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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Emergence of two distinct phase transitions in monolayer CoSe2 on graphene

Abstract Dimensional modifications play a crucial role in various applications, especially in the context of device miniaturization, giving rise to novel quantum phenomena. The many-body dynamics induced by dimensional modifications, including electron-electron, electron-phonon, electron-magnon and electron-plasmon coupling, are known to significantly affect the atomic and electronic properties of the materials. By reducing the dimensionality of orthorhombic CoSe 2 and forming heterostructure with bilayer graphene using molecular beam epitaxy, we unveil the emergence of two types of phase transitions through angle-resolved photoemission spectroscopy and scanning tunneling microscopy measurements. We disclose that the 2 × 1 superstructure is associated with charge density wave induced by Fermi surface nesting, characterized by a transition temperature of 340 K. Additionally, another phase transition at temperature of 160 K based on temperature dependent gap evolution are observed with renormalized electronic structure induced by electron-boson coupling. These discoveries of the electronic and atomic modifications, influenced by electron-electron and electron-boson interactions, underscore that many-body physics play significant roles in understanding low-dimensional properties of non-van der Waals Co-chalcogenides and related heterostructures. Graphical Abstract

36 MATERIALS SCIENCE↗
DOE OSTI2021

Expanding the application of a UV-visible reporter for transient gene expression and stable transformation in plants

Abstract Green fluorescent protein (GFP) has been widely used for monitoring gene expression and protein localization in diverse organisms. However, highly sensitive imaging equipment, like fluorescence microscope, is usually required for the visualization of GFP, limitings its application to fixed locations in samples. A reporter that can be visualized in real-time regardless the shape, size and location of the target samples will increase the flexibility and efficiency of research work. Here, we report the application of a GFP-like protein, called eYGFPuv, in both transient expression and stable transformation, in two herbaceous plant species ( Arabidopsis and tobacco) and two woody plant species (poplar and citrus). We observed bright fluorescence under UV light in all of the four plant species without any effects on plant growth or development. eYGFPuv was shown to be effective for imaging transient expression in leaf and root tissues. With a focus on in vitro transformation, we demonstrated that the transgenic events expressing 1x eYGFPuv could be easily identified visually during the callus stage and the shoot stage, enabling early and efficient selection of transformants. Furthermore, whole-plant level visualization of eYGFPuv revealed its ubiquitous stability in transgenic plants. In addition, our transformation experiments showed that eYGFPuv can also be used to select transgenic plants without antibiotics. This work demonstrates the feasibility of utilizing 1x eYGFPuv in studies of gene expression and plant transformation in diverse plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Application of a Distributed Element Roughness Model to Additively Manufactured Internal Cooling Channels

Abstract Design for cooling effectiveness in turbine blades relies on accurate models for dynamic losses and heat transfer of internal cooling passages. Metal additive manufacturing (AM) has expanded the design space for these configurations, but can give rise to large-scale roughness features. The range of roughness length scales in these systems makes morphology resolved computational fluid dynamics (CFD) impractical. However, volumetric roughness models can be leveraged, as they have computational costs orders of magnitude lower. In this work, a discrete element roughness model (DERM), based on the double-averaged Navier–Stokes equations, is presented and applied to additively manufactured rough channels, representative of gas turbine blade cooling passages. Unique to this formulation of DERM is a generalized sheltering-based treatment of drag, a two-layer model for spatially averaged Reynolds stresses, and explicit treatment of dispersion. Six different AM rough surface channel configurations are studied, with roughness trough to peak sizes ranging from 15% to 60% nominal channel passage half-width, and the roughness Reynolds number ranges from Rek = 60 to 300. DERM predictions for spatially and temporally averaged mean flow quantities are compared to previously reported direct numerical simulation results. Good agreement in the mean velocity profiles, stress balances, and drag partitions are observed. While DERM models are typically calibrated to specific deterministic roughness morphologies at comparatively small roughness Reynolds numbers, the present more generalized DERM formulation has wider applicability. Here, it is demonstrated that the model can accommodate random roughness of large scale, typical of AM.

Engineering↗
DOE OSTI2022

Stable Cuprous Hydroxide Nanostructures by Organic Ligand Functionalization

Abstract Copper compounds have been extensively investigated for diverse applications. However, studies of cuprous hydroxide (CuOH) have been scarce due to structural metastability. Herein, a facile, wet‐chemistry procedure is reported for the preparation of stable CuOH nanostructures via deliberate functionalization with select organic ligands, such as acetylene and mercapto derivatives. The resulting nanostructures are found to exhibit a nanoribbon morphology consisting of small nanocrystals embedded within a largely amorphous nanosheet‐like scaffold. The acetylene derivatives are found to anchor onto the CuOH forming CuC linkages, whereas CuS interfacial bonds are formed with the mercapto ligands. Effective electronic coupling occurs at the ligand‐core interface in the former, in contrast to mostly non‐conjugated interfacial bonds in the latter, as manifested in spectroscopic measurements and confirmed in theoretical studies based on first principles calculations. Notably, the acetylene‐capped CuOH nanostructures exhibit markedly enhanced photodynamic activity in the inhibition of bacteria growth, as compared to the mercapto‐capped counterparts due to a reduced material bandgap and effective photocatalytic generation of reactive oxygen species. Results from this study demonstrate that deliberate structural engineering with select organic ligands is an effective strategy in the stabilization and functionalization of CuOH nanostructures, a critical first step in exploring their diverse applications.

Liu, Qiming↗
DOE OSTI2020

Revealing the Structural Evolution of Green Rust Synthesized in Ionic Liquids by In Situ Molecular Imaging

Abstract Ionic liquids are green solvents that have wide applications in material synthesis, catalysis, and separation. A model switchable ionic liquid (SWIL) consisting of 1,8‐diazabicycloundec‐7‐ene (DBU) and 1‐hexanol with carbon dioxide (CO 2 ) as the trigger gas is chosen to synthesize nanocrystalline green rust. Under anoxic conditions, by adding iron (II) acetate (Fe(C 2 H 3 O 2 ) 2 ) and methanol to the degassed SWIL, a nanoparticulate green rust with carbonate (nano‐GR) is synthesized. More importantly, the molecular structural change of the ionic liquid resulting in green rust crystallines is first characterized using in situ liquid using time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) coupled with the system for analysis at the liquid vacuum interface (SALVI), a vacuum compatible microfluidic reactor in this work. Dimers and ionic clusters consisting ferric and ferrous ions are identified in liquid ToF‐SIMS spectra. Spectral principal component analysis is used to confirm that these ion pairs including dimers and cluster ions are different from nonionic liquids. The results show that liquid ToF‐SIMS can be a useful tool to study complex liquids at the molecular level providing insights into predicative synthesis of nanomaterials using environmentally friendly green solvents and giving unique visualization of the evolving material interface due to nucleation.

Shen, Yanjie↗
DOE OSTI2020

Device Performance of Emerging Photovoltaic Materials (Version 1)

Abstract Emerging photovoltaics (PVs) focus on a variety of applications complementing large scale electricity generation. Organic, dye‐sensitized, and some perovskite solar cells are considered in building integration, greenhouses, wearable, and indoor applications, thereby motivating research on flexible, transparent, semitransparent, and multi‐junction PVs. Nevertheless, it can be very time consuming to find or develop an up‐to‐date overview of the state‐of‐the‐art performance for these systems and applications. Two important resources for recording research cells efficiencies are the National Renewable Energy Laboratory chart and the efficiency tables compiled biannually by Martin Green and colleagues. Both publications provide an effective coverage over the established technologies, bridging research and industry. An alternative approach is proposed here summarizing the best reports in the diverse research subjects for emerging PVs. Best performance parameters are provided as a function of the photovoltaic bandgap energy for each technology and application, and are put into perspective using, e.g., the Shockley–Queisser limit. In all cases, the reported data correspond to published and/or properly described certified results, with enough details provided for prospective data reproduction. Additionally, the stability test energy yield is included as an analysis parameter among state‐of‐the‐art emerging PVs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bayesian optimization of dynamic Ce( III ) electrooxidation

Abstract Cerium(III/IV) is a redox couple with promising applications in redox flow batteries and redox‐mediated water electrolyzers, but due to its high oxidation potential, parasitic oxygen evolution can occur and decrease the Faradaic efficiency (FE) of the oxidation reaction. Dynamic potential dosing can help balance the transport limitations and the electrooxidation rates, allowing for control over the cerium(III) concentration near the electrode, enhancing FE. Due to the large number of possible pulsing operating conditions, Bayesian optimization (BO) was used to rapidly identify the optimal potential pulses. With an active pulse voltage of 2.5 V vs. Ag/AgCl, the maximum FE achieved was 0.91 (active pulse time = 5 ms, resting pulse time = 136 ms), underscoring the need for long inactive times for cerium(III) diffusion to the electrode. Furthermore, a multiobjective BO was performed to identify optimal trade‐offs between FE and the partial current density to achieve improvements over constant current operation.

Engineering↗
DOE OSTI2020

Regioselective Arene C−H Alkylation Enabled by Organic Photoredox Catalysis

Abstract Expanding the toolbox of C−H functionalization reactions applicable to the late‐stage modification of complex molecules is of interest in medicinal chemistry, wherein the preparation of structural variants of known pharmacophores is a key strategy for drug development. One manifold for the functionalization of aromatic molecules utilizes diazo compounds and a transition‐metal catalyst to generate a metallocarbene species, which is capable of direct insertion into an aromatic C−H bond. However, these high‐energy intermediates can often require directing groups or a large excess of substrate to achieve efficient and selective reactivity. Herein, we report that arene cation radicals generated by organic photoredox catalysis engage in formal C−H functionalization reactions with diazoacetate derivatives, furnishing sp 2 –sp 3 coupled products with moderate‐to‐good regioselectivity. In contrast to previous methods utilizing metallocarbene intermediates, this transformation does not proceed via a carbene intermediate, nor does it require the presence of a transition‐metal catalyst.

Holmberg‐Douglas, Natalie↗
DOE OSTI2024

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

Abstract Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

Fricke, Sophia N.↗
DOE OSTI2020

Regioselective Arene C−H Alkylation Enabled by Organic Photoredox Catalysis

Abstract Expanding the toolbox of C−H functionalization reactions applicable to the late‐stage modification of complex molecules is of interest in medicinal chemistry, wherein the preparation of structural variants of known pharmacophores is a key strategy for drug development. One manifold for the functionalization of aromatic molecules utilizes diazo compounds and a transition‐metal catalyst to generate a metallocarbene species, which is capable of direct insertion into an aromatic C−H bond. However, these high‐energy intermediates can often require directing groups or a large excess of substrate to achieve efficient and selective reactivity. Herein, we report that arene cation radicals generated by organic photoredox catalysis engage in formal C−H functionalization reactions with diazoacetate derivatives, furnishing sp 2 –sp 3 coupled products with moderate‐to‐good regioselectivity. In contrast to previous methods utilizing metallocarbene intermediates, this transformation does not proceed via a carbene intermediate, nor does it require the presence of a transition‐metal catalyst.

Holmberg‐Douglas, Natalie↗
DOE OSTI2024

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

Abstract Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

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

Nonnegative canonical tensor decomposition with linear constraints: nnCANDELINC

Abstract There is an emerging interest for tensor factorization applications in big‐data analytics and machine learning. To speed up the factorization of extra‐large datasets, organized in multidimensional arrays (also known as tensors), easy to compute compression‐based tensor representations, such as, Tucker and tensor train formats, are used to approximate the initial large‐tensor. Further, tensor factorization is used to extract latent features that can facilitate discoveries of new mechanisms and signatures hidden in the data, where the explainability of the latent features is of principal importance. Nonnegative tensor factorization extracts latent features that are naturally sparse and parts of the data, which makes them easily interpretable. However, to take into account available domain knowledge and subject matter expertise, often additional constraints need to be imposed, which lead us to canonical decomposition with linear constraints (CANDELINC), a canonical polyadic decomposition with rank deficient factors. In CANDELINC, Tucker compression is used as a preprocessing step, which lead to a larger residual error but to more explainable latent features. Here, we propose a nonnegative CANDELINC (nnCANDELINC) accomplished via a specific nonnegative Tucker decomposition; we refer to as minimal or canonical nonnegative Tucker. We derive several results required to understand the specificity of nnCANDELINC, focusing on the difficulties of preserving the nonnegative rank of a tensor to its Tucker core and comparing the real valued to nonnegative case. Finally, we demonstrate nnCANDELINC performance on synthetic and real‐world examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design

Abstract There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure–property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Chitin- and cellulose-based sustainable barrier materials: a review

Abstract The accumulation of synthetic plastics used in packaging applications in landfills and the environment is a serious problem. This challenge is driving research efforts to develop biodegradable, compostable, or recyclable barrier materials derived from renewable sources. Cellulose, chitin/chitosan, and their combinations are versatile biobased packaging materials because of their diverse biological properties (biocompatibility, biodegradability, antimicrobial properties, antioxidant activity, non-toxicity, and less immunogenic compared to protein), superior physical properties (high surface area, good barrier properties, and mechanical properties), and they can be assembled into different forms and shapes (powders, fibers, films, beads, sponges, gels, and solutions). They can be either assembled into packaging films or used as fillers to improve the properties of other biobased polymers. Methods such as preparation of composites, multilayer coating, and alignment control are used to further improve their barrier, mechanical properties, and ameliorate their moisture sensitivity. With the growing application of cellulose and chitin-based packaging materials, their biodegradability and recyclability are also discussed in this review paper. The future trends of these biobased materials in packaging applications and the possibility of gradually replacing petroleum-based plastics are analyzed in the “Conclusions” section.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Colloidal CdSe nanocrystals are inherently defective

Abstract Colloidal CdSe nanocrystals (NCs) have shown promise in applications ranging from LED displays to medical imaging. Their unique photophysics depend sensitively on the presence or absence of surface defects. Using simulations, we show that CdSe NCs are inherently defective; even for stoichiometric NCs with perfect ligand passivation and no vacancies or defects, we still observe that the low energy spectrum is dominated by dark, surface-associated excitations, which are more numerous in larger NCs. Surface structure analysis shows that the majority of these states involve holes that are localized on two-coordinate Se atoms. As chalcogenide atoms are not passivated by any Lewis base ligand, varying the ligand should not dramatically change the number of dark states, which we confirm by simulating three passivation schemes. Our results have significant implications for understanding CdSe NC photophysics, and suggest that photochemistry and short-range photoinduced charge transfer should be much more facile than previously anticipated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Spontaneous dynamical disordering of borophenes in MgB2 and related metal borides

Abstract Layered boron compounds have attracted significant interest in applications from energy storage to electronic materials to device applications, owing in part to a diversity of surface properties tied to specific arrangements of boron atoms. Here we report the energy landscape for surface atomic configurations of MgB 2 by combining first-principles calculations, global optimization, material synthesis and characterization. We demonstrate that contrary to previous assumptions, multiple disordered reconstructions are thermodynamically preferred and kinetically accessible within exposed B surfaces in MgB 2 and other layered metal diborides at low boron chemical potentials. Such a dynamic environment and intrinsic disordering of the B surface atoms present new opportunities to realize a diverse set of 2D boron structures. We validated the predicted surface disorder by characterizing exfoliated boron-terminated MgB 2 nanosheets. We further discuss application-relevant implications, with a particular view towards understanding the impact of boron surface heterogeneity on hydrogen storage performance.

36 MATERIALS SCIENCE↗