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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Zeolitic imidazolate framework membranes made by ligand-induced permselectivation

Zeolitic imidazolate framework (ZIF) membranes are emerging as a promising energy-efficient separation technology. However, their reliable and scalable manufacturing remains a challenge. We demonstrate the fabrication of ZIF nanocomposite membranes by means of an all-vapor-phase processing method based on atomic layer deposition (ALD) of ZnO in a porous support followed by ligand-vapor treatment. After ALD, the obtained nanocomposite exhibits low flux and is not selective, whereas after ligand-vapor (2-methylimidazole) treatment, it is partially transformed to ZIF and shows stable performance with high mixture separation factor for propylene over propane (an energy-intensive high-volume separation) and high propylene flux. Membrane synthesis through ligand-induced permselectivation of a nonselective and impermeable deposit is shown to be simple and highly reproducible and holds promise for scalability.

Science & Technology - Other Topics↗
DOE OSTI2024

Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement

This paper analyzes a popular computational framework to solve infinite-dimensional Bayesian inverse problems, discretizing the prior and the forward model in a finite-dimensional weighted inner product space. We demonstrate the benefit of working on a weighted space by establishing operator-norm bounds for finite element and graph-based discretizations of Matérn-type priors and deconvolution forward models. For linear-Gaussian inverse problems, we develop a general theory to characterize the error in the approximation to the posterior. We also embed the computational framework into ensemble Kalman methods and MAP estimators for nonlinear inverse problems. Furthermore, our operator-norm bounds for prior discretizations guarantee the scalability and accuracy of these algorithms under mesh refinement.

Bayesian inverse problem↗
DOE OSTI2023

QASMTrans: A QASM Quantum Transpiler Framework for NISQ Devices

In quantum computing, transpilation plays a crucial role in converting high-level, machine-independent quantum circuits into circuits specially for a quantum device, considering factors such as basis gate set, topology, error profile, etc. Yet, the efficiency of transpilation remains a significant bottleneck, particularly when dealing with very large QASM level input files. In this paper, we present QASMTrans, a C++ based high-performance quantum transpiler framework that can demonstrate on average 50-100× speedups compared to the internal transpiler of Qiskit. Particularly, for large dense circuits such as ’uccsd n24’ and ’qft n320’ incorporating millions of gates, QASMTrans can successfully transpile in 69s and 31s, respectively, while Qiskit failed to finish in one hour. Using QASMTrans as the baseline, it becomes more feasible to explore much larger design space and impose more comprehensive compiler optimizations.

Hua, Fei↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗
DOE OSTI2017

Towards a framework for selection of supervisory control for commercial buildings: HVAC system energy efficiency

This paper develops and utilizes an optimization based framework to investigate the maximal energy efficiency potentially attainable by HVAC system operation in a non-predictive context. Performance is evaluated relative to the existing state of the art set-point reset strategies. The expected efficiency increase driven by operation constraints relaxations is evaluated.

Ramachandran, Thiagarajan↗
DOE OSTI2025

IBPSA Project 2 BOPTEST: An update on the test cases available in the framework for testing advanced control strategies in buildings

Project 2 develops software infrastructure, test cases, and extensions for the Building Optimization Testing Framework (BOPTEST) to address the expanding needs of building and urban energy system controls through open international collaboration. This paper provides an overview of the new test cases available as of BOPTEST version 0.7.1. Each test case is developed using open-source Modelica libraries and Spawn of EnergyPlus, enabling the creation of high-fidelity building models that incorporate envelope dynamics, Heating Ventilation and Air Conditioning (HVAC) systems, and explicit control representations. Currently, eight test cases are available, with five additional cases under development. These test cases cover a wide range of climates, building types, and HVAC systems. This paper compiles and summarizes test case descriptions, cites original manuscripts that developed them for a more detailed description, and reports baseline control performance metrics. Furthermore, two example applications are presented: one illustrating different levels of control, from supervisory to low-level, and another demonstrating how Model Predictive Control (MPC) solutions must be adapted from continuous to integer to control some building actuators.

