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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Simulated moving bed-inspired method for continuous adsorptive denitrogenation of model fuel

Efficient, continuous routes for removing nitrogen-containing compounds from hydrothermal liquefaction-derived synthetic aviation fuel are needed to enable direct blending with conventional jet fuels. Here, we report a simulated moving bed-inspired process for adsorptive denitrogenation of a model fuel. Unlike conventional simulated moving bed systems, which are designed for sharp separations between similar solutes, this approach was run deliberately outside the classical separation region so that both pyridine and indole were removed together from the hydrocarbon stream. Alcohol solvents were used to regenerate the silica adsorbent, maintaining performance over extended operation and avoiding the downtime and energy demand associated with calcination. Under these conditions, the system demonstrates removal of more than 98% of nitrogen while cutting solvent use by 28% compared to batch operation. Classical modeling tools predicted column concentration profiles even in this nontraditional regime, suggesting a straightforward path to scaling. Together, these results motivate solvent-efficient, continuous denitrogenation strategies that could be integrated with biorefinery processes.

Adsorption↗
DOE OSTI2021

Experimental and computational analysis of bending fatigue failure in chopped carbon fiber chip reinforced composites

With a better balance among good mechanical performance, high freedom of design, and low material and manufacturing cost, chopped carbon fiber chip reinforced sheet molding compound (SMC) composites show great potential in different engineering applications. Here in this paper, bending fatigue behaviors of SMC composites considering the heterogeneous fiber orientation distributions have been thoroughly investigated utilizing both experimental and computational methods. First, four-point bending fatigue tests are performed with designed SMC composites, and the local modulus is adopted as a metric to represent the local fiber orientation of two opposing sides. Interestingly, SMC composites with and without large discrepancy in local modulus of opposing sides show different fatigue behaviors. Interrupted tests are conducted to explore the bending fatigue failure mechanism, and the damage processes of valid specimens are also closely examined. We find that the fatigue failure of SMC composites under four-point bending is governed by crack propagation instead of crack initiation. Because of this, the heterogeneous local fiber orientations of both sides of the specimen influence fatigue life. The microstructure of the lower side shows a direct influence while that of the upper side also exhibiting influence which becomes more prominent for high cycle fatigue cases. Furthermore, a hybrid micro–macro computational model is proposed to efficiently study the cyclic bending behavior of SMC composites. The region of interest is reconstructed with a modified random sequential absorption algorithm to conserve all the microstructural details including the heterogeneous fiber orientation, while the rest of the regions are modeled as homogenized macro-scale continua. Combined with a framework to capture the progressive fatigue damage under cyclic bending, the bending fatigue behaviors of SMC composites are accurately captured by the hybrid computational model comparing with our experimental analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Competition of magnetocrystalline anisotropy of uranium layers and zigzag chains in UNi 0.34 Ge 2 single crystals

Structural and thermodynamic properties of single-crystalline UNi 1-x Ge 2 with x = 0.66 have been investigated by measuring magnetization, specific heat, and thermal expansion over a wide range of temperatures and magnetic fields. The measurements revealed the emergence of a long-range antiferromagnetic ordering of uranium magnetic moments below the Néel temperature T N = 45.5 (1) K and the existence of two easy axes in the studied compound, namely b and c, which correspond to the planes of the uranium zig-zag chains. A magnetic field applied along these two crystallographic directions induces in the system a first-order metamagnetic phase transition (from antiferromagnetic to field-polarized paramagnetism), and the width of the magnetic hysteresis associated with that transition reaches as much as 40 kOe at the lowest temperatures. A magnetic phase diagram developed from the experimental data showed that the metastable region associated with that magnetic hysteresis forms a funnel that narrows toward the N´eel point in a zero magnetic field. The four-layer Ising model has successfully predicted the colinear antiferromagnetic structure in UNi 0.34 Ge 2 (known from earlier reports), its magnetic phase diagram, and temperature and field variations of its magnetization. Moreover, it suggests that the first-order phase transition extends down to zero magnetic field, although it is barely detectable in the experiments performed in low magnetic fields. According to this model, the second-order phase transition occurs in the compound only in zero field.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2018

NASA’s Mid-Atlantic Communities and Areas at Intensive Risk Demonstration: Translating Compounding Hazards to Societal Risk

