime growth of the boundary layer through the levels important for energy generation, has contributed to errors in numerical modeling of wind generation resources. The present study explores several machine learning algorithms to both forecast and correct standard WRF Model forecasts of winds and temperature at hub height within wind turbine plants over several different time periods that are critical for the anticipation of potential blackouts and aiding in black start operations on the power grid. It was found that mean square error for day-2 wind forecasts from the WRF Model can be improved by over 90% with the use of a multioutput neural network, and that 60-min forecasts of WRF error, which can then be used to adjust forecasts, can be made with an LSTM with great accuracy. Nowcasting of temperature and wind speed over a 10-min period using an LSTM produced very low error and especially skillful forecasts of maximum and minimum values over the turbine plant area.

17 WIND ENERGY↗
DOE OSTI2020

Radiation-Induced Modifications in Copper Oxide Growth

Radiation-induced effects and their influence on oxidation processes were evaluated for their use as a forensic tool for special nuclear material (SNM). A beam of 10 MeV Au³⁺ ions was used to mimic the accumulation of microstructural damage from self-irradiation through the decay of radionuclides. Several copper samples were irradiated as suitable surrogate materials at 200 °C with a flux of 1x10¹² ions/cm·s to damage levels of 5, 10, and 15 displacements per atom (dpa). This corresponds to about 50, 100, and 150 years, respectively, of accumulated α-decay damage in a PuGa alloy assuming a damage rate of 0.1 dpa/year. After irradiation, all samples were exposed to an accelerated aging process induced by thermal treatment at 350 °C for 1 hour in air. This resulted in the growth of a mixed oxide layer (Cu₂O and CuO) which was characterized in detail using several complementary analytical techniques: Scanning Electron Microscopy, Raman spectroscopy, Synchrotron X-ray diffraction (transmission mode), and Grazing Incidence X-ray diffraction. The oxide layer growth of irradiated Cu at 350°C is distinctly modified as a result of the ion irradiation. Most notably, the growth of the CuO phase is suppressed with increasing radiation damage on the Cu substrate, and structural changes occurred in the Cu₂O phase. These results indicate that damage from self-irradiation over time can cause quantifiable modifications in the oxidation process of metals that could be harnessed for their use as a novel forensic tool.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Two bright, optically clear, intrinsic fast-neutron and charged-particle detector materials for neutron imaging and other applications

Two optically clear, bright, scintillating ZnS materials have been identified that, like previous opaque ZnS scintillators, are excellent charged particle detectors. We show that ZnS is a good fast-neutron-to charged-particle converter making optically clear ZnS an intrinsic fast-neutron detector that does not require layering or mixing of converter materials and scintillator materials, and removes the limitations imposed by opaque ZnS scintillator materials used for more than a century. Thermal neutron detection using optically clear 6 LiF or 10 BN and clear ZnS may benefit from improved spatial resolution and light transmission. Properties and tests of clear scintillating ZnS are described. Fast-neutron imaging applications benefit from increased efficiency of these scintillators, minimal scattering in the scintillator, and increased useful detector volume. Charged particle and fast-neutron detectors have numerous applications in nuclear non-proliferation and security, nuclear and particle physics, and non-destructive testing and environmental measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

SPRUCE Vegetation Phenology in Experimental Plots from Phenocam Imagery, 2015-2019

The most recent version of these data are available https://doi.org/10.25581/spruce.100/1874948 and supersedes all previous versions. This dataset consists of PhenoCam data from the SPRUCE experiment from the beginning of whole ecosystem warming in August 2015 through March-31 of 2020, with start- and end-of-season phenological transition dates derived through the end of autumn 2019. Digital cameras, or phenocams, installed in each SPRUCE enclosure track seasonal variation in vegetation “greenness”, a proxy for vegetation phenology and associated physiological activity. Three separate regions of interest (ROIs) were defined for each camera field of view, corresponding to different vegetation types and demarcating: Picea trees (vegetation type EN, for evergreen needleleaf); Larix trees (vegetation type DN, for deciduous needleleaf); and the mixed shrub layer (vegetation type SH).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

