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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

LaCeO x coupled N-doped graphene/Ru single-atoms as a binary-site catalyst for efficient hydrogen evolution based on hydrogen spillover

Based on the hydrogen spillover effect, versatile binary-site catalysts are promising for the alkaline hydrogen evolution reaction (HER). In which one site can adsorb and dissociate water, while the neighboring site is favorable for liberating hydrogen. Inspired by these possibilities, oxygen vacancy (O v )-rich LaCeO x coupled N-doped graphene/Ru single-atoms (LaCeO x @NGr/Ru 1 ) was synthesized as an efficient dual-site HER catalyst. It delivered an impressive low overpotential of 22 mV at a current density of 10mAcm –2 and a slight Tafel slope of 40 mV dec –1 in an alkaline medium, outstanding the advanced Ru-based catalysts. Moreover, this promising binary-component catalyst exhibited higher mass activity and longer lasting durability than commercial Pt/C catalyst (20 wt%). Finally, experimental and theoretical investigations provided insights into the HER mechanisms of LaCeO x @NGr/Ru 1 based on three indispensable steps: water adsorption and dissociation on O v -rich LaCeO x , diffusion of generated H* species towards Ce 3+ -N-Ru 1 bridges, and hydrogen evolution on Ru 1 sites.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Convolutional neural networks for intra-hour solar forecasting based on sky image sequences

Accurate and timely solar forecasts play an increasingly critical role in power systems. Compared to longer forecasting timescales, very short-term solar forecasting has lagged behind in both research and practice. In this paper, we propose deep convolutional neural networks (CNNs) to provide operational intra-hour (10-minute-ahead to 60-minute-ahead) solar forecasts. We develop two CNN structures inspired by a widely-used CNN architecture. The CNNs are tailored to our solar forecasting regression tasks and rely solely on sky image sequences. Case studies based on six years of data (over 150,000 data points) demonstrate that the best CNN model has forecast skill scores of 20%-39% over the naive persistence of cloudiness benchmark, even at these very short timescales. The CNNs also have consistently superior performance when compared to shallow machine learning models with meteorological predictors, where the improvement averages around 7%. The sensitivity analyses show that the sky image length, resolution, and weather conditions have impacts on the deep learning model accuracy. In our intra-hour problem with specific setups, two sky images with a 10-minute 128 x 128 resolution yield the most accurate forecasts. Current limitations, future work, and deployment challenges and solutions are also discussed.

14 SOLAR ENERGY↗
DOE OSTI2024

Situational awareness-enhancing community-level load mapping with opportunistic machine learning

Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.

14 SOLAR ENERGY↗
DOE OSTI2022

The W4 method: A new multi-dimensional root-finding scheme for nonlinear systems of equations

We propose a new class of method for solving nonlinear systems of equations, which, among other things, has four nice features: (i) it is inspired by the mathematical property of damped oscillators, (ii) it can be regarded as a simple extension to the Newton-Raphson (NR) method, (iii) it has the same local convergence as the NR method does, (iv) it has a significantly wider convergence region or the global convergence than that of the NR method. In this article, we present the evidence of these properties, applying our new method to some examples and comparing it with the NR method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Tailoring activity of iron phthalocyanine by edge-nitrogen sites induced electronic delocalization

Fe-N-C catalysts have been recognized as the most satisfactory candidates alternatively to Pt-based catalysts for oxygen reduction reaction (ORR). However, fine-tailoring of their intrinsic ORR catalytic activity still remains a great challenge due to the inferior accessibility and intrinsic activity of FeN x moieties. Herein, one order of magnitude activity enhancement of pristine Fe-N-C through cooperating with nitrogen-doped carbon micro-flower is achieved. The axial coordination effect between Fe active center and nitrogen atoms in support can break the electronic distribution symmetry of FeN x moieties and induce the electron delocalization on Fe active center and the electron localization on N, respectively, which favor the adsorption behavior of *OH intermediate. As a result, the catalyst exhibits a remarkable half-wave potential of 0.9 V and a high kinetic current density of 74.04 mA cm -2 at 0.85 V. In addition, when utilized as a cathode catalyst of liquid Zn-air batteries (ZABs), it possesses excellent electrochemical performance, for example, a high open circuit voltage (OCV) and peak power density of 1.59 V and 170.09 mW cm -2 , respectively. In conclusion, this work provides a new understanding into the activity enhancement mechanism of Fe-N-C catalysts, and inspires electronic delocalization of active sites for adjusting catalytic activity.

