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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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Novel economically-sustainable and environmentally-friendly technologies for per- and poly-fluoroalkyl substance (PFAS) destruction are becoming increasingly important as PFAS contamination has increased in drinking water throughout the globe.
Drinking water treatment residuals (DWTRs) are complex mixtures of organic and inorganic phases generally disposed of as waste materials. However, their strong sorptive properties could be further exploited to immobilize contaminants. To characterize these materials, we applied a range of analytical techniques to a set of aluminum-based DWTRs. Here we determined surface areas, elemental compositions using CHNS analysis and X-ray fluorescence (XRF), performed thermogravimetric analyses paired with mass spectrometry (TGA-MS), and used synchrotron-based methods: X-ray diffraction (XRD) and X-ray absorption spectroscopy (XAS). Elemental analyses and specific surface area measurements – that vary between 7.23 ± 0.03 and 197.6 ± 0.9 m 2 g –1 – indicate that non-coagulant additives must play an important role in controlling sorption. High resolution powder XRD reveals the presence of only a few crystalline minerals mostly derived from source waters or additives. Fe was detected by XRF in all samples, whereas Mn was present in significant levels in samples with potassium permanganate as additive. The chemical speciation of these elements was characterized by performing spectral decompositions of XAS spectra. The spectral features of Fe are consistent with iron(III) oxides/hydroxides and structural iron in clays. On the other hand, Mn is predominantly present under its reduced form, Mn(II).
Significance The kinetic assessment of catalytic reactions is essential to the design and application of thermochemical processes useful in petrochemical synthesis, energy conversion, and environmental remediation. We demonstrate that the ubiquitously used Langmuir–Hinshelwood formalism is incomplete and fails to correctly describe even simple catalytic reactions (e.g., A + A → A 2 ). An accurate kinetic description requires explicit rate equations for multisite configurations (e.g., A*–A* pairs) to account for the nonrandom clustering/isolation of surface species. We rigorously describe clustering/isolation phenomena by derivation of coverage-dependent stoichiometric coefficients, which capture the distinct influence each elementary step exerts on each catalytic site ensemble—thereby overcoming limitations of the mean-field (i.e., Hinshelwood) assumption in describing reactions of mobile Langmuirian adsorbates on catalytic surfaces.
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Abstract This study presents incremental finite element computations of creep‐fatigue crack growth in Alloy 718 at in air. Alloy 718 is representative of creep‐brittle materials, in which viscoplastic deformation is restricted near the tip of a growing crack. The computations predict crack growth using a unique combination of an irreversible cohesive zone formulation and a strain gradient viscoplastic material model based on the Kocks–Mecking formulation. Cohesive zone damage parameters are estimated using sustained loading and constant‐amplitude cyclic loading experiments. Computations of crack extension under three different waveforms containing overloads all predict post‐overload retardation. The amount of retardation depends strongly on the overload ratio, consistent with experiments in the literature using similar waveforms. Analysis of the crack‐tip fields demonstrates retardation is associated with unloading in the highly deformed material near the advancing crack tip. Dynamic recovery and geometrically necessary dislocations are shown to significantly influence post‐overload crack extension.
Here, photoluminescence (PL) mapping was utilized to investigate damage in β-Ga 2 O 3 epilayers induced by 1064 nm laser pulses. The intensity and position of the intrinsic UV band were determined and plotted as a false-color image. Two types of damage were identified: circular damage and damage cracks. Circular damage shows lower UV PL intensity than the surrounding material with color centers in a “halo” around the damaged region. Damage cracks are aligned with the a and c axes and show higher PL intensity than undamaged material. Defects in the as-grown material were revealed by shifts in the UV band energy.
Abstract Artificial intelligence and machine learning (ML) continue to see increasing interest in science and engineering every year. Polymer science is no different, though implementation of data-driven algorithms in this subfield has unique challenges barring widespread application of these techniques to the study of polymer systems. In this Prospective, we discuss several critical challenges to implementation of ML in polymer science, including polymer structure and representation, high-throughput techniques and limitations, and limited data availability. Promising studies targeting resolution of these issues are explored, and contemporary research demonstrating the potential of ML in polymer science despite existing obstacles are discussed. Finally, we present an outlook for ML in polymer science moving forward. Graphical Abstract
The United States Department of Energy (DOE) laboratory system employs over 78,000 people and had a budget of $8.1 billion for Fiscal Year 2023 (FY23). This diverse workforce includes employees at all levels from operational support staff to senior level management. Together, the labs take on incredibly complex and unique technical challenges in an attempt to address fundamental National and global needs. These technical challenges often require specialized training such that only a single person is truly capable of completing specific tasks. With the modern competitive environment, and an aging workforce, the risk of technical capability loss is highly probable with potentially severe impact. An online survey of Lawrence Livermore National Laboratories (LLNL) employees, a typical DOE lab, found that of responders with greater than 10 years of experience, 100% of respondents felt burnt out, 40% felt they were not paid fairly, and 50% believe that not enough is done by leadership to retain them as an employee. Couple that to an aging workforce, and the risk of losing institutional knowledge due to single point of failure, or lynchpin, is significant. There is a need for a system which reduces the risk of operational and technical losses due to lynchpin employees leaving the organization. A mitigation of this risk would lead to the successful transfer of knowledge from one generation to the next, improving the rate of development and minimizing general risk of mistakes.
Marine energy structures are typically made of advanced composite materials and are subjected to extreme ocean environments in service. In extreme ocean environments, seawater currents and waves load structures repeatedly, which cause two environmental conditions: water intrusion and mechanical fatigue. In prior research, the two environmental conditions were applied sequentially, where composite specimens were aged and then mechanically tested. To understand the combined effects of dynamic loading and water intrusion on composite materials, the present study involves the static and fatigue four-point bend testing of composite coupons in a water tank. The water tank was designed and built to fit either a 100 kN or a 250 kN load frame. Flexural strength value, cycles to failure, and failure mode results from submerged fatigue testing will be used to inform marine energy structure designs. The coupon-scale test method will be used to scale up to and inform methods for subsequent subcomponent testing and standards development. Finally, the benefits of designing marine energy structures to informed standards are decreased lifetime costs and increased reliability and energy production, ultimately leading to a sustainable and low-carbon energy system.
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Large language models (LLMs) are increasingly capable of answering technical questions, synthesizing domain knowledge, and supporting engineering workflows. For nuclear science and engineering, these capabilities require careful, domain-specific evaluation before they can be credibly incorporated into safety-related activities, regulatory review, or technical decision support. This paper presents preliminary results from benchmarking framework for evaluating LLM capabilities in nuclear contexts. The framework is organized into three evaluation categories: nuclear fundamentals, general dual-use knowledge, and plant specific knowledge. These categories are intended to distinguish general nuclear engineering competence from broader technical reasoning and more context-dependent nuclear knowledge. Initial evaluations focus on nuclear fundamentals using questions representative of the knowledge expected of a nuclear professional engineer. Results indicate that contemporary frontier models perform at a high level and substantially exceed the performance of older model generations, with some models approaching saturation of the current benchmark. These findings suggest both the rapid improvement of LLM capabilities in specialized technical domains and the need for more discriminating evaluation methods. The paper presents the benchmark structure, preliminary model-comparison results, and ongoing work. This work supports development of verifiable, responsible, and safety-conscious methods for assessing AI systems in nuclear engineering applications.
An optimized ether-based electrolyte is designed to suppress solvent co-intercalation and decomposition of graphite anodes, thereby enabling lithium–sulfur batteries with low N/P ratio.