SPRUCE Vegetation Phenology in Experimental Plots from Phenocam Imagery, 2015-2020

The most recent version of these data are available https://doi.org/10.25581/spruce.100/1874948 and supersedes all previous versions. This data set consists of PhenoCam data from the SPRUCE experiment from the beginning of whole ecosystem warming (Hanson et al. 2017) in August 2015 through March 31 of 2021, with start- and end-of-season phenological transition dates derived through the end of autumn 2020. Digital cameras, or phenocams, installed in each SPRUCE enclosure track seasonal variation in vegetation “greenness”, a proxy for vegetation phenology and associated physiological activity. Three separate regions of interest (ROIs) were defined for each camera field of view, corresponding to different vegetation types and demarcating (1) Picea trees (vegetation type EN, for evergreen needleleaf); (2) Larix trees (vegetation type DN, for deciduous needleleaf); and (3) the mixed shrub layer (vegetation type SH). User note: A list of previous versions can be found in the Related Datasets section of the user guide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

SPRUCE Vegetation Phenology in Experimental Plots from Phenocam Imagery, 2015-2021

This data set consists of PhenoCam data from the SPRUCE experiment from the beginning of whole ecosystem warming (Hanson et al. 2017) in August 2015 through March 31 of 2022, with start- and end-of-season phenological transition dates derived through the end of autumn 2021. Digital cameras, or phenocams, installed in each SPRUCE enclosure track seasonal variation in vegetation “greenness”, a proxy for vegetation phenology and associated physiological activity. Three separate regions of interest (ROIs) were defined for each camera field of view, corresponding to different vegetation types and demarcating (1) Picea trees (vegetation type EN, for evergreen needleleaf); (2) Larix trees (vegetation type DN, for deciduous needleleaf); and (3) the mixed shrub layer (vegetation type SH). User Note: This dataset supersedes previous versions of SPRUCE Vegetation Phenology in Experimental Plots from Phenocam Imagery. A list of previous versions can be found in the Related Datasets section of the user guide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

SPRUCE Vegetation Phenology in Experimental Plots from Phenocam Imagery, 2015-2022

This data set consists of PhenoCam data from the SPRUCE experiment from the beginning of whole ecosystem warming (Hanson et al. 2017) in August 2015 through March 31 of 2023, with start- and end-of-season phenological transition dates derived through the end of autumn 2022. Digital cameras, or phenocams, installed in each SPRUCE enclosure track seasonal variation in vegetation “greenness”, a proxy for vegetation phenology and associated physiological activity. Three separate regions of interest (ROIs) were defined for each camera field of view, corresponding to different vegetation types and demarcating (1) Picea trees (vegetation type EN, for evergreen needleleaf); (2) Larix trees (vegetation type DN, for deciduous needleleaf); and (3) the mixed shrub layer (vegetation type SH). This data set consists of three sets of data files: 3-day summary product files: One file for each camera and each ROI (i.e. vegetation type), characterizing vegetation color at a 3-day time step Contains 35 files in *.csv format inside a compressed (*.zip) file. Transition date file: Estimates “greenness rising” (spring) and “greenness falling” (autumn) transition dates derived from the smoothed daily green chromatic coordinate (GCC) values, for each camera and each ROI (i.e. vegetation type) Contains one file in *.csv format Snow flag files: Indicate days with snow on trees or snow on ground for each experimental enclosure Contains two files in *.csv format, one for snow on trees and one for snow on ground This data set consists of two sets of companion files: Accompanying HTML files show the 90th quantiles of the mean GCC plotted together with transition dates for each vegetation type and plot. Contains three files in HTML format, one for each vegetation type One additional file in HTML format with the transition dates plotted for each vegetation type, by year R files for processing Phenocam files and flags. Contains five files in R file (*.R) format in one compressed (*.zip) file User Note: All imagery is posted in near-real time to the PhenoCam Project web page (http://phenocam.sr.unh.edu/), where it is publicly available. Scroll to “spruce” in the Gallery or link directly to the 29 SPRUCE cameras at https://tinyurl.com/y7z5mau7. The data reported here are based on the complete camera record from SPRUCE and supersedes the previously released phenocam datasets (see Related Data Sets). The estimated transition dates for previously released datasets may differ slightly (in most cases, by ±3 days or less), because following standard PhenoCam processing protocols (Richardson et al. 2018, Scientific Data), smoothing and interpolation, outlier removal, and transition date estimation are always conducted using the full data record.

