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DOE OSTI2020

Better management practices for environmentally sustainable production of microalgae and algal biofuels

The commercialization of biofuels produced from microalgae is in its infancy; therefore, many resource-management practices and production processes are still flexible. The purpose of this paper is to guide development of supply chains toward more environmentally sustainable practices. Furthermore, in this work, we review current and projected technologies and practices for autotrophic microalgae cultivation that promote environmental sustainability. We develop a framework that leverages these studies to propose better management practices (BMPs) for water quality and quantity, biodiversity, or greenhouse gas emissions in concert with productivity and profitability considerations. Some proposed BMPs are linked to numerical environmental targets, such as percent reductions in nutrient loadings to streams, whereas others seek to avoid thresholds leading to adverse health or ecological effects. Still others involve using the best available technologies, developed iteratively through life-cycle and techno-economic analyses. Proposed BMPs for microalgae cultivation focus on water quality and quantity, as well as improving greenhouse gas (GHG) emissions to obtain advanced biofuel designation. BMPs must allow producers to meet productivity and profitability targets, as well as environmental targets. These example BMPs characterize the state of science and engineering; thus, they will change over time.

09 BIOMASS FUELS↗

DOE OSTI2021

A data engineering approach for sustainable chemical end-of-life management

The presence of chemicals causing significant adverse human health and environmental effects during end-of-life (EoL) stages is a challenge for implementing sustainable management efforts and transitioning towards a safer circular life cycle. Conducting chemical risk evaluation and exposure assessment of potential EoL scenarios can help understand the chemical EoL management chain for its safer utilization in a circular life-cycle environment. However, the first step is to track the chemical flows, estimate releases, and potential exposure pathways. Hence, this work proposes an EoL data engineering approach to perform chemical flow analysis and screening to support risk evaluation and exposure assessment for designing a safer circular life cycle of chemicals. This work uses publicly-available data to identify potential post-recycling scenarios (e.g., industrial processing/use operations), estimate inter-industry chemical transfers, and exposure pathways to chemicals of interest. Furthermore, a case study demonstration shows how the data engineering framework identifies, estimates, and tracks chemical flow transfers from EoL stage facilities (e.g., recycling and recovery) to upstream chemical life cycle stage facilities (e.g., manufacturing). Also, the proposed framework considers current regulatory constraints on closing the recycling loop operations and provides a range of values for the flow allocated to post-recycling uses associated with occupational exposure and fugitive air releases from EoL operations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Systematic engineering for production of anti-aging sunscreen compound in Pseudomonas putida

Sunscreen has been used for thousands of years to protect skin from ultraviolet radiation. However, the use of modern commercial sunscreen containing oxybenzone, ZnO, and TiO 2 has raised concerns due to their negative effects on human health and the environment. In this study, we aim to establish an efficient microbial platform for production of shinorine, a UV light absorbing compound with anti-aging properties. First, we methodically selected an appropriate host for shinorine production by analyzing central carbon flux distribution data from prior studies alongside predictions from genome-scale metabolic models (GEMs). We enhanced shinorine productivity through CRISPRi-mediated downregulation and utilized shotgun proteomics to pinpoint potential competing pathways. Simultaneously, we improved the shinorine biosynthetic pathway by refining its design, optimizing promoter usage, and altering the strength of ribosome binding sites. Finally, we conducted amino acid feeding experiments under various conditions to identify the key limiting factors in shinorine production. The study combines meta-analysis of 13 C-metabolic flux analysis, GEMs, synthetic biology, CRISPRi-mediated gene downregulation, and omics analysis to improve shinorine production, demonstrating the potential of Pseudomonas putida KT2440 as platform for shinorine production.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Ionic-Based Electrochemical Gas Sensors for Low-Cost, High-Sensitivity SO2 Detection