Zanetti, Ettore↗
DOE OSTI2016

MATLAB/Simulink Framework for Modeling Complex Coolant Flow Configurations of Advanced Automotive Thermal Management Systems

The National Renewable Energy Laboratory's (NREL's) CoolSim MATLAB/Simulink modeling framework was extended by including a newly developed coolant loop solution method aimed at reducing the simulation effort for arbitrarily complex thermal management systems. The new approach does not require the user to identify specific coolant loops and their flow. The user only needs to connect the fluid network elements in a manner consistent with the desired schematic. Using the new solution method, a model of NREL's advanced combined coolant loop system for electric vehicles was created that reflected the test system architecture. This system was built using components provided by the MAHLE Group and included both air conditioning and heat pump modes. Validation with test bench data and verification with the previous solution method were performed for 10 operating points spanning a range of ambient temperatures between -2 degrees C and 43 degrees C. The largest root mean square difference between pressure, temperature, energy and mass flow rate data and simulation results was less than 7%.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1992

Framework Programmable Platform for the Advanced Software Development Workstation (FPP/ASDW). Demonstration framework document. Volume 1: Concepts and activity descriptions

The Framework Programmable Software Development Platform (FPP) is a project aimed at effectively combining tool and data integration mechanisms with a model of the software development process to provide an intelligent integrated software development environment. Guided by the model, this system development framework will take advantage of an integrated operating environment to automate effectively the management of the software development process so that costly mistakes during the development phase can be eliminated. The Advanced Software Development Workstation (ASDW) program is conducting research into development of advanced technologies for Computer Aided Software Engineering (CASE).

Mayer, Richard J.↗
DOE OSTI2018

Unveiling the Effects of Linker Substitution in Suzuki Coupling with Palladium Nanoparticles in Metal–Organic Frameworks [Unveiling the Effects of Linker Substitution in Suzuki Coupling Reaction with Palladium Nanoparticles in Metal–Organic Frameworks]

The establishment of structure–property relationships in heterogeneous catalysis is of prime importance but remains a formidable challenge. Metal–organic frameworks (MOFs) featuring excellent chemical tunability are emerging as an auspicious platform for the atomic-level control of heterogeneous catalysis. Herein, we encapsulate palladium nanoparticles (Pd NPs) in a series of isoreticular mixed-linker MOFs, and the obtained MOF-Pd NPs catalysts were used to unveil the electronic and steric effects of linker substitution on the activity of these catalysts in the Suzuki–Miyaura cross-coupling reactions. Significantly, m-6,6'-Me2bpy-MOF-Pd exhibits a remarkable enhancement in the activity compared to non-functionalized m-bpy-MOF-Pd and m-4,4'-Me 2 bpy-MOF-Pd. This study unambiguously demonstrates that the stereoelectronic properties of linker units are crucial to the catalytic activity of nanoparticles encapsulated in MOFs. More interestingly, the trend of activity change is consistent with our previous work on catalytic sites generated in situ from Pd(II) coordinated in MOFs bearing the same functional groups, which suggests that both MOF-Pd NPs and MOF-Pd(II) catalysts generate similar active centers during Suzuki–Miyaura coupling reactions. Lastly, this work paves a new avenue to the fabrication of advanced and tunable MOF-based catalysts through rational linker engineering.

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

Leaching Model of Radionuclides in Metal-Organic Framework Particles

Metal-organic frameworks (MOFs) have been used to sequester radionuclides and seal them inside of porous scaffolds using postsynthetic modification procedures. Experiments show that certain Zr-MOF with different capping linkers significantly affects the radionuclide release kinetics. In this work, we developed a leaching model of radionuclides in Zr-MOF particles. The model assumes that uranyl species occupy two energetically favored sites: the metal node and the MOF pores. For a given overall concentration of uranyl species, the partitions of uranyl species at the metal nodes and within the pores are determined by their chemical potentials. The model also considers the effect of particle surface and concentration on chemical potentials and diffusivity. The effects of spatial and structural dependent chemical potentials and diffusivity as well as particle sizes on leaching kinetics are investigated with the model. Predicted and measured uranyl leaching kinetics in Zr-MOF particles under batch experiments are compared. The results demonstrate the model’s capability for exploring the mechanisms of leaching and provide guidance for material design.