SPRUCE Experiment, Marcell Experimental Forest, Sp↗
DOE OSTI2024

SPRUCE Vegetation Phenology in Experimental Plots from Phenocam Imagery, 2015-2023

This data set consists of PhenoCam data from the SPRUCE experiment from the beginning of whole ecosystem warming (Hanson et al. 2017) in August 2015 through March 31 of 2024, with start- and end-of-season phenological transition dates derived through the end of autumn 2023. Digital cameras, or phenocams, installed in each SPRUCE enclosure track seasonal variation in vegetation “greenness”, a proxy for vegetation phenology and associated physiological activity. Three separate regions of interest (ROIs) were defined for each camera field of view, corresponding to different vegetation types and demarcating (1) Picea trees (vegetation type EN, for evergreen needleleaf); (2) Larix trees (vegetation type DN, for deciduous needleleaf); and (3) the mixed shrub layer (vegetation type SH). This data set consists of three sets of data files: (1) 3-day summary product files: One file for each camera and each ROI (i.e. vegetation type), characterizing vegetation color at a 3-day time step • Contains 36 files in *.csv format inside a compressed (*.zip) file. (2) Transition date file: Estimates “greenness rising” (spring) and “greenness falling” (autumn) transition dates derived from the smoothed daily green chromatic coordinate (GCC) values, for each camera and each ROI (i.e. vegetation type) • Contains one file in *.csv format (3) Snow flag files: Indicate days with snow on trees or snow on ground for each experimental enclosure • Contains two files in *.csv format, one for snow on trees and one for snow on ground This data set consists of two sets of companion files: (1) Accompanying HTML files show the 90th quantiles of the mean GCC plotted together with transition dates for each vegetation type and plot. • Contains three files in HTML format, one for each vegetation type • One additional file in HTML format with the transition dates plotted for each vegetation type, by year (2) R files for processing Phenocam files and flags. • Contains five files in R file (*.R) format in one compressed (*.zip) file User Note: All imagery is posted in near-real time to the PhenoCam Project web page (http://phenocam.sr.unh.edu/), where it is publicly available. Scroll to “spruce” in the Gallery or link directly to the 29 SPRUCE cameras at https://tinyurl.com/sprucecams. The data reported here are based on the complete camera record from SPRUCE and supersedes the previously released data inclusive of the 2015-2022 data (see Related Data Sets). The estimated transition dates for previously released datasets may differ slightly (in most cases, by ±3 days or less), because following standard PhenoCam processing protocols (Richardson et al. 2018, Scientific Data), smoothing and interpolation, outlier removal, and transition date estimation are always conducted using the full data record.

Spruce and Peatland Responses Under Changing Envir↗
DOE OSTI2025

SPRUCE Vegetation Phenology in Experimental Plots from PhenoCam Imagery, 2015-2024

