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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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In this work, the chronic toxicity of an innovative Hg water treatment system using tin (Sn) (II) chloride (SnCl 2 ) followed by air stripping was assessed through measurements of survival, growth, and reproduction rate in the freshwater cladoceran Ceriodaphnia dubia, a model species for toxicity testing. We first calculated the concentrations of Hg causing 25% reduction in survival and reproduction (Lethal or Inhibition Concentrations, or LC 25 and IC 25 , for survival and reproduction, respectively) through exposure to aqueous Hg at concentrations ranging from 0 to 25,000 ng L -1 . Then, we treated media (DMW and natural stream water) contaminated with Hg at LC 25 and IC 25 concentrations with SnCl 2 at a Sn:Hg stoichiometric ratio of 8:1 and air stripping and exposed C. dubia to this Sn-amended media. Our results showed that Hg significantly affected survival, reproduction rates and impaired growth. SnCl 2 -treatment removed 100% of the Hg from the media at all concentrations tested with no deleterious effects on survival, growth and reproduction. Our results confirmed the efficacy of SnCl 2 in removing aqueous Hg from stream water and showed that the added Sn did not impact C. dubia at the concentrations tested, supporting the suitability of SnCl 2 -based treatments in appropriate Hg-contaminated environments.
Ion soft landing (SL) is a preparative mass spectrometry approach that enables deposition of mass- and charge-selected gaseous ions onto surfaces with controlled kinetic energy. The unique capabilities of SL provide an opportunity to populate electrode–electrolyte interfaces (EEIs) with well-defined intact electroactive ions of known composition, thereby facilitating the characterization of their intrinsic electrochemical properties. Here, in this perspective, we describe the SL technique and discuss how it may be used to study the effect of the charge state, stoichiometry, and composition of large redox active molecules and clusters on their electron transfer kinetics. SL has enabled the characterization of redox-active species that cannot be purified and examined using conventional bulk-phase separation and deposition approaches. Furthermore, precise control over the deposition process provides an opportunity to prepare and characterize well-defined EEIs relevant to energy storage and conversion, catalysis, and sensing.
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Agricultural systems are under increasing pressure from declining environmental conditions, a growing population, and changes in consumer preferences, resulting in widespread malnutrition-related illnesses. Improving plant nutritional content through biotechnology techniques such as synthetic biology is a promising strategy to help combat hidden hunger caused by the lack of affordable and healthy foods in human diets. Production of compounds usually found in animal-rich diets, such as vitamin D or omega-3 fatty acids, has been recently demonstrated in planta. Here, we review recent biotechnological approaches to biofortifying plants with vitamins, minerals, and other metabolites, and summarise synthetic biology advances that offer the opportunity to build on these early biofortification efforts.
Zymomonas mobilis is an ethanologenic bacterium that has been used for over 1500 years to produce alcoholic beverages. Recently, this microbe has become a top candidate for biofuel production due to its efficient metabolism. Z. mobilis is being developed to utilize lignocellulosic biomass as a feedstock and synthesize a range of valuable chemicals and fuels. Genetic and metabolic engineering strategies are crucial to reach these goals. Recent advances include genome engineering, CRISPR editing, and CRISPRi knockdown of genes. Metabolic engineering has enabled redirection of carbon from the natural product ethanol to chemicals such as 2,3-butanediol and polyhydroxybutyrate. Finally, the approaches summarized here will streamline the development of Z. mobilis as an industrial chassis for sustainable liquid fuels and chemicals.
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Nonlinear Optical Spectroscopy is a well-developed field with theoretical and experimental advances that have benefited multiple disciplines, including chemistry, biology, and physics. However, for the accurate interpretation of the corresponding multi-dimensional spectra, there is a need for precise quantum dynamical simulations based on model Hamiltonians. In this article, we present the initial release of our code, QuDPy (Quantum Dynamics in Python), which provides a robust numerical platform for performing quantum dynamics simulations based on model systems, including open quantum systems. Furthermore, a distinguishing feature of our approach is the ability to specify various high-order optical response pathways in the form of double-sided Feynman diagrams through a straightforward input syntax. This syntax outlines the time-ordering of ket-sided or bra-sided optical interactions acting on the time-evolving density matrix of the system. We utilize the quantum dynamics capabilities of QuTip to simulate the spectral response of complex systems, allowing us to compute virtually any $n$-th order optical response of the model system. To illustrate the utility of our approach, we provide a series of example calculations.
Here, in this paper, we present a new static and time-dependent MagnetoHydroDynamic (MHD) equilibrium code, TokaMaker, for axisymmetric configurations of magnetized plasmas, based on the well-known Grad-Shafranov equation. This code utilizes finite element methods on an unstructured triangular grid to enable capturing accurate machine geometry and simple mesh generation from engineering-like descriptions of present and future devices. The new code is designed for ease of use without sacrificing capability and speed through a combination of Python, Fortran, and C/C++ components. A detailed description of the numerical methods of the code, including a novel formulation of the boundary conditions for free-boundary equilibria, and validation of the implementation of those methods using both analytic test cases and cross-code validation is shown. Results show expected convergence across tested polynomial degree for analytic and cross-code test cases.
Characterizing the functional properties of plant acyl-ACP thioesterases (TEs), a key enzyme class used in the production of renewable oleochemicals in microbial hosts, experimentally, can be an expensive and time consuming process since it requires manual screening of thousands of candidates in a database. Using amino acid sequence to computationally predict an enzyme’s function might accelerate this process; however obtaining the necessary amount of information on previously characterized enzymes and their respective sequences required by standard Machine Learning (ML) based approaches to accurately infer sequence-function relationships can be prohibitive, especially with a low-throughput testing cycle. Experimental noise, unbalanced dataset where high sequence similarity does not always imply identical functional properties will further prevent robust prediction performance. Herein we present a ML method, Ensemble method for enZyme Classification (EnZymClass), that is specifically designed to address these issues. We used EnZymClass to classify TEs into short, long and mixed free fatty acid substrate specificity categories. While general guidelines for inferring substrate specificity have been proposed before, prediction of chain-length preference from primary sequence has remained elusive for plant acyl-ACP TEs. By applying EnZymClass to a subset of TEs in the ThYme database, we identified two medium chain TEs, ClFatB3 and CwFatB2, with previously uncharacterized activity in E. coli fatty acid production hosts.