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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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Synergistically combining peracetic acid and reduced graphene oxide membranes to degrade trace organic contaminants
The removal of trace organic contaminants is a critical step for the reuse of municipal and industrial wastewater. Herein, we demonstrate a catalytic membrane platform synergistically integrating reduced graphene oxide (rGO) membranes and peracetic acid (PAA) oxidation, using a dye of methylene blue (MB) and a pesticide of 2,4-dichlorophenoxyacetic acid (2,4-D) as model contaminants. In a crossflow system with rGO membrane and 25 ppm PAA, the degradation rates of MB and 2,4-D were 22 and 0.12 g h −1 per g rGO, with an initial concentration of 10 and 1.1 ppm, respectively. The degradation time profile of MB and 2,4-D follows a pseudo-first-order model, and the degradation rate constant increases with increasing initial PAA doses. Here, the rGO membranes also exhibited good stability over a 1-month test for 2,4-D degradation. In addition to the nanofiltration performance of the rGO membrane, the integrated PAA-rGO membrane process shows great potential to degrade various organic contaminants without the need to separate and recover the metal-free catalyst in downstream treatment.
Combined effects of ambient air pollution and home environmental factors on low birth weight
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A Tchebycheff-based multi-objective combined with a PSO–SQP dynamic real-time optimization framework for cycling energy systems
The aim in this work is to develop an optimization framework to address cycling of baseload energy systems due to the penetration of renewables into the grid. The developed strategy corresponds to a multi-objective and dynamic real-time optimization (MOO-DRTO) framework applied to a postcombustion MEA-based CO 2 capture process from a baseload coal-fired power plant under cycling conditions. A Tchebycheff-based method is used for the multi-objective optimization (MOO) component. Also, a hybrid approach consisting of Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) is implemented for the first time in process systems engineering to solve the dynamic real-time optimization (DRTO) component. The objectives considered in the MOO-DRTO framework are economic and environmental. The proposed MOO-DRTO strategy is successfully implemented and 24-h optimal output trajectories for the carbon capture system under cycling are generated. Also, the optimal compromise is chosen from the Pareto front according to a set of selected weights for the objectives with minimal interaction between the framework and decision maker. The results indicated that the developed framework has potential to be extended to plant-wide optimization applications under cycling.
Do Patients With Low-Risk Pulmonary Arterial Hypertension Really Benefit From Upfront Combination Therapy?
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A combination of cross-neutralizing antibodies synergizes to prevent SARS-CoV-2 and SARS-CoV pseudovirus infection
Coronaviruses have caused several human epidemics and pandemics including the ongoing coronavirus disease 2019 (COVID-19). Prophylactic vaccines and therapeutic antibodies have already shown striking effectiveness against COVID-19. Nevertheless, concerns remain about antigenic drift in SARS-CoV-2 as well as threats from other sarbecoviruses. Cross-neutralizing antibodies to SARS-related viruses provide opportunities to address such concerns. Here, we report on crystal structures of a cross-neutralizing antibody, CV38-142, in complex with the receptor-binding domains from SARS-CoV-2 and SARS-CoV. Recognition of the N343 glycosylation site and water-mediated interactions facilitate cross-reactivity of CV38-142 to SARS-related viruses, allowing the antibody to accommodate antigenic variation in these viruses. CV38-142 synergizes with other cross-neutralizing antibodies, notably COVA1-16, to enhance neutralization of SARS-CoV and SARS-CoV-2, including circulating variants of concern B.1.1.7 and B.1.351. Overall, this study provides valuable information for vaccine and therapeutic design to address current and future antigenic drift in SARS-CoV-2 and to protect against zoonotic SARS-related coronaviruses.
Crosslinked polymeric ionic liquid stationary phases incorporating a combination of silver(I) and copper(II) ions for olefin separations by gas chromatography
The development of efficient and selective olefin separation technologies is a critical challenge within the chemical industry given the high energy demands of conventional cryogenic distillation methods. To address this, the study explores the effectiveness of polymeric ionic liquid (PIL) stationary phases in gas chromatography (GC) that incorporate both silver(I) ([Ag + ]) and copper(II) ([Cu 2+ ]) ions for enhanced olefin separation via π-complexation interactions. Further, the effects of both non-crosslinked and crosslinked PIL stationary phases containing varying ratios of [Ag⁺] and [Cu²⁺] ions were systematically investigated to evaluate their impact on the separation of olefins from paraffins and structurally similar olefins. Retention measurements using a broad range of olefinic probes revealed that [Ag⁺] ions provided stronger and more consistent π-complexation in PIL-based stationary phases, while [Cu²⁺] ions preferentially enhance olefin retention in crosslinked systems. Furthermore, thermal stability studies showed that [Ag + ]-containing PIL stationary phases maintain stable olefin separation efficiency over prolonged exposure under elevated temperatures, while crosslinked PIL stationary phases containing [Cu 2+ ] ions exhibited stronger retention due to increased surface adsorption. These findings provide insight into the tunability of metal–olefin interactions using PIL-based stationary phases and suggest design strategies for achieving thermally stable and highly selective chromatographic separations of unsaturated hydrocarbons.
