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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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ALchemist is a modular Python toolkit that brings active learning and Bayesian optimization to experimental design in chemical and materials research. It is designed for scientists and engineers who want to efficiently explore or optimize high-dimensional variable spaces—without writing code—using an intuitive graphical interface.
Ascribe XR is an immersive visualization software designed for scientists and engineers working with 3D data sets. Its key features include interactive exploration, multi-user collaboration, and flexible data import capabilities, supporting various formats such as meshes, volumes, and terrain maps. The software utilizes Godot, OpenXR and PC-VR technology to provide an immersive experience. Ascribe XR is used for data analysis, visualization, and collaboration in various fields, enabling users to gain deeper insights into complex data sets. Its advantages over similar technologies include its flexibility, customizability, and ease of use. Ascribe XR's interactive and immersive environment facilitates collaboration and accelerates the discovery process. Compared to traditional 2D visualization tools, Ascribe XR offers a more engaging and intuitive experience, allowing users to explore complex data sets in a more natural and interactive way. Its ability to support multi-user collaboration and flexible data import capabilities make it a versatile tool for various applications. Overall, Ascribe XR provides a unique combination of features, usability, and performance, making it an attractive solution for scientists and engineers working with 3D data sets.
Abstract Quantitative Structure–Property Relationship studies (QSPR), often referred to interchangeably as QSAR, seek to establish a mapping between molecular structure and an arbitrary target property. Historically this was done on a target-by-target basis with new descriptors being devised to specifically map to a given target. Today software packages exist that calculate thousands of these descriptors, enabling general modeling typically with classical and machine learning methods. Also present today are learned representation methods in which deep learning models generate a target-specific representation during training. The former requires less training data and offers improved speed and interpretability while the latter offers excellent generality, while the intersection of the two remains under-explored. This paper introduces , a software package and general Deep-QSPR framework that combines a cogent set of molecular descriptors with deep learning to achieve state-of-the-art performance on datasets ranging from tens to tens of thousands of molecules. provides both a user-friendly Command Line Interface and highly interoperable set of Python modules for the training and deployment of feedforward neural networks for property prediction. This approach yields improvements in speed and interpretability over existing methods while statistically equaling or exceeding their performance across most of the tested benchmarks. is designed with Research Software Engineering best practices and is free and open source, hosted at github.com/jacksonburns/fastprop.
This article addresses recent advances in using de novo protein design to create coherent interfaces between proteins and inorganic materials, either through protein self-assembly on crystal lattices or through directed nucleation and growth of crystals by protein scaffolds. Inspired by natural protein-crystal interfaces, we focus on a class of designed helical repeat proteins that present a repeating pattern of charged amino acid residues. We describe the use of in situ imaging and spectroscopic methods to investigate both the assembly of these proteins and their ability to direct crystal nucleation and growth. Furthermore, the findings reveal the importance of surface charge, facet-specific binding, solvent organization, and, more generally, the balance of protein-substrate-solvent interactions in determining how organized protein-materials interfaces emerge. Moreover, the results demonstrate the vast potential of protein design in materials science and elucidate the mechanisms by which interactions between biomolecules and inorganic surfaces lead to unique materials and morphologies.