Sulfur dioxide (SO2) is a toxic gas associated with adverse health and environmental effects that necessitate reliable monitoring techniques. Here, we report the development of an all-solid-state electrochemical sensor utilizing a lithium borate (Li3BO3) solid electrolyte capable of subppm of SO2 detection. While subppm of SO2 sensing has been previously demonstrated in other solid-state electrolyte systems─such as stabilized zirconia, natrium super ionic conductors (NASICON) under mixed-potential conditions─here we establish Li3BO3 as an alternative solid electrolyte enabling equilibrium potentiometric sensing in an all-solid architecture. This sensor demonstrates a detection limit of at least 0.25 ppm, surpassing the human-olfactory threshold and meeting the rigorous requirements for industrial and personal monitoring applications. The sensing mechanism relies on the formation of Li2SO4 on the electrode surface, as evidenced by multimodal characterization techniques, including Raman spectroscopy, scanning electron microscopy (SEM), and scanning transmission electron microscopy (STEM). The strong linear correlation between the open-circuit potential (OCV) and the logarithm of SO2 concentration between 0.25 and 2 ppm indicates that the response is Nernstian in nature.

Lagunas, Francisco (ORCID:000000026377683X)↗

DOE OSTI2023

Contrasting Capability of Single Atom Palladium for Thermocatalytic versus Electrocatalytic Nitrate Reduction Reaction

The occurrence of high concentrations of nitrate in various water resources is a significant environmental and human health threat, demanding effective removal technologies. Single atom alloys (SAAs) have emerged as a promising bimetallic material architecture in various thermocatalytic and electrocatalytic schemes including nitrate reduction reaction (NRR). This study suggests that there exists a stark contrast between thermocatalytic (T-NRR) and electrocatalytic (E-NRR) pathways that resulted in dramatic differences in SAA performances. Among Pd/Cu nanoalloys with varying Pd–Cu ratios from 1:100 to 100:1, Pd/Cu (1:100) SAA exhibited the greatest activity (TOF Pd = 2 min –1 ) and highest N 2 selectivity (94%) for E-NRR, while the same SAA performed poorly for T-NRR as compared to other nanoalloy counterparts. DFT calculations demonstrate that the improved performance and N 2 selectivity of Pd/Cu (1:100) in E-NRR compared to T-NRR originate from the higher stability of NO 3 * in electrocatalysis and a lower N 2 formation barrier than NH due to localized pH effects and the ability to extract protons from water. Here, this study establishes the performance and mechanistic differences of SAA and nanoalloys for T-NRR versus E-NRR.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Exploring Approaches for Accelerated Mass Spectrometric Detection of Fluorochemicals from Aqueous Solutions Using Indirect, Adsorbent-Facilitated Desorption Ionization

There is a growing need for rapid, low-cost analytical tools to screen water supplies for persistent contaminants. Due to their ubiquitous presence in the environment and their detrimental effects on human health, there is a public interest in widespread testing for perfluoroalkyl substances (PFAS). However, the most common methods for PFAS analysis can be time-intensive, requiring a preconcentration step followed by liquid chromatography–mass spectrometry (LC–MS), and are cost-prohibitive for most members of the public. To address these challenges, this study explores a novel method for rapid analysis of aqueous analytes by concentrating analytes on solid adsorbents and detecting them using mass spectrometric analysis via desorption ionization directly from the adsorbent surface. We demonstrate this general approach of indirect solution analysis (ISA) with desorption electrospray ionization (ISA-DESI) and laser desorption ionization (ISA-LDI). Various adsorbent materials were screened, including commercial mesoporous carbons, alumina, and metal–organic frameworks (MOFs). Using ISA-DESI, we consistently detect concentrations as low as 5–100 parts per trillion (ppt) for perfluorooctanoic acid (PFOA) and 10–100 ppt for perfluoroundecanoic acid (PFUnDA). This approach enables rapid detection in under 5 min, making it suitable for applications requiring trace detection with fast response times. We further demonstrate the use of ISA-DESI on a range of environmentally relevant contaminants. Advantages of this indirect solution analysis approach include simplified sample preparation, accelerated analysis, and low cost with readily available materials, making it a promising tool to accelerate mass spectrometric PFAS detection for identifying contaminated sites, performing laboratory studies, and monitoring industrial waste streams.

