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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Co‑cultivation of anaerobic fungi with Clostridium acetobutylicum bolsters butyrate and butanol production from cellulose and lignocellulose

A system for co-cultivation of anaerobic fungi with anaerobic bacteria was established based on lactate cross-feeding to produce butyrate and butanol from plant biomass. Several co-culture formulations were assembled that consisted of anaerobic fungi (Anaeromyces robustus, Neocallimastix californiae, or Caecomyces churrovis) with the bacterium Clostridium acetobutylicum. Co-cultures were grown simultaneously (e.g., ‘one pot’), and compared to cultures where bacteria were cultured in fungal hydrolysate sequentially. Fungal hydrolysis of lignocellulose resulted in 7–11 mM amounts of glucose and xylose, as well as acetate, formate, ethanol, and lactate to support clostridial growth. Under these conditions, one-stage simultaneous co-culture of anaerobic fungi with C. acetobutylicum promoted the production of butyrate up to 30 mM. Alternatively, two-stage growth slightly promoted solventogenesis and elevated butanol levels (∼4–9 mM). Transcriptional regulation in the two-stage growth condition indicated that this cultivation method may decrease the time required to reach solventogenesis and induce the expression of cellulose-degrading genes in C. acetobutylicum due to relieved carbon-catabolite repression. Overall, this study demonstrates a proof of concept for biobutanol and bio-butyrate production from lignocellulose using an anaerobic fungal-bacterial co-culture system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Purple non-sulfur bacteria for biotechnological applications

Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.

Morrison, Hailee M. (ORCID:0000000225121824)↗
DOE OSTI2022

A reliable workflow for improving nanoscale X-ray fluorescence tomographic analysis on nanoparticle-treated HeLa cells

Abstract Scanning X-ray fluorescence (XRF) tomography provides powerful characterization capabilities in evaluating elemental distribution and differentiating their inter- and intra-cellular interactions in a three-dimensional (3D) space. Scanning XRF tomography encounters practical challenges from the sample itself, where the range of rotation angles is limited by geometric constraints, involving sample substrates or nearby features either blocking or converging into the field of view. This study aims to develop a reliable and efficient workflow that can (1) expand the experimental window for nanoscale tomographic analysis of local areas of interest within a laterally extended specimen, and (2) bridge 3D analysis at micrometer and nanoscales on the same specimen. We demonstrate the workflow using a specimen of HeLa cells exposed to iron oxide core and titanium dioxide shell (Fe3O4/TiO2) nanocomposites. The workflow utilizes iterative and multiscale XRF data collection with intermediate sample processing by focused ion beam (FIB) sample preparation between measurements at different length scales. Initial assessment combined with precise sample manipulation via FIB allows direct removal of sample regions that are obstacles to both incident X-ray beam and outgoing XRF signals, which considerably improves the subsequent nanoscale tomography analysis. This multiscale analysis workflow has advanced bio-nanotechnology studies by providing deep insights into the interaction between nanocomposites and single cells at a subcellular level as well as statistical assessments from measuring a population of cells.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Screening the complex biological behavior of late lanthanides through genome-wide interactions

Abstract Despite their similar physicochemical properties, recent studies have demonstrated that lanthanides can display different biological behaviors. Hence, the lanthanide series can be divided into three parts, namely early, mid, and late lanthanides, based on their interactions with biological systems. In particular, the late lanthanides demonstrate distinct, but poorly understood biological activity. In the current study, we employed genome-wide functional screening to help understand biological effects of exposure to Yb(III) and Lu(III), which were selected as representatives of the late lanthanides. As a model organism, we used Saccharomyces cerevisiae, since it shares many biological functions with humans. Analysis of the functional screening results indicated toxicity of late lanthanides is consistent with disruption of vesicle-mediated transport, and further supported a role for calcium transport processes and mitophagy in mitigating toxicity. Unexpectedly, our analysis suggested that late lanthanides target proteins with SH3 domains, which may underlie the observed toxicity. This study provides fundamental insights into the unique biological chemistry of late lanthanides, which may help devise new avenues toward the development of decorporation strategies and bio-inspired separation processes.