Proton-conducting solid oxide electrolysis cells (P-SOECs) are a promising technology for cost-effective and efficient production of green hydrogen. Breakthroughs in materials development, optimization of cell structure, and achievement of high performance and durability are essential to significantly increase the commercial competitiveness of these technologies. The main objective of this project is to gain scientific knowledge for the rational design, fabrication, and demonstration of a robust, highly efficient, and low-cost SOEC technology based on a proton-conducting electrolyte membrane for hydrogen production. We focused on better understanding the degradation mechanisms of proton-conducting electrolytes, air electrodes, and catalyst materials under electrolysis mode to develop an effective strategy for rationalizing new materials that are vital for enhancing cell performance and durability. The scope includes enhancing the performance and durability of the electrolyte and electrode materials under realistic operating conditions, developing highly active and robust catalysts to minimize electrode losses while improving tolerance to contaminant poisoning, revealing the mechanism of enhanced activity and stability of the catalyst, and understanding the underlying degradation mechanisms. In addition, various characterization techniques were employed to gain a fundamental understanding of the materials’ behavior and their impact on cell performance, providing vital information to guide materials discovery and cell design. After defect chemistry engineering, the optimized donor and acceptor co-doped electrolytes BaMo/W 0.03 Ce 0.71 Yb 0.26 O 3-δ (BM/W03) showed substantially improved chemical stability against high concentrations of CO 2 and H 2 O compared to the state-of-the-art electrolyte (BaZr 0.1 Ce 0.7 Y 0.1 Yb 0.1 O 3-δ , BZCYYb1711) while maintaining comparable ionic conductivity and ionic transference number. To bypass the inherent trade-off between conductivity and chemical stability, we fabricated a bi-layer electrolyte composed of BZCYYb1711 coated with a highly-stable thin layer of BaHf 0.83 Yb 0.17 O 3-δ (BHYb). This bi-layer electrolyte displayed excellent chemical stability against high concentration CO 2 ; there was no detectable formation of BaCO 3 after exposure to 97% CO 2 (with 3% H 2 O) at 500 °C for 1000 hours and the rate of degradation in resistance was about 0.4% per 1,000 hours (kh). In contrast, the same BZCYYb1711 electrolyte without a BHYb coating degraded significantly under the same testing conditions; the degradation rate was increased to 5.1%/kh. In addition, a triple conducting air electrode Ba 0.9 Pr 0.1 Hf 0.1 Y0.1Co 0.8 O 3-δ (BPHYC) was developed by heavily doping transition metal ions into a proton-conducting material. This air electrode material, composed of 3 distinct phases, exhibits superior electrocatalytic activity due to the synergistic effect from the three component phases. Moreover, an active and durable catalyst, La 2 Ni 0.5 Fe 0.5 O 4+δ (LNF), was developed, showing excellent catalytic activity and contaminant tolerance, with a degradation rate of only 0.49%/kh when exposed to high concentrations of steam and Cr. Finally, single cells were constructed from the best electrolytes, electrodes, and catalyst coatings developed in this project. These cells demonstrated superior high current density at a given cell voltage, high roundtrip efficiency, and remarkable durability (up to 1000 hours of operation).
The Surveillance, Fabrication, and Off-Site Operations team at Lawrence Livermore National Laboratory is beginning an update to the nuclear material machining lab in Superblock. Plutonium and other nuclear materials tend to accelerate the aging process for equipment and machines that would otherwise be maintainable for long lives. They have their lifespans shortened from deteriorated seals, embrittled polymer components, accelerated corrosion, and damage to electronics from exposure to radioactive materials. The cost of installation of equipment into a glove box (GB) tends to outstrip the cost of the equipment itself. Due to the magnification of cost, the historical approach to equipment installs has been to purchase and install the highest-quality and most robust equipment available to maximize time between failures and updates. To make room for the installation of a new lathe, the inspection GB is being moved to a different location within the machining lab. As part of the overall effort, the equipment within the inspection GB is being revisited. Current measurement capabilities are antiquated and starting to become problematic due to age. Advances in metrology are sought for more comprehensive characterization of scientific samples. This document aims to identify several options for the replacement of current measurement equipment and identify a system for purchase and eventual installation. The intention is not to select the cheapest system, but rather to identify a system that best meets the needs of both the operators and principal investigators (PI). Following the convention of the SYSM-5620 Design Thinking and System Engineering, it was determined that a laser confocal measurement system from Keyence is the most suitable option for our programmatic need. Form factor, capability, and maintainability were considered. Along with stakeholder input, to ensure the best decision for the Nuclear Material Technology Program.
The UTEC-1 LIBS Retrofit and Expansion Project aimed to retrofit and expand Braskem’s UHMWPE unit in La Porte, TX to produce lithium-ion battery separator (LIBS) grade material and increase capacity. The project sought to strengthen the domestic supply chain for advanced battery manufacturing and reduce reliance on imports. While significant progress was achieved in conceptual design and FEL-2 engineering, strategic realignment and external factors led to project termination prior to FEL-3 and FEED execution.