This data set consists of PhenoCam data from the SPRUCE experiment from the beginning of whole ecosystem warming (Hanson et al. 2017) in August 2015 through March 31 of 2025 (2015-08-24 to 2025-03-31), with start- and end-of-season phenological transition dates derived through the end of autumn 2024. Digital cameras, or phenocams, installed in each SPRUCE enclosure track seasonal variation in vegetation “greenness”, a proxy for vegetation phenology and associated physiological activity. Three separate regions of interest (ROIs) were defined for each camera field of view, corresponding to different vegetation types and demarcating (1) Picea trees (vegetation type EN, for evergreen needleleaf); (2) Larix trees (vegetation type DN, for deciduous needleleaf); and (3) the mixed shrub layer (vegetation type SH). This data set consists of three sets of data files: (1) 3-day summary product files: One file for each camera and each ROI (i.e. vegetation type), characterizing vegetation color at a 3-day time step. • Contains 36 files in *.csv format inside a compressed (*.zip) file. (2) Transition date file: Estimates “greenness rising” (spring) and “greenness falling” (autumn) transition dates derived from the smoothed daily green chromatic coordinate (GCC) values, for each camera and each ROI (i.e., vegetation type). • Contains one file in *.csv format. (3) Snow flag files: Indicate days with snow on trees or snow on ground for each experimental enclosure. • Contains two files in *.csv format, one for snow on trees and one for snow on ground. This data set consists of two sets of companion files: (1) Accompanying HTML files show the 90th quantiles of the mean GCC plotted together with transition dates for each vegetation type and plot. • Contains three files in HTML format, one for each vegetation type. • One additional file in HTML format with the transition dates plotted for each vegetation type, by year. (2) R files for processing PhenoCam files and flags. • Contains five files in R file(*.R) format and the components of the phenocamr package (Version 1.1.4) used for calculating transition dates for 2015-2024. These are contained in a compressed (*.zip) file. User Note: All imagery is posted in near-real time to the PhenoCam Project web page (https://phenocam.nau.edu), where it is publicly available. Scroll to “spruce” in the Gallery or link directly to the 29 SPRUCE cameras at https://tinyurl.com/sprucecams. This data set is based on the complete camera record from SPRUCE and supersedes all previously released PhenoCam datasets (see Related Data Sets). The estimated transition dates for previously released datasets may differ slightly (in most cases, by ±3 days or less), because following standard PhenoCam processing protocols (Richardson et al. 2018, Scientific Data), smoothing and interpolation, outlier removal, and transition date estimation are always conducted using the full data record.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Competitive Adsorption of Ionic Liquids Versus Friction Modifier and Anti-Wear Additive at Solid/Lubricant Interface—Speciation with Vibrational Sum Frequency Generation Spectroscopy

A modern lubricant contains various additives with different functionalities and the interactions or reactions between these additives could induce synergistic or antagonistic effects in tribological performance. In this study, sum frequency generation (SFG) spectroscopy was used to investigate competitive adsorption of lubricant additives at a solid/base oil interface. A silica substrate was used as a model solid surface. The lubricant additives studied here included two oil-soluble ionic liquids (ILs, [N888H][DEHP] and [P8888][DEHP]), an antiwear additive (secondary ZDDP), an organic friction modifier (OFM), and a dispersant (PIBSI). Our results showed that for mixtures of ZDDP and IL in a base oil (PAO4), the silica surface is dominated by the IL molecules. In the cases of base oils containing OFM and IL, the silica/lubricant interface is dominated by OFM over [N888H][DEHP], while it is preferentially occupied by [P8888][DEHP] over OFM. The presence of PIBSI in the mixture of PAO4 and IL leads to the formation of a mixed surface layer at the silica surface with PIBSI as a major component. The SFG results in this investigation provide fundamental insights that are helpful to design the formulation of new lubricant additives of desired properties.

25 ENERGY STORAGE↗
DOE OSTI2022

Cosmogenic nuclide and solute flux data from central Cuban rivers emphasize the importance of both physical and chemical mass loss from tropical landscapes