Pallares, Roger M. (ORCID:0000000174238706)↗
DOE OSTI2024

A century of studying plant secondary metabolism—From “what?” to “where, how, and why?”

Abstract Over the past century, early advances in understanding the identity of the chemicals that collectively form a living plant have led scientists to deeper investigations exploring where these molecules localize, how they are made, and why they are synthesized in the first place. Many small molecules are specific to the plant kingdom and have been termed plant secondary metabolites, despite the fact that they can play primary and essential roles in plant structure, development, and response to the environment. The past 100 yr have witnessed elucidation of the structure, function, localization, and biosynthesis of selected plant secondary metabolites. Nevertheless, many mysteries remain about the vast diversity of chemicals produced by plants and their roles in plant biology. From early work characterizing unpurified plant extracts, to modern integration of ‘omics technology to discover genes in metabolite biosynthesis and perception, research in plant (bio)chemistry has produced knowledge with substantial benefits for society, including human medicine and agricultural biotechnology. Here, we review the history of this work and offer suggestions for future areas of exploration. We also highlight some of the recently developed technologies that are leading to ongoing research advances.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Efficient secretion of a plastic degrading enzyme from the green algae Chlamydomonas reinhardtii

AbstractPlastic pollution has become a global crisis, with microplastics contaminating every environment on the planet, including our food, water, and even our bodies. In response, there is a growing interest in developing plastics that biodegrade naturally, thus avoiding the creation of persistent microplastics. As a mechanism to increase the rate of polyester plastic degradation, we examined the potential of using the green microalgaChlamydomonas reinhardtiifor the expression and secretion of PHL7, an enzyme that breaks down post-consumer polyethylene terephthalate (PET) plastics. We engineeredC. reinhardtiito secrete active PHL7 enzyme and selected strains showing robust expression, by using agar plates containing a polyester polyurethane (PU) dispersion as an efficient screening tool. This method demonstrated the enzyme’s efficacy in degrading ester bond-containing plastics, such as PET and bio-based polyurethanes, and highlights the potential for microalgae to be implemented in environmental biotechnology. The effectiveness of algal-expressed PHL7 in degrading plastics was shown by incubating PET with the supernatant from engineered strains, resulting in substantial plastic degradation, confirmed by mass spectrometry analysis of terephthalic acid (TPA) formation from PET. Our findings demonstrate the feasibility of polyester plastic recycling using microalgae to produce plastic-degrading enzymes. This eco-friendly approach can support global efforts toward eliminating plastic in our environment, and aligns with the pursuit of low-carbon materials, as these engineered algae can also produce plastic monomer precursors. Finally, this data demonstratesC. reinhardtiicapabilities for recombinant enzyme production and secretion, offering a “green” alternative to traditional industrial enzyme production methods.Graphical Abstract

Molino, João Vitor Dutra (ORCID:0000000324759807)↗
DOE OSTI2025

Supermodulation-driven evolution of the nodal structure of bismuth-based cuprate superconductors

Recent work has shown novel properties of twisted cuprates. In this paper, I point out that related phenomena occur intrinsically in bismuth-based cuprate superconductors due to the presence of the BiO supermodulation. As the ratio of the supermodulation potential to the superconducting energy gap increases, two critical points are found where semi-Dirac nodes form (that is, they have quadratic dispersion in one direction and linear dispersion in the orthogonal direction). The first critical point should be realized in Bi2212, the second in Bi2201. Implications of these findings are discussed.