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

MADLens, a python package for fast and differentiable non-Gaussian lensing simulations

Here, we present MADLens a python package for producing non-Gaussian lensing convergence maps at arbitrary source redshifts with unprecedented precision. MADLens is designed to achieve high accuracy while keeping computational costs as low as possible. A MADLens simulation with only particles produces convergence maps whose power agrees with theoretical lensing power spectra up to within the accuracy limits of HaloFit. This is made possible by a combination of a highly parallelizable particle-mesh algorithm, a sub-evolution scheme in the lensing projection, and a machine-learning inspired sharpening step. Further, MADLens is fully differentiable with respect to the initial conditions of the underlying particle-mesh simulations and a number of cosmological parameters. These properties allow MADLens to be used as a forward model in Bayesian inference algorithms that require optimization or derivative-aided sampling. Another use case for MADLens is the production of large, high resolution simulation sets as they are required for training novel deep-learning-based lensing analysis tools. We make the MADLens package publicly available under a Creative Commons License

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Linking human-building interactions in shared offices with personality traits

Occupant behavior influences office building energy performance. The level of human-building interactions (HBIs) in shared offices strongly influences building energy use and occupant well-being. This study explored the link between occupant personality types and their behaviors of sharing energy and environment control systems and interactions with their colleagues. Inspired by the Five-Factor Model (FFM), we classified HBI behaviors into four dimensions: willingness to share control, knowledge of control, group decision behavior, and adaptive strategies. These four variables can be mapped to the four personality traits proposed by the FFM: agreeableness, openness, extraversion, and conscientiousness. Our cluster analysis identified six behavioral patterns: average (17.7%), reserved (15.3%), environmentally friendly (16.6%), role model (24.2%), self-centered (17.2%), and mechanist (9.0%). We further applied association rules, a widely utilized machine learning technique, to discover how demographics, building-related contextual factors, and perception-attitudinal factors influence HBI behaviors. Country, control feature accessibility, and group dynamics were found to be the three most influential factors that determine occupants’ HBI behaviors. Finally, the study provides insights about building design and operation, as well as policy to promote socially and environmentally desirable HBI behaviors in a shared office environment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Ten questions concerning agent-based modeling of occupant behavior for energy and environmental performance of buildings

We report the complexity of occupant behavior is one of the major contributors to uncertainty in building performance simulation. Agent-based modeling (ABM), a computational simulation technique, has gained attention in the occupant modeling field due to its capability and flexibility to capture the heterogeneity and dynamics of human behavior and the emergent effects. While multiple efforts in the past decade have demonstrated the usefulness of the ABM approach for simulating occupants and their impacts on building performance, several crucial matters in the ABM research still remain unexplored. This paper presents ten questions that highlight the most important issues regarding ABM research and applications for occupant behavior in the context of building performance simulation. The questions and answers aim to provide insights into current and future ABM research, and more importantly to inspire new significant questions from young researchers in the field. This research is part of the IEA EBC Annex 79 project, occupant-centric building design and operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

A scalable domain decomposition method for FEM discretizations of nonlocal equations of integrable and fractional type

Nonlocal models allow for the description of phenomena which cannot be captured by classical partial differential equations. The availability of efficient solvers is one of the main concerns for the use of nonlocal models in real world engineering applications. Here, we present a domain decomposition solver that is inspired by substructuring methods for classical local equations. In numerical experiments involving finite element discretizations of scalar and vectorial nonlocal equations of integrable and fractional type, we observe improvements in solution time of up to 14.6x compared to commonly used solver strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN

Design of materials with desired properties is currently laborious and heavily relies on intuition of researchers through a trial-and-error process. To tackle this challenge, in this work we propose a novel regressional and conditional generative adversarial network (RCGAN) for inverse design of representative two-dimensional materials, the graphene and boron-nitride (BN) hybrids. RCGAN incorporates a supervised regressor network, thus overcoming the common technical barrier in the traditional unsupervised GANs, which cannot generate data when fed with continuous and quantitative labels. RCGAN can autonomously generate graphene/BN hybrids given any target bandgap values. These structures are distinguished from the ones used for training and exhibit high diversity for a given bandgap. Moreover, they exhibit high fidelity, yielding bandgaps within ~10% MAE F of the desired bandgaps as validated by density functional theory (DFT) calculations. Analysis by the principle component analysis (PCA) and modified locally linear embedding (MLLE) reveals that the generator has successfully generated structures following the statistical distribution of the real structures. It implies the possibility of the RCGAN in recognizing physical rules hidden in the high-dimensional data. The novel strategy for designing regressional GAN architecture together with the successful application to inverse design of materials would inspire further exploration in research fields beyond materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Influence of graphite geography on the yield of mechanically exfoliated few-layer graphene

Some of graphene's exciting properties that inspired countless studies over the last two decades are exclusive to defect-free, few-layer graphene (FLG, ≤4 layers) produced by mechanical exfoliation. Despite graphite exfoliation having been thoroughly studied, the influence of the graphite source on the successful exfoliation of FLG remains unexplored. Here, in this work, we examine the physicochemical properties and exfoliation performance of various graphite types (e.g., natural crystalline flake, natural vein, and synthetic) from around the globe (e.g., Alaska, Tanzania, China, etc.). We first established a normalization method for the facile comparison of FLG (Φ) yields between separately exfoliated graphites. Using this approach, we find that the relative yield of FLG ranges from 0% to 22% across graphite types, with regular natural crystalline flakes (NCF-R) performing the best overall (Φ NCF-R = 16% ± 5%). FLG yield was also found to depend on source geography, evidenced by Φ NCF-R ranging from 8% to 22% for NCF-R sourced from Alabama and Canada, respectively. We employed machine learning and Pearson correlation analysis to determine the graphite characteristics that govern FLG yield. Graphite source-dependent properties such as graphite surface area and mineral impurities, including aluminum and calcium, were found to be critically important. Our findings highlight the importance of the graphite source when trying to maximize the yield of mechanically exfoliated FLG and represent an important step toward producing higher quantities of defect-free FLG for graphene-based research.

42 ENGINEERING↗
DOE OSTI2022

Protein-based models offer mechanistic insight into complex nickel metalloenzymes

There are ten nickel enzymes found across biological systems, each with a distinct active site and reactivity that spans reductive, oxidative, and redox–neutral processes. We focus on the reductive enzymes, which catalyze reactions that are highly germane to the modern-day climate crisis: [NiFe] hydrogenase, carbon monoxide dehydrogenase, acetyl coenzyme A synthase, and methyl coenzyme M reductase. The current mechanistic understanding of each enzyme system is reviewed along with existing knowledge gaps, which are addressed through the development of protein-derived models, as described here. Finally, this opinion is intended to highlight the advantages of using robust protein scaffolds for modeling multiscale contributions to reactivity and inspire the development of novel artificial metalloenzymes for other small molecule transformations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Tackling issues of lithium metal anodes with a novel polymeric lithicone coating