Metal-Organic Framework, Diffusion, uranyl, Leachi↗
DOE OSTI2017

Postsynthetic Tuning of Metal–Organic Frameworks for Targeted Applications

Metal–organic frameworks (MOFs) are periodic, hybrid, atomically well-defined porous materials that typically form by self-assembly and consist of inorganic nodes (metal ions or clusters) and multitopic organic linkers. MOFs as a whole offer many intriguing properties, including ultrahigh porosity, tunable chemical functionality, and low density. These properties point to numerous potential applications, including gas storage, chemical separations, catalysis, light harvesting, and chemical sensing, to name a few. Reticular chemistry, or the linking of molecular building blocks into predetermined network structures, has been employed to synthesize thousands of MOFs. Given the vast library of candidate nodes and linkers, the number of potentially synthetically accessible MOFs is enormous. Nevertheless, a powerful complementary approach to obtain specific structures with desired chemical functionality is to modify known MOFs after synthesis. This approach is particularly useful when incorporation of particular chemical functionalities via direct synthesis is challenging or impossible. Here, the challenges may stem from limited stability or solubility of precursors, unwanted secondary reactivity of precursors, or incompatibility of functional groups with the conditions needed for direct synthesis. MOFs can be postsynthetically modified by replacing the metal nodes and/or organic linkers or via functionalization of the metal nodes and/or organic linkers.

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

Zirconium-Based Metal–Organic Frameworks as Reusable Antibacterial Peroxide Carriers for Protective Textiles

Countries around the world have sought efficient protective coverings, including masks, gowns, and fabrics, for air purification to protect people against infectious diseases. However, the demand for significant quantities of disposable protective textiles poses a global challenge, especially when the production of protective gear is suspended due to COVID-19 outbreaks in factories and along supply lines. Therefore, the development of reusable, self-decontaminating protective masks and coverings loaded with disinfectants, such as antibacterial peroxide species, presents an attractive strategy to fight against bacteria risks. In this work, we incorporated persulfate ions, which serve as stable active peroxide precursors, into two porous zirconium-based metal–organic frameworks (Zr-MOFs), enabling these materials to act as regenerable reservoirs for the slow release of biocidal hydrogen peroxide upon hydrolysis by contact with humid air. Single-crystal X-ray diffraction studies reveal the two different coordination motifs for the persulfate ions, which can either bridge between two adjacent nodes or coordinate to a single node depending on both the node connectivity and the distances between open metal sites. The active peroxide precursors within the porous Zr-MOF carriers are stable during storage and easily regenerated once consumed. Importantly, these persulfate-loaded Zr-MOFs can be integrated onto textiles using a facile aqueous in-situ growth procedure, and these composites demonstrate potent and reusable biocidal activity against both Gram-negative bacteria and Gram-positive bacteria. Altogether, this approach presents a viable strategy to develop robust protective textiles capable of rapidly deactivating pathogens.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Toward Design Rules of Metal–Organic Frameworks for Adsorption Cooling: Effect of Topology on the Ethanol Working Capacity

Using combined experimental and computational approaches, we studied the adsorption behavior of ethanol in a series of metal-organic frameworks (MOFs) with similar building blocks but different topologies and tested their performance as adsorption coolers. Here, we analyzed the relationship between the MOF structures and their ethanol working capacities under different working conditions. Molecular-level insights into the microscopic adsorption behavior were also gained from atomistic simulations. Based on the structure-property relationships that we found, we selected another MOF (MOF-525) for study, and our experiments and simulations both showed superior ethanol working capacity for this MOF, which verified our design rules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