Cuprates↗
DOE OSTI2020

Excitation of Bloch Surface Waves in Zero-Admittance Multilayers for High-Sensitivity Sensor Applications

The growing need for classical as well as quantum optical sensing places increasingly stringent requirements upon the desired characteristics of the engendered fields. Specifically, achieving superior field enhancement plays a critical role in applications ranging from chem-bio sensing, Raman and infrared spectroscopies to ion trapping and qubit control in emerging quantum-information science. Due to their low optical losses and ability to exhibit resonant field enhancements, all dielectric multilayers are emerging as an optical material system not only useful to classical photonics and sensing but also of potential to be integrated with quantum materials and quantum sensing. The recently introduced concept of zero-admittance layers [1] within dielectric multilayer materials, enables the creation and control of resonant fields orders of magnitude larger than the exciting field. Here, invoking the zero-admittance concept, we design, fabricate, and characterize an all-dielectric nonabsorbing stack and demonstrate the engendered huge field enhancement. Describing the fields in terms of Bloch surface waves, we connect the surface field to the semiperiodicity in the dielectric domains of the stack. As a specific application of the resonant field, we propose and demonstrate refractive-index sensing for the detection of trace amounts of an analyte. The results include a quantification of the sensitivity of the device with respect to the profile of the exciting field. The experimental results are shown to be in good agreement with theoretical calculations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Structure of disordered TiO 2 phases from ab initio based deep neural network simulations

Amorphous TiO 2 (a-TiO 2 ) is widely used in many fields, ranging from photo-electrochemistry to bio-engineering, hence detailed knowledge of its atomic structure is of scientific and technological interest. Here we use an ab initio-based deep neural network potential (DP) to simulate large scale atomic models of crystalline and disordered TiO 2 with molecular dynamics. Our DP reproduces the structural properties of all (11) TiO 2 crystalline phases, predicts the densities and structure factors of molten and amorphous TiO 2 with only a few percent deviation from experiments, and describes the pressure dependence of the amorphous structure in agreement with recent observations. Furthermore, it can be extended to model additional structures and compositions and can be thus of great value in the study of TiO 2 -based (nano-)materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

High-speed quantitative X-ray multi-contrast imaging with deep learning based modulated pattern analysis

The advent of X-ray multi-contrast imaging methods, providing absorption, phase, and dark-field images, holds tremendous promise for complementary and non-destructive visualization of inner structures within materials and bio-samples. However, the low efficiency in measuring and analyzing X-ray modulated patterns has hindered their application in high-resolution in situ imaging. In this work, the Enhanced Scanning Pattern-based Imaging Neural Network (ESPINNet) is introduced as a powerful tool for achieving high-speed, high-resolution quantitative imaging. ESPINNet is faster than correlation-based speckle tracking methods such as XSVT and UMPA, and provides a balanced performance in terms of resolution and speed for data collection by using fewer scanning images. In comparison with our previously developed neural network, ESPINNet introduces the capability to generate dark-field images, further enhancing its versatility. By leveraging scanning patterns, ESPINNet significantly improves resolution and measurement precision. Furthermore, its adaptability to various modulation patterns, including those produced by sandpaper, coded masks, or gratings, ensures broad applicability. These features enable real-time 2D and 3D multi-contrast imaging, positioning ESPINNet as a transformative solution for applications in materials science and biomedical research, particularly for high-speed and in situ measurements.

X-ray at-wavelength metrology↗
DOE OSTI2022

Design and Challenges of Edge Computing ASICs on Front-End Electronics

In situ or hardware-embedded data processing of raw signals, close to their source, in radiation detectors is expected to provide dramatic improvements in data quality and volumes. However, the implementation of artificial neural networks (ANNs) in the front-end electronics, and the design of custom integrated circuits (ASICs), comes with challenges. In addition, detectors have to operate with limited power budget and implement complex functionalities in a very dense space. They often are exposed to extreme conditions as they work in high-radiation environments and/or cryogenic temperatures. This paper presents examples of applications and design methodologies for in-situ ANNs, along with the challenges of retaining the fidelity of the trained networks. For illustration, we use the problem of estimating the energy deposited by the radiation from digitized waveforms. The proposed implementation starts with an ML algorithm trained in Qkeras and eventually leads to an equivalent ASIC implementation. The associated design challenges in realizing energy and area efficient implementations in CMOS processes are reviewed. Novel approaches that employ hybrid technologies (combination of CMOS with memristors), in-memory computing models, and bio-inspired spiking neural networks are also highlighted