Lithium metal (Li) has been hindered from as anodes in commercial batteries for over 50 years, due to two main issues: continuous formation of solid electrolyte interphase (SEI) and lithium dendritic growth. In this work, we report a new strategy to tackle these issues, i.e., using molecular layer deposition (MLD) to grow an ionically conducting but electrically insulating polymeric lithicone coating, an Li-containing triethanolamine (LiTEA). Our electrochemical tests revealed that this LiTEA coating could serve as an exceptional protection layer over Li anodes. Consequently, the LiTEA-coated Li electrodes could achieve a superior cyclability of > 10000 Li stripping/plating cycles at a current density of 5 mA cm -2 and a long cyclability of > 5500 cycles at 2 mA cm -2 in Li||Li symmetric cells without failures, under a fixed areal capacity of 1 mAh cm -2 . Characterizations using scanning electron microscopy and X-ray photoelectron microscopy verified that Li + ions could be easily extracted through and deposited under the LiTEA coating during the stripping/plating processes. Consequently, this LiTEA coating significantly inhibited the formation of SEI and Li dendrites. This underlies the long cycling lifetime of the LiTEA-coated Li||Li cells. Coupling with the nickel-rich LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) cathodes, more encouragingly, the LiTEA-coated Li anodes could remarkably extend the cyclability and sustainable capacity of the resultant Li||NMC full cells. Finally, we also demonstrated that the performance of Li||NMC cells could be further improved through combining an LiTEA-coated Li anode with an Li 2 S-modified NMC811 via atomic layer deposition (ALD). Thus, this study is inspiring for developing high-energy Li||NMC lithium metal batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

Advances in atomistic modeling and understanding of drying shrinkage in cementitious materials

Despite more than a century of research, drying shrinkage still causes unwanted stresses and cracks in our concrete infrastructures. This is partly due to the lack of understanding of the underlying mechanisms governing the drying shrinkage in cementitious materials. Inspired by the tremendous impact of atomistic simulations on materials science, we expect such innovative simulation techniques to hold a key to unlocking the enigmatic nature of drying shrinkage at the nanoscale. In this treatise, we first introduce basic physics concepts and atomistic simulation methods in detail. Afterward, we discuss the progress brought by atomistic simulations in understanding the drying shrinkage in cementitious materials and other nanoporous and nanolayered systems. This review also highlights important remaining fundamental questions and practical issues regarding drying shrinkage and delineates how atomistic simulations can help resolve them.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Trends in electrocatalytic activity and stability of transition-metal nitrides

Transition metal nitrides (TMNs) are a class of electrocatalyst support materials similar to transition metal carbides (TMCs) with the advantage of avoiding the issues arising from graphitic carbon surface deposits during synthesis. Inspired by previous studies suggesting that TMCs could be used to reduce Pt loading for the hydrogen evolution reaction (HER), this work explored the feasibility of TMN-supported Pt and Au as HER electrocatalysts. Here this study established a volcano-like trend between electrochemical HER activity and hydrogen-binding energy (HBE) calculated from density functional theory. The Pt/TiN and Au/TiN materials were used to extend knowledge from well-characterized thin films to powder catalysts. In situ X-ray absorption spectroscopy (XAS) measurements provided additional characterization of the Pt/TiN and Au/TiN catalysts under HER conditions. Trends in the electrochemical stability of TMNs were also investigated over a wide range of potentials and pH values, which can be used to guide future studies for TMN-supported electrocatalysts.

58 GEOSCIENCES↗
DOE OSTI2021

A biohybrid strategy for enabling photoredox catalysis with low-energy light

Natural systems drive the high-energy reactions of photosynthesis with efficient and broadband energy capture. Transition-metal photocatalysts similarly convert light into chemical reactivity, and yet suffer from light-limited operation and require blue-to-UV excitation. In photosynthesis, both light capture and reactivity have been optimized by separation into distinct sites. In this work, inspired by this modular architecture, we synthesized a biohybrid photocatalyst by covalent attachment of the photosynthetic light-harvesting protein R-phycoerythrin (RPE) to the transition-metal photocatalyst tris(2,2'-bipyridine)ruthenium(II) ([Ru(bpy) 3 ] 2+ ). Spectroscopic investigation found that absorbed photoenergy was efficiently funneled from RPE to [Ru(bpy) 3 ] 2+ . The utility of the biohybrid photocatalyst was demonstrated via an increase in yields for a thiol-ene coupling reaction and a cysteinyl-desulfurization reaction, including recovered reactivity at red wavelengths where [Ru(bpy) 3 ] 2+ alone does not absorb.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