Bays, Nathan Robert [Sandia National Laboratories ↗

DOE OSTI2022

Ultra-high SERS detection of consumable coloring agents using plasmonic gold nanostars with high aspect-ratio spikes

Solution-based SERS detection by using a portable Raman instrument has emerged as an important tool due to its simplicity, and flexibility for rapid and on-site screening of analyte molecules. However, this method has several shortcomings, including poor sensitivity especially for weak-affinity analyte molecules, where there is no close contact between the plasmonic metal surface and analyte molecule. Examples of weak-affinity molecules include pigment molecules that are commonly used as a consumable coloring agent, such as allura red (AR), and sunset yellow (SY). As high consumption of colorant agents has been shown to cause adverse effects on human health, there is a strong need to develop a simple and practical sensing system with high sensitivity for these agents. Here we present a novel, highly sensitive solution-based SERS detection method for AR, and SY by using CTAC capped gold nanostars (GNS) having different aspect ratios (GNS-2, GNS-4, and GNS-5) without utilizing any aggregating agents which can enhance SERS signal however it reduces batch to batch reproducibility. The influence of the aspect ratio of GNS on SERS properties was investigated. We have achieved a limit of detection (LOD) of AR and SY as low as 0.5 and 1 ppb, respectively by using GNS-5 with the advantages of minimal sample preparation by just mixing the analyte solution into a well plate containing GNS solution. In addition, excellent colloidal stability and reproducibility have further enhanced the applicability in real-world samples. Altogether, our results evidence that the solution-based SERS detection platform using high aspect-ratio GNS can be applied for practical application to detect pigment molecules in real samples with satisfactory results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Designing a boron nitride polyethylene composite for shielding neutrons

Neutrons are encountered in many different fields, including condensed matter physics, space exploration, nuclear power, and healthcare. Neutrons interacting with a biological target produce secondary charged particles that are damaging to human health. The most effective way to shield neutrons is to slow them to thermal energies and then capture the thermalized neutrons. These factors lead us to consider potential materials solutions for neutron shields that maximize the protection of humans while minimizing the shield mass and adapt well to modern additive manufacturing techniques. Using hexagonal boron nitride (hBN) as a capture medium and high-density polyethylene (HDPE) as a thermalization medium, we aim to design the optimal internal structure of h 10 BN/HDPE composites by minimizing the effective dose, which is a measure of the estimated radiation damage exposure for a human. Through Monte Carlo simulations in Geant4, we find that the optimal structure reduces the effective dose up to a factor of 72 over aluminum (Al) and up to a factor of 4 over HDPE; this is a significant improvement in shielding effectiveness that could dramatically reduce the radiation exposure of occupational workers.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Tungsten enzymes play a role in detoxifying food and antimicrobial aldehydes in the human gut microbiome

Significance The diverse microorganisms contained within the human gut are known to have significant effects on human health. Herein, we show that genes encoding members of the tungsten oxidoreductase (WOR) family of enzymes and a tungstate-specific transporter are prevalent in the human gut microbiome and metagenome. We demonstrate that two model gut microbes assimilate tungsten into multiple WOR enzymes and that some of these enzymes catalyze the conversion of gut aldehydes to the corresponding acid, likely as a detoxification strategy to remove these reactive compounds.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Psychosomatic Bias in Low-dose Radiation Epidemiology: Assessing the Role of Radiophobia and Stress in Cancer Incidence

Abstract Historical assessment of radiation effects at low doses (below 0.2 Sv) are generally the result of back extrapolation from higher doses, which are known to have a linear relation between risk and dose. There are multiple counter-examples, and some literature argues that a threshold, nonlinear, or even a beneficial effect (hormeisis) can occur from radiation below these doses. The common theme found in all of these studies stems from the traditional approach of correlating disease rates to stimulus and then effectively curve-fitting the result toward zero dose. What has not been considered in general are the personal stress levels of the exposed individuals due to fear of cancer from low doses. The increased levels of cortisol due to the psychological stress from fear or depression has been shown in the literature to increase cancer probability. The extent to which low-dose exposed individuals were highly fearful or stressed from the radiation exposure would then give rise to elevated cancer based on stress rather than a fundamental radiogenic mechanism. If the population under epidemiological study is aware of a potential historical exposure (no matter how small) and has then lived under stress from fear or depression due to that exposure, the psychosomatic effects will bias the epidemiology accordingly and so should be quantified and accounted for as done with the effects of smoking. Health Phys. 129(0):000-000; 2025

Environmental Sciences & Ecology↗

DOE OSTI2023

Review of machine learning and deep learning models for toxicity prediction

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

Research & Experimental Medicine↗

DOE OSTI2022

Wood combustion nanoparticles emitted by conventional and advanced technology cordwood boilers, and their interactions in vitro with human lung epithelial monolayers