The goal of this project was to probe fundamental kinetics questions regarding the gas phase reactive behavior of oxygenates. In order to succeed, our program necessitated both the development of new experimental tools and the development of theory based kinetic models to explain the chemistry of oxygenate fuels. By using advanced hybrid additive manufacturing/ traditional manufacturing techniques to create specialized ceramic micro-reactors, the team was able to simulate extreme high-temperature environments with much greater precision and durability than was previously possible. These tiny, high-tech tubes allow researchers to capture and identify "fleeting" chemical species—molecules that exist for only a fraction of a millisecond—using sophisticated light sources and mass spectrometry. Additionally, the team supported the development of a tabletop VUV laser system for isomer detection without a synchrotron. Through this work, we have successfully mapped out the specific chemical pathways of various oxygen-rich fuels, solving long-standing mysteries about how these substances break down kinetically. Ultimately, this research provides the fundamental knowledge needed to design next-generation engines and fuels that are better for the environment.
The CIE for Power Generation: Insights and Case Studies guide is being developed to assist engineers at utilities, asset owner-operators developers, and cybersecurity teams to build in robustness and cyber resiliency into their designs using cyber-informed engineering practices. This guide will break out these topics including use cases by chapters for areas such as Nuclear, IBRs, Geothermal, natural gas, etc.
This project purchased equipment to advance hydrogen technology research at the University at Buffalo. The equipment is installed in laboratories in the Departments of Chemical and Biological Engineering (CBE) and Materials Design and Innovation (MDI) at the University at Buffalo (UB).
Retrofitting existing central plants with high-efficiency heat pump technologies can play a crucial role in achieving long-term planning goals. Modern heat pump technologies are able to use waste heat recovery to meet a building's heating demand, but there is a lack of accessible tools designed for non-HVAC experts, such as building owners, to quickly and easily conduct what-if analysis, e.g., estimating retrofit costs and payback period for their partial or full equipment replacement. This paper introduces an open-source software toolkit designed to facilitate the initial screening and decision-making of heat pump retrofits in existing central plants using a building's yearly load profile from metered or utility bill data. The toolkit evaluates the technical and economic viability of replacing traditional central plant equipment with various options including water-to-water or air-to-water heat pumps, which can provide efficient and lower-cost heating and cooling. It allows users to compare current central plant configurations with retrofit scenarios, assessing energy consumption, life-cycle costs, and environmental impact. The toolkit offers (1) a web-based tool designed for user-friendly access by a broad audience and (2) Python-based source code for researchers and engineers conducting parametric studies and design parameter optimization. The toolkit compares a typical central plant configuration to a configuration that uses a heat pump to supply hydronic heating and cooling. The output metrics include energy consumption and output of each equipment, life-cycle cost analyses and metrics, and environmental impact of the system.
The CHIPS R&D project focuses on development of low-cost water Cherenkov neutrino detectors through novel design strategies and resourceful engineering. This work presents an end-to-end DAQ solution intended for a recent 5 kt CHIPS prototype, which is largely based on affordable mass-produced components. Much like the detector itself, the presented instrumentation is composed of modular arrays that can be scaled up and easily serviced. A single such array can carry up to 30 photomultiplier tubes (PMTs) accompanied by electronics that generate high voltage in-situ and deliver time resolution of up to 0.69 ns. In addition, the technology is compatible with the White Rabbit timing system, which can synchronize its elements to within 100 ps. While deployment issues did not permit the presented DAQ system to operate beyond initial evaluation, the presented hardware and software successfully passed numerous commissioning tests that demonstrated their viability for use in a large-scale neutrino detector, instrumented with thousands of PMTs.
High-performance buildings are reliable, cost effective, and sustainable structures that minimize energy and water use, reduce solid waste and pollutant emissions, and limit the depletion of natural resources. High-performance buildings also provide a thermally and visually comfortable working environment that increases productivity for building occupants. As Idaho National Laboratory (INL) is the nation’s premier nuclear energy research laboratory, the physical infrastructure requires continual updating and repurposing to help accomplish that mission. INL’s infrastructure must incorporate high-performance sustainable design features to be fiscally responsible and reflect an image of innovation to the public and prospective employees. INL is a large consumer of energy with annual energy costs exceeding $16M. This High-Performance and Sustainable Building Strategy will help engineering and construction project teams design sustainable facilities, reduce life cycle operating costs, and support the INL net-zero plan while providing INL employees with a safe and healthy working environment. With these goals in mind, the recommendations described in this document are intended to form INL’s foundation for sustainable and high-performance building standards. This strategy incorporates the latest federal and Department of Energy (DOE) orders and directives, including DOE Order 436.1A, “Departmental Sustainability,” the DOE Sustainability Plan (SP), the INL Site Sustainability Plan (SSP), and Code of Federal Regulations (CFR). This document identifies the requirements of the “Guiding Principles for Sustainable Federal Buildings” (Guiding Principles) and briefly highlights the Leadership in Energy and Environmental Design (LEED) Gold certification. LEED Gold certification can be used to meet many of the requirements of the Guiding Principles.