Surface location error in robotic milling: Effect of combined low frequency and high frequency vibration modes
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Discovering a reaction–diffusion model for Alzheimer’s disease by combining PINNs with symbolic regression
Misfolded tau proteins play a critical role in the progression and pathology of Alzheimer's disease. Recent studies suggest that the spatio-temporal pattern of misfolded tau follows a reaction-diffusion type equation. However, the precise mathematical model and parameters that characterize the progression of misfolded protein across the brain remain incompletely understood. Here, we use deep learning and artificial intelligence to discover a mathematical model for the progression of Alzheimer's disease using longitudinal tau positron emission tomography from the Alzheimer's Disease Neuroimaging Initiative database. Specifically, we integrate physics informed neural networks (PINNs) and symbolic regression to discover a reaction-diffusion type partial differential equation for tau protein misfolding and spreading. First, we demonstrate the potential of our model and parameter discovery on synthetic data. Then, we apply our method to discover the best model and parameters to explain tau imaging data from 46 individuals who are likely to develop Alzheimer's disease and 30 healthy controls. Our symbolic regression discovers different misfolding models f(c) for two groups, with a faster misfolding for the Alzheimer's group, f(c) = 0.23c 3 – 1.34c 2 + 1.11c, than for the healthy control group, f(c) = –c 3 + 0.62c 2 + 0.39c. Our results suggest that PINNs, supplemented by symbolic regression, can discover a reaction-diffusion type model to explain misfolded tau protein concentrations in Alzheimer's disease. Furthermore, we expect our study to be the starting point for a more holistic analysis to provide image-based technologies for early diagnosis, and ideally early treatment of neurodegeneration in Alzheimer's disease and possibly other misfolding-protein based neurodegenerative disorders.
A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young’s modulus
The multicomponent Ti alloys, specifically the -phase, have experienced a strong growth over the last decades, due to their outstanding properties of ultra-high strength and low Young’s modulus. These properties play a significant role in many aerospace and biomedical applications. Selection and optimization of multicomponent alloys is challenging due to the vast chemical and compositional space. Here we investigate the use of machine learning techniques informed by density functional calculations to guide the selection of Nb- and Zr-based Ti binary alloys. From the cubic structures obtained from high throughput calculations and literature, we identify several structures with Young’s moduli below 40 GPa. The multivariant decision tree methods provide efficient surrogate models to identify structure variables have high influences on the energetic stability and Young’s modulus. Furthermore, we implement a workflow of incorporating DFT provided results and machine learning method to explore the chemical and composition space of other binary and multicomponent alloys, to eventually accelerate the material design via taking advantages of identified key variables.
Techno-economic analysis of a combined power plant CO2 capture and direct air capture concept for flexible power plant operation
Not Available
A combined experimental and computational analysis of failure mechanisms in open-hole cross-ply laminates under flexural loading
In this work, integrated experimental tests and computational modeling are proposed to investigate the failure mechanisms of open-hole cross-ply carbon fiber reinforced polymer (CFRP) laminated composites. In particular, we propose two effective methods, which include width-tapered double cantilever beam (WTDCB) and fixed-ratio mixed-mode end load split (FRMMELS) tests, to obtain the experimental data more reliably. We then calibrate the traction-separation laws of cohesive zone model (CZM) used among laminas of the composites by leveraging these two methods. The experimental results of fracture energy, i.e. G Ic and G Tc , obtained from WTDCB and FRMMELS tests are generally insensitive to the crack length thus requiring no effort to accurately measure the crack tip. Moreover, FRMMELS sample contains a fixed mixed-mode ratio of G IIc /G Tc depending on the width taper ratio. Examining comparisons between experimental results of FRMMELS tests and failure surface of B–K failure criterion predicted from a curve fitting, good agreement between the predictions and experimental data has been found, indicating that FRMMELS tests are an effective method to determine mixed-mode fracture criterion. In addition, a coupled experimental-computational modeling of WTDCB, edge notched flexure, and FRMMELS tests are adopted to calibrate and validate the interfacial strengths. Finally, failure mechanisms of open-hole cross-ply CFRP laminates under flexural loading have been studied systematically using experimental and multi-scale computational analyses based on the developed CZM model. The initiation and propagation of delamination, the failure of laminated layers as well as load-displacement curves predicted from computational analyses are in good agreement with what we have observed experimentally.