Biomass-burning boilers and stoves are widely used in many parts of the world, producing combustion emissions linked with health risks. Here, combustion emission nanoparticles (NPs) were collected from four representative wood burning boilers using oak cordwood at specific times in the burn cycle. The morphology and composition of the NPs was characterized using transmission electron microscopy and energy dispersive X-ray analysis. To determine the degree of NP cytotoxicity with human lung tissue, the combustion NPs were introduced to incubated lung bronchial epithelial monolayers (NCI-H292) in vitro at doses of 0.1×10 -6 and 3.0×10 -6 kg/L for 2 and 4 h. Histochemical analysis showed that cell death increased by a factor of 3.5 for both doses after 4 h when compared to the control. Ultrapure NPs prepared by wet chemical methods were also introduced to the epithelial lung cells for similar doses and exposure times and the cultures exhibited significantly reduced mortality. Electron microscopy was used to study the mechanism of cell mortality for the synthesized and combustion-based NPs by examining how the NP byproducts interacted with individual cell organelles. It was found that cell survival was strongly correlated with the absence of contaminants (salts, heavy metals, poly aromatic hydrocarbons) associated with the NPs entering the cells. Synthesized NPs consisting of pure carbon were relatively well tolerated and could be excreted without damaging the cell ultrastructure. Thus, careful removal of extraneous contaminants by controlling the burn cycle with a catalyst is essential to minimize the health and environmental effects of wood biofuel combustion. In better words, optimized advanced technology wood-burning boilers and stoves can provide a CO 2 -neutral energy source and significantly contribute to a future where fossil fuels have a reduced role.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Environmental Monitoring Report, Calendar year 2020. Knolls Laboratory and Kesselring Site

The results of the effluent and environmental monitoring programs at the Knolls Laboratory and Kesselring Site are summarized and assessed in this report. Tables 1 - 4 summarize the major elements of the environmental monitoring programs at each site. Information regarding the U.S. Department of Energy – Office of Environmental Management (DOE-EM) Separations Process Research Unit (SPRU) Disposition Project (SPRU DP), is also included in this report; however, the project was completed in 2020 and will no longer be reported in subsequent reports. DOE-EM will be continuing to perform other Decontamination & Decommissioning (D&D) work at Knolls Laboratory and Kesselring Site, which may require environmental monitoring, and will be included in this report, as warranted. Operations at the Knolls Laboratory, which includes SPRU and other DOE-EM activities, and the Kesselring Site continue to have no adverse effect on human health and the quality of the environment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Knolls Laboratory and Kesselring Site Environmental Monitoring Report (CY2019)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Astronauts Plasma-Derived Exosomes Induced Aberrant EZH2-Mediated H3K27me3 Epigenetic Regulation of the Vitamin D Receptor

There are unique stressors in the spaceflight environment. Exposure to such stressors may be associated with adverse effects on astronauts' health, including increased cancer and cardiovascular disease risks. Small extracellular vesicles (sEVs, i.e., exosomes) play a vital role in intercellular communication and regulate various biological processes contributing to their role in disease pathogenesis. To assess whether spaceflight alters sEVs transcriptome profile, sEVs were isolated from the blood plasma of 3 astronauts at two different time points: 10 days before launch (L-10) and 3 days after return (R+3) from the Shuttle mission. AC16 cells (human cardiomyocyte cell line) were treated with L-10 and R+3 astronauts-derived exosomes for 24 h. Total RNA was isolated and analyzed for gene expression profiling using Affymetrix microarrays. Enrichment analysis was performed using Enrichr. Furthermore, transcription factor (TF) enrichment analysis using the ENCODE/ChEA Consensus TF database identified gene sets related to the polycomb repressive complex 2 (PRC2) and Vitamin D receptor (VDR) in AC16 cells treated with R+3 compared to cells treated with L-10 astronauts-derived exosomes. Further analysis of the histone modifications using datasets from the Roadmap Epigenomics Project confirmed enrichment in gene sets related to the H3K27me3 repressive mark. Interestingly, analysis of previously published H3K27me3–chromatin immunoprecipitation sequencing (ChIP-Seq) ENCODE datasets showed enrichment of H3K27me3 in the VDR promoter. Collectively, our results suggest that astronaut-derived sEVs may epigenetically repress the expression of the VDR in human adult cardiomyocytes by promoting the activation of the PRC2 complex and H3K27me3 levels.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Process-based and observation-constrained SOA simulations in China: the role of semivolatile and intermediate-volatility organic compounds and OH levels

Organic aerosol (OA) is a major component of tropospheric submicron aerosol that contributes to air pollution and causes adverse effects on human health. Chemical transport models have difficulties in reproducing the variability in OA concentrations in polluted areas, hindering understanding of the OA budget and sources. Herein, we apply both process-based and observation-constrained schemes to simulate OA in GEOS-Chem. Comprehensive data sets of surface OA, OA components, secondary organic aerosol (SOA) precursors, and oxidants were used for model–observation comparisons. The base models generally underestimate the SOA concentrations in China. In the revised schemes, updates were made on the emissions, volatility distributions, and SOA yields of semivolatile and intermediate-volatility organic compounds (SVOCs and IVOCs) and additional nitrous acid sources. With all the model improvements, both the process-based and observation-constrained SOA schemes can reproduce the observed mass concentrations of SOA and show spatial and seasonal consistency with each other. Our best model simulations suggest that anthropogenic SVOCs and IVOCs are the dominant source of SOA, with a contribution of over 50% in most of China, which should be considered for pollution mitigation in the future. The residential sector may be the predominant source of SVOCs and IVOCs in winter, despite large uncertainty remaining in the emissions of IVOCs from the residential sector in northern China. The industry sector is also an important source of IVOCs, especially in summer. More SVOC and IVOC measurements are needed to constrain their emissions. Besides, the results highlight the sensitivity of SOA to hydroxyl radical (OH) levels in winter in polluted environments. The addition of nitrous acid sources can lead to over 30% greater SOA mass concentrations in winter in northern China. It is important to have good OH simulations in air quality models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

A new approach for measuring the carbon and oxygen content of atmospherically relevant compounds and mixtures

Abstract. Due to its complexity, gas- and particle-phase organic carbon in the atmosphere is often classified by its bulk physicochemical properties. However, there is a dearth of robust, moderate-cost approaches to measure the bulk chemical composition of organic carbon in the atmosphere. This is particularly true for the degree of oxygenation, which critically affects the properties and impacts of organic carbon but for which routine measurement approaches are lacking. This gap has limited the understanding of a wide range of atmospheric components, including particulate matter, the mass of which is monitored worldwide due to its health and environmental effects but the chemical characterization of which requires relatively high capital costs and complex operation by highly trained technical personnel. In this work, we demonstrate a new approach to estimate the mass of carbon and oxygen in analytes and mixtures that relies only on robust, moderate-cost detectors designed for use with gas chromatography. Organic compounds entering a flame ionization detector were found to be converted with approximately complete efficiency to CO2, which was analyzed downstream using an infrared detector to measure the mass of carbon analyzed. The ratio of the flame ionization detector (FID) signal generated to CO2 formed (FID∕CO2) was shown to be strongly correlated (R2=0.89) to the oxygen-to-carbon ratio (O∕C) of the analyte. Furthermore, simple mixtures of analytes behaved as the weighted average of their components, indicating that this correlation extends to mixtures. These properties were also observed to correlate well with the sensitivity of the FID estimated by structure activity relationships (quantified as the relative effective carbon number). The relationships between measured FID∕CO2, analyte O∕C, and FID sensitivity allow the estimation of one property from another with <15 % error for mixtures and <20 % error for most individual analytes. The approach opens the possibility of field-deployable, autonomous measurement of the carbon and oxygen content of particulate matter using time-tested, low-maintenance detectors, though such an application would require some additional testing on complex mixtures. With some instrumental modifications, similar measurements on gas-phase species may be feasible. Moreover, the potential expansion to additional gas chromatography detectors may provide concurrent measurement of other elements (e.g., sulfur, nitrogen).

42 ENGINEERING↗

DOE OSTI2024

Metabolic model-based ecological modeling for probiotic design

The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter ‘probiotic’ treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual’s microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.

59 BASIC BIOLOGICAL SCIENCES↗