The advancement of artificial intelligence systems enables the use of data-driven physics-based surrogate models to explore design spaces rapidly and deeply for engineering projects. This work presents a surrogate model workflow that accelerates electric traction motor design optimization by replacing finite element analysis (FEA) with an artificial neural network (ANN) and using this model in a genetic algorithm for design optimization. A baseline interior permanent-magnet motor is parameterized and sampled to generate FEA-labeled training data, after which a feed-forward ANN predicts key outputs (e.g., loss components and weight). The validated surrogate enables genetic-algorithm optimization and deep search over the design space without new FEA runs, producing Pareto-optimal trade-offs between weight and losses and set of optimized designs for rapid downselection of manufacturable motor designs.
Abstract After more than 20 years of operation, the cold compressor technology in use at the Spallation Neutron Source (SNS) is obsolete, and replacement parts and service are no longer available. SNS has partnered with Jefferson Lab to design and construct a replacement sub-atmospheric cold box outfitted with modern cold compressor technology. The general design follows from Jefferson Lab’s experience on other recent sub-atmospheric cold box projects. Design decisions are backed by thorough engineering analysis to ensure technical requirements are met in a cost-effective manner. Conceptual design of the replacement cold box has been completed, and will be summarized in this paper. It features five cold compressors with an operating flow range of 80-140 g/s, and includes piping and valving to support cold compressor maintenance without interrupting flow circulation to the load. The approach to thermal shielding, insulation, and integration of the upgraded cold compressor hardware into the existing SNS control system will also be addressed.
In this study, we systematically design and simulate a series of GaAs-based superlattice configurations aimed at enhancing heavy-hole–light-hole band splitting while simultaneously optimizing band alignment to reduce the conduction band barrier, thereby facilitating efficient electron transport. These combined effects are crucial for achieving high electron spin polarization and high quantum efficiency, the two key performance metrics of next-generation spin-polarized electron sources. We investigated three types of superlattice architectures: (1) compressively strained GaAs wells on GaInP barriers, yielding a maximum band splitting of 140 meV, (2) lattice-matched GaAs/GaInP structures, resulting in the maximum band splitting of 75 meV, and (3) tensile strained GaAs wells on GaInP barriers, with a maximum band splitting of 40 meV. The results demonstrate the tunability of heavy-hole–light-hole band splitting and establish a design framework for high-performance spin-polarized photocathodes based on a combination of strain engineering, quantum confinement, and optimized heterostructure design.
This project developed a Wave Energy Converter (WEC) system design that was ready for fabrication, deployment, and prototype testing at PacWave. The WEC design incorporated the International Electrotechnical Commission (IEC) Technical Specifications (TS) and Institute of Electrical and Electronics Engineers (IEEE) standards to ensure that designs are fully ready to utilize for future fabrication and open-water testing. Moreover, the project began the certification process with a certification provider, allowing for a seamless continuation into future work beyond project-end.
Questions of ‘how best to acquire data’ are essential to modelling and prediction in the natural and social sciences, engineering applications, and beyond. Optimal experimental design (OED) formalizes these questions and creates computational methods to answer them. This article presents a systematic survey of modern OED, from its foundations in classical design theory to current research involving OED for complex models. We begin by reviewing criteria used to formulate an OED problem and thus to encode the goal of performing an experiment. We emphasize the flexibility of the Bayesian and decision-theoretic approach, which encompasses information-based criteria that are well-suited to nonlinear and non-Gaussian statistical models. We then discuss methods for estimating or bounding the values of these design criteria; this endeavour can be quite challenging due to strong nonlinearities, high parameter dimension, large per-sample costs, or settings where the model is implicit. A complementary set of computational issues involves optimization methods used to find a design; we discuss such methods in the discrete (combinatorial) setting of observation selection and in settings where an exact design can be continuously parametrized. Finally we present emerging methods for sequential OED that build non-myopic design policies, rather than explicit designs; these methods naturally adapt to the outcomes of past experiments in proposing new experiments, while seeking coordination among all experiments to be performed. Throughout, we highlight important open questions and challenges.