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Fusion Intelligence: A Paradigm for Merging Natural and Artificial Intelligence

Here, this article presents fusion intelligence (FI), a bio-inspired paradigm that synergistically integrates the intrinsic capabilities of intelligent biological organisms with the advanced potential of artificial intelligence (AI)-driven systems. FI harnesses the unique intelligence, sensing, actuation, and mobility attributes of living organisms, such as honeybees, blending these with the sophisticated data-driven problem-solving functionalities of AI. By bridging the gap between natural intelligence (NI) and AI, FI can transform how humans interact with and harness the capabilities of both natural and artificial systems. This article presents the model of FI and its application to solve practical problems, discusses the challenges and future directions of FI research, emphasizing a generalized approach to solve complex problems, where AI can observe/control NI in a closed-loop system. We demonstrate the potential for FI to enhance the performance of an agricultural IoT system via a simulated case study, which achieves 50% improvement in the efficacy of insect pollination (entomophily).

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

The phototrophic purple non‐sulfur bacteria Rhodomicrobium spp. are novel chassis for bioplastic production

Abstract Petroleum‐based plastics levy significant environmental and economic costs that can be alleviated with sustainably sourced, biodegradable, and bio‐based polymers such as polyhydroxyalkanoates (PHAs). However, industrial‐scale production of PHAs faces barriers stemming from insufficient product yields and high costs. To address these challenges, we must look beyond the current suite of microbes for PHA production and investigate non‐model organisms with versatile metabolisms. In that vein, we assessed PHA production by the photosynthetic purple non‐sulfur bacteria (PNSB) Rhodomicrobium vannielii and Rhodomicrobium udaipurense. We show that both species accumulate PHA across photo‐heterotrophic, photo‐hydrogenotrophic, photo‐ferrotrophic, and photo‐electrotrophic growth conditions, with either ammonium chloride (NH 4 Cl) or dinitrogen gas (N 2 ) as nitrogen sources. Our data indicate that nitrogen source plays a significant role in dictating PHA synthesis, with N 2 fixation promoting PHA production during photoheterotrophy and photoelectrotrophy but inhibiting production during photohydrogenotrophy and photoferrotrophy. We observed the highest PHA titres (up to 44.08 mg/L, or 43.61% cell dry weight) when cells were grown photoheterotrophically on sodium butyrate with N 2 , while production was at its lowest during photoelectrotrophy (as low as 0.04 mg/L, or 0.16% cell dry weight). We also find that photohydrogenotrophically grown cells supplemented with NH 4 Cl exhibit the highest electron yields – up to 58.89% – while photoheterotrophy demonstrated the lowest (0.27%–1.39%). Finally, we highlight superior electron conversion and PHA production compared to a related PNSB, Rhodopseudomonas palustris TIE‐1. This study illustrates the value of studying non‐model organisms like Rhodomicrobium for sustainable PHA production and indicates future directions for exploring PNSB metabolisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Estimation of Body Fat Percentage for Clinical Pharmacokinetic Studies in Children

Obesity is a prevalent childhood condition and the degree of adiposity appears likely to be an important covariate in the pharmacokinetics (PKs) of many drugs. We undertook these studies to facilitate the evaluation and, where appropriate, quantification of the covariate effect of body fat percentage (BF%) on PK parameters in children. We examined two large databases to determine the values and variabilities of BF% in children with healthy body weights and in those with obesity, comparing the accuracy and precision of BF% estimation by both clinical methods and demographically derived techniques. Additionally, we conducted simulation studies to evaluate the utility of the several methods for application in clinical trials. BF% was correlated with body mass index (BMI), but was highly variable among both children with healthy body weights and those with obesity. Bio‐impedance and several demographically derived techniques produced mean estimates of BF% that differed from dual x‐ray absorptiometry by < 1% (accuracy) and a SD of 5% or less (precision). Simulation studies confirmed that when the differences in precision among the several methods were small compared with unexplained between‐subject variability of a PK parameter, the techniques were of similar value in assessing the contribution of BF%, if any, as a covariate for that PK parameter. The combination of sex and obesity stage explained 68% of the variance of BF% with BMI. The estimation of BF% from sex and obesity stage can routinely be applied to PK clinical trials to evaluate the contribution of BF% as a potential covariate.

Green, Thomas P.↗
DOE OSTI2021

Toward sustainable production of value‐added bioenergy and industrial oils in oilseed and biomass feedstocks

Abstract Plant fatty acids are used for food, feed, fuel, and industrial materials. Structurally and chemically diverse fatty acids, referred to as unusual or specialized fatty acids, are found in the seed oils of diverse plant species. Many unusual fatty acids have potential use as alternative and renewable sources of biofuels and bio‐based industrial feedstocks due to their variant structures' physical or functional properties. Oils enriched in these fatty acids can increase the value of oilseed crops and provide co‐products that can be readily extracted from lignocellulosic materials in biomass crops. Here, we describe recent progress in strategies for enhancement of oil production and quality in oilseed crops and the extension of this knowledge for metabolic engineering of biomass crops. Successful implementation of these strategies, in part, through the use of emerging synthetic biology tools, will provide added product value for the economic sustainability of biomass crops as bioenergy feedstocks.

Park, Kiyoul↗
DOE OSTI2024

Economic and environmental bottlenecks in the industrial‐scale production of lipid‐derived biofuels from oleaginous yeasts: A review of the current trends and future prospects

Abstract Concerns about climate change and the reliance on fossil fuel reserves have motivated researchers to identify new renewable sources of energy. Biomass holds the potential to replace fossil‐derived products with biofuels and bio‐based chemicals. Plant‐derived lipids are promising sources of biofuels; however, the production of plant oil often leads to the release of massive amounts of carbon dioxide due to deforestation and land‐use change. The production of biofuels via plant oils (such as soybean) also competes with food production and in turn, impacts biodiversity. To mitigate these issues, the production of lipids from oleaginous yeasts could be an excellent alternative by incorporating these microbes into biorefineries utilizing agricultural or forest residues. Eventually, these microbial lipids could be potential sources for producing lipid‐derived biofuels. However, the current conventional methods for the production and recovery of lipids from oleaginous microbes suffer from economic and ecological challenges that affect its industrial‐scale expansion. This review highlights the major economic and environmental bottlenecks for the production of lipid‐derived biofuels from oleaginous yeasts. It also provides perspectives on the strategies that could be adapted on economic and ecological fronts to assist the expansion of the production of microbial lipid‐derived biofuels at an industrial scale.

Agriculture↗
DOE OSTI2023

A Systematic Investigation of the Effects of Standard-Sample Concentration Mismatch during Fe Isotope Measurement by MC-ICP-MS

Advances in multi-collector inductively coupled plasma-mass spectrometry (MC-ICP-MS) have led to the widespread use of iron (Fe) isotopes to elucidate the (bio)geochemical history of a range of environments. To generate Fe isotope ratio measurements, standard-sample bracketing (SSB) is commonly used to correct for instrumental mass bias inherent to MC-ICP-MS. However, SSB is only accurate when sample and isotope standard Fe concentrations match, in addition to the bulk solution matrix. When the Fe concentrations differ, Fe isotope ratio measurement results may be inaccurate, a phenomenon known as the "self-induced matrix effect." Here, this study systematically characterised the self-induced matrix effect for dry plasma Fe isotope ratio measurements on three MC-ICP-MS instruments and three introduction systems. Our extensive dataset indicates that: (1) the degree of mass bias is consistent regardless of MC-ICP-MS front-end design, (2) the degree of mass bias becomes less reproducible as the concentration difference between the sample and bracketing standard increases, and (3) this applies to both pure Fe solutions and solutions from geological materials. This study reinforces the requirement to match bracketing standard and sample concentrations within 10% and provides a correction method for that fall beyond the recommended concentration range to subsequently allow for proper concentration matching during SSB.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