We use 25 new measurements of in situ produced cosmogenic 26 Al and 10 Be in river sand, paired with estimates of dissolved load flux in river water, to characterize the processes and pace of landscape change in central Cuba. Long-term erosion rates inferred from 10 Be concentrations in quartz extracted from central Cuban river sand range from 3.4–189 Mg km –2 yr –1 (mean 59, median 45). Dissolved loads (10–176 Mg km –2 yr –1 ; mean 92, median 97), calculated from stream solute concentrations and modeled runoff, exceed measured cosmogenic- 10 Be-derived erosion rates in 18 of 23 basins. This disparity mandates that in this environment landscape-scale mass loss is not fully represented by the cosmogenic nuclide measurements. The 26 Al / 10 Be ratios are lower than expected for steadystate exposure or erosion in 16 of 24 samples. Depressed 26 Al/ 10 Be ratios occur in many of the basins that have the greatest disparity between dissolved loads (high) and erosion rates inferred from cosmogenic nuclide concentrations (low). Depressed 26 Al/ 10 Be ratios are consistent with the presence of a deep, mixed, regolith layer providing extended storage times on slopes and/or burial and extended storage during fluvial transport. River water chemical analyses indicate that many basins with lower 26 Al/ 10 Be ratios and high 10 Be concentrations are underlain at least in part by evaporitic rocks that rapidly dissolve. Our data show that when assessing mass loss in humid tropical landscapes, accounting for the contribution of rock dissolution at depth is particularly important. In such warm, wet climates, mineral dissolution can occur many meters below the surface, beyond the penetration depth of most cosmic rays and thus the production of most cosmogenic nuclides. Our data suggest the importance of estimating solute fluxes and measuring paired cosmogenic nuclides to better understand the processes and rates of mass transfer at a basin scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Direct Observation of Defect‐Aided Structural Evolution in a Nickel‐Rich Layered Cathode

Abstract Ni‐rich LiNi 1− x − y Mn x Co y O 2 (NMC) layered compounds are the dominant cathode for lithium ion batteries. The role of crystallographic defects on structure evolution and performance degradation during electrochemical cycling is not yet fully understood. Here, we investigated the structural evolution of a Ni‐rich NMC cathode in a solid‐state cell by in situ transmission electron microscopy. Antiphase boundary (APB) and twin boundary (TB) separating layered phases played an important role on phase change. Upon Li depletion, the APB extended across the layered structure, while Li/transition metal (TM) ion mixing in the layered phases was detected to induce the rock‐salt phase formation along the coherent TB. According to DFT calculations, Li/TM mixing and phase transition were aided by the low diffusion barriers of TM ions at planar defects. This work reveals the dynamical scenario of secondary phase evolution, helping unveil the origin of performance fading in Ni‐rich NMC.

Li, Shuang↗
DOE OSTI2020

Direct observation of defect-aided structural evolution in Ni-rich layered cathode

Ni-rich LiNi 1-x-y Mn x Co y O 2 (NMC) layered compounds have become the dominated cathode for lithium ion batteries. The role of crystallographic defects on their structure evolution and consequent performance degradation during electrochemical cycling is not yet fully understood. In this work, we investigated the structural evolution of Ni-rich NMC cathode in a solid-state cell via in-situ transmission electron microscopy. We identified antiphase boundary (APB) and twin boundary (TB) separating layered phases played an important role on phase change. Upon the lithium depletion, the APB extends across the layered structure, while Li/transition metal (TM) ion mixing in the layered phases is detected to induce the rock-salt phase formation along the coherent TB. According to DFT calculations, Li/TM mixing and phase transition are aided by the low diffusion barriers of TM ions at planar defects. Finally, this work reveals the dynamical scenario of secondary phase evolution, which helps to unveil the origin of performance fading in Ni-rich NMC.

25 ENERGY STORAGE↗
DOE OSTI2021

Iridate Li8IrO 6 : An Antiferromagnetic Insulator

In this work, a polycrystalline iridate Li 8 IrO 6 material was prepared via heating Li 2 O and IrO 2 starting materials in a sealed quartz tube at 650 °C for 48 h. The structure was determined from Rietveld refinement of room-temperature powder neutron diffraction data. Li 8 IrO 6 adopts the nonpolar space group $R\bar{3}$ with Li atoms occupying the tetrahedral and octahedral sites, which is supported by the electron diffraction and solid-state 7Li NMR. This results in a crystal structure consisting of LiO 4 tetrahedral layers alternating with mixed IrO 6 and LiO 6 octahedral layers along the crystallographic c -axis. The +4 oxidation state of Ir 4+ was confirmed by near-edge X-ray absorption spectroscopy. An in situ synchrotron X-ray diffraction study of Li 8 IrO 6 indicates that the sample is stable up to 1000 °C and exhibits no structural transitions. Magnetic measurements suggest long-range antiferromagnetic ordering with a Néel temperature (TN) of 4 K, which is corroborated by heat capacity measurements. The localized effective moment $μ_{\text{eff}}$ (Ir) = 1.73 $μ_{\text{B}}$ and insulating character indicate that Li 8 IrO 6 is a correlated insulator. First-principles calculations support the nonpolar crystal structure and reveal the insulating behavior both in paramagnetic and antiferromagnetic states.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A large-eddy simulation exploration of the assumptions used in retrieving entrainment from a mixing diagram approach with ground-based remote sensors

Entrainment is a crucial component of the atmospheric boundary layer (BL) moisture and heat budget. While usually thought of as only entrainment flux, entrainment within the mixed layer budget equation is really composed of two terms: the flux of a property across the boundary separating the BL from the free troposphere and the change in the concentration of a property as the depth of the BL changes. In a recent study, Wakefield et al. (2023) used ground-based remote-sensing observations to estimate entrainment flux as the residual of a mixing diagram framework that was applied to the daytime convective boundary layer. This present work uses large-eddy simulation (LES) to examine how well this residual assumption for entrainment fluxes alone compares to the actual sum of those two entrainment terms derived from spatial averages of the LES output. We highlight the importance of the second entrainment term in closing the mixed layer budget and show that the residual assumption does not represent entrainment flux only but rather a total entrainment term when the boundary layer depth is changing.

Rosenberger, Tessa E. [Cleveland State Univ., Clev↗
DOE OSTI2020

Toward non-Si electronics: From remote epitaxy to layer splitting of 2D materials for mixed dimensional heterostructures

The current electronics has been mainly dominated by Si-based devices due to their mature processing system and exceptional cost-effectiveness. However, next generation electronics needs novel functionalities that cannot be realized by Si because of intrinsic limitation of Si. Accordingly, demand for non-Si electronics has been getting substantially high. Unfortunately, current methodology requires extremely high cost for non-Si materials, which impedes the progress in developing the non-Si based electronics. Here, I will discuss about our group’s efforts to address this issue. Our team recently conceived a new crystalline growth, termed as “remote epitaxy”, which can copy/paste crystalline information from substrates remotely through graphene, thus generating single-crystalline films on graphene. As interfacial binding energy is attenuated by inserting graphene at interface, the single-crystalline films can be easily exfoliated from the slippery graphene surface. Also, the graphene-coated substrates can be, in principle, reused infinitely to produce single-crystalline films. Thus, the remote epitaxy can produce non-Si semiconductor films with unprecedented cost efficiency while allowing additional flexible device functionality required for current ubiquitous electronics. Next, I will discuss about a layer splitting technique which can be a potential solution to overcome the problem in obtaining large-scale and monolayer 2D materials. A 2D material-based heterostructure has been intensively studied because of its unique device functionalities and novel physics. However, it is extremely challenging to secure large-scale and monolayer 2D materials because of following issues: 1) poor scalability for laboratory fabrication processes of 2D heterostructures and 2) lack of well-defined control parameters for kinetics of 2D materials and predictable number of layers of 2D materials. To resolve this issue, we conceived a new approach called “layer-resolved splitting” which obtains multiple monolayer from multilayer 2D materials by controlling interfacial toughness contrast. As this method is versatile and universal, we can, in principle, apply to all 2D materials. We succeeded in having large-scale, monolayer 2D materials through our approach and, thereby 2D heterostructures were demonstrated for functional devices. Lastly, I would like to discuss opportunities of mixed-dimensional heterostructure demonstrated by remote epitaxy and layer-resolved splitting. As they produce freestanding 3D bulk films and 2D atomic layers, a new type of 3D/2D heterostructures can be realized where a new physics and new device architecture are revealed. Therefore, I believe that a new opportunity will be discovered through the mixed-dimensional heterostructures.

Bae, Sang-Hoon↗