Remote sensing provides a unique perspective on our dynamic planet, tracking changes and revealing the course of complex interactions. Long term monitoring and targeted observation combine with modeling and mapping to provide increased awareness of hydro-meteorological and geological hazards. Disasters often follow hazards and the goal of NASA’s Disasters Program is to look at the earth as a highly coupled system to reduce risk and enable resilience. Remote sensing and geospatial science are used as tools to help answer critical questions that inform decisions. Data is not the same as information, nor does understanding of processes necessarily translate into decision support for disaster preparedness, response and recovery. Accordingly, NASA is engaging the scientific and decision-support communities to apply remote sensing, modeling, and related applications in Communities and Areas at Intensive Risk (CAIR). In 2017, NASA’s Applied Sciences Disasters Program hosted a regional workshop to explore these issues with particular focus on coastal Virginia and North Carolina. The workshop brought together partners in academia, emergency management, and scientists from NASA and partnering federal agencies to explore capabilities among the team that could improve understanding of the physical processes related to these hazards, their potential impact to changing communities, and to identify methodologies for supporting emergency response and risk mitigation. The resulting initiative, the mid-Atlantic CAIR project, demonstrates the ability to integrate satellite derived earth observations and physical models into actionable, trusted knowledge. Severe storms and associated storm surge, sea level rise, and land subsidence coupled with increasing populations and densely populated, aging critical infrastructure often leave coastal regions and their communities extremely vulnerable. The integration of observations and models allow for a comprehensive understanding of the compounding risk experienced in coastal regions and enables individuals in all positions make risk-informed decisions. This initiative uses a representative storm surge case as a baseline to produce flood inundation maps. These maps predict building level impacts at current day and for sea level rise (SLR) and subsidence scenarios of the future in order to inform critical decisions at both the tactical and strategic levels. To accomplish this analysis, the mid-Atlantic CAIR project brings together Federal research activities with academia to examine coastal hazards in multiple ways: 1) reanalysis of impacts from 2011 Hurricane Irene, using numerical weather modeling in combination with coastal surge and hydrodynamic, urban inundation modeling to evaluate combined impact scenarios considering SLR and subsidence, 2) remote sensing of flood extent from available optical imagery, 3) adding value to remotely sensed flood maps through depth predictions, and 4) examining coastal subsidence as measured through time-series analysis of synthetic aperture radar observations. Efforts and results are published via ArcGIS story maps to communicate neighborhoods and infrastructure most vulnerable to changing conditions. Story map features enable time-aware flood mapping using hydrodynamic models, photographic comparison of flooding following Hurricane Irene, as well as visualization of heightened risk in the future due to SLR and land subsidence.

Rogers, Laura↗
DOE OSTI2021

Modeled Emission of Hydroxyl and Ozone Reactivity from Evaporation of Fragrance Mixtures

Volatile chemical products (VCPs) account for increasing fractions of organic carbon emitted to the atmosphere, particularly in urban areas. Fragrances are potentially reactive components that are added to many VCPs. To better constrain these emissions, 11 commercially available liquid fragrance mixtures were characterized for their composition and their evaporation modeled. Here, emissions of mass, hydroxyl reactivity, and ozone reactivity were estimated by modeling under four different scenarios. Fragrance compounds were generally less than one-half the mass of fragrance mixtures, with the balance comprised of solvents and plasticizers and unresolved mass thought to be dominated by plasticizers. The results showed that terpenes and terpenoids account for nearly all of the emitted mass and reactivity while only comprising ~10% w/w on average of the liquid fragrance mixtures. Most of the reactivity is emitted within hours, with ozone reactivity evolving more rapidly than OH reactivity and comprised almost entirely of terpenes. Limonene, a common fragrance constituent, dominates the reactivity of emitted carbon. Generally, 20–40% of the potential hydroxyl reactivity contained in the fragrance mixture does not evaporate on time scales sufficient to have an impact on local or regional air quality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Distinguishing Models for Mixed Halide Lead Perovskite Photosegregation via Terminal Halide Stoichiometry

Photoinduced halide segregation in mixed halide hybrid perovskites [i.e., APb(I 1–x Br x ) 3 ] represents an intrinsic instability that impedes their commercialization. Resolving this issue requires developing a microscopic understanding of the phenomenon. Key to this is distinguishing existing models of halide photosegregation by comparing their corresponding predictions to experiment. In this work, we test the temperature dependency of predicted perovskite terminal stoichiometries (x terminal ), following photosegregation, in single and double cation mixed halide perovskites. Our results show a general temperature invariance of x terminal . This largely supports the idea that band gap difference between parent and halide-segregated perovskite phases drives photosegregation. Beyond this, careful examination of temperature- and halide composition-dependent emission energies suggests an alternative explanation for the enhanced photostability of mixed cation/mixed halide perovskites, linked to band gap energy differences between parent and phase-segregated perovskite phases. Together, our results make inroads in clarifying the microscopic origin of mixed halide perovskite photosegregation and pave the way toward approaches that stabilize their use in applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning to accelerate screening for Marcus reorganization energies

Understanding and predicting the charge transport properties of π-conjugated materials is an important challenge for designing new organic electronic devices, such as solar cells, plastic transistors, light-emitting devices, and chemical sensors. A key component of the hopping mechanism of charge transfer in these materials is the Marcus reorganization energy which serves as an activation barrier to hole or electron transfer. While modern density functional methods have proven to accurately predict trends in intramolecular reorganization energy, such calculations are computationally expensive. In this work, we outline active machine learning methods to predict computed intramolecular reorganization energies of a wide range of polythiophenes and their use toward screening new compounds with low internal reorganization energies. Our models have an overall root mean square error (RMSE) of ±0.113 eV, but a much smaller RMSE of only ±0.036 eV on the new screening set. Since the larger error derives from high-reorganization energy compounds, the new method is highly effective to screen for compounds with potentially efficient charge transport parameters.

14 SOLAR ENERGY↗
DOE OSTI2023

Impacts of climate change on subannual hydropower generation: a multi-model assessment of the United States federal hydropower plant

Abstract Hydropower is a low-carbon emission renewable energy source that provides competitive and flexible electricity generation and is essential to the evolving power grid in the context of decarbonization. Assessing hydropower availability in a changing climate is technically challenging because there is a lack of consensus in the modeling representation of key dynamics across scales and processes. Focusing on 132 US federal hydropower plants, in this study we evaluate the compounded impact of climate and reservoir-hydropower models’ structural uncertainties on monthly hydropower projections. In particular, instead of relying on one single regression-based hydropower model, we introduce another conceptual reservoir operations-hydropower model in the assessment framework. This multi-model assessment approach allows us to partition uncertainties associated with both climate and hydropower models for better clarity. Results suggest that while at least 70% of the uncertainties at the annual scale and 50% at the seasonal scale can be attributed to the choice of climate models, up to 50% of seasonal variability can be attributed to the choice of hydropower models, particularly in regions over the western US where the reservoir storage is substantial. The analysis identifies regions where multi-model assessments are needed and presents a novel approach to partition uncertainties in hydropower projections. Another outcome includes an updated evaluation of Coupled Model Intercomparison Project Phase 5 (CMIP5)-based federal hydropower projection, at the monthly scale and with a larger ensemble, which can provide a baseline for understanding future assessments based on CMIP6 and beyond.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Advancing energy storage through solubility prediction: leveraging the potential of deep learning

Solubility prediction plays a crucial role in energy storage applications, such as redox flow batteries, because it directly affects the efficiency and reliability. Researchers have developed various methods that utilize quantum calculations and descriptors to predict the aqueous solubilities of organic molecules. Notably, machine learning models based on descriptors have shown promise for solubility prediction. As deep learning tools, graph neural networks (GNNs) have emerged to capture complex structure–property relationships for material property prediction. Specifically, MolGAT, a type of GNN model, was designed to incorporate n-dimensional edge attributes, enabling the modeling of intricacies in molecular graphs and enhancing the prediction capabilities. In a previous study, MolGAT successfully screened 23 467 promising redox-active molecules from a database of over 500 000 compounds, based on redox potential predictions. This study focused on applying the MolGAT model to predict the aqueous solubility (log S) of a broad range of organic compounds, including those previously screened for redox activity. The model was trained on a diverse sample of 8494 organic molecules from AqSolDB and benchmarked against literature data, demonstrating superior accuracy compared with other state of the art graph-based and descriptor-based models. Subsequently, the trained MolGAT model was employed to screen redox-active organic compounds identified in the first phase of high-throughput virtual screening, targeting favorable solubility in energy storage applications. The second round of screening, which considered solubility, yielded 12 332 promising redox-active and soluble organic molecules suitable for use in aqueous redox flow batteries. Thus, the two-phase high-throughput virtual screening approach utilizing MolGAT, specifically trained for redox potential and solubility, is an effective strategy for selecting suitable intrinsically soluble redox-active molecules from extensive databases, potentially advancing energy storage through reliable material development. This indicates that the model is reliable for predicting the solubility of various molecules and provides valuable insights for energy storage, pharmaceutical, environmental, and chemical applications.

25 ENERGY STORAGE↗
NASA NTRS2012

Frequency Shift During Mass Properties Testing Using Compound Pendulum Method

During mass properties testing on the X-48B Blended Wing Body aircraft (The Boeing Company, Chicago, Illinois) at the National Aeronautics and Space Administration Dryden Flight Research Center, Edwards, California, large inertia measurement errors were observed in results from compound pendulum swings when compared to analytical models. By comparing periods of oscillations as measured from an average over the test period versus the period of each oscillation, it was noticed that the frequency of oscillation was shifting significantly throughout the test. This phenomenon was only noticed during compound pendulum swings, and not during bifilar pendulum swings. The frequency shift was only visible upon extensive data analysis of the frequency for each oscillation, and did not appear in averaged frequency data over the test period. Multiple test articles, test techniques, and hardware setups were used in attempts to eliminate or identify the cause of the frequency shift. Plotting the frequency of oscillation revealed a region of minimal shift that corresponded to a larger amplitude range. This region of minimal shift provided the most accurate results compared to a known test article; however, the amplitudes that produce accurate inertia measurements are amplitudes larger than those generally accepted in mass properties testing. This paper examines two case studies of the frequency shift, using mass properties testing performed on a dummy test article, and on the X-48B Blended Wing Body aircraft.

Wolfe, David↗
DOE OSTI2022

In‐Silico Device Performance Prediction of Cosensitizer Dye Pairs for Dye‐Sensitized Solar Cells

Abstract Endeavors in the field of dye‐sensitized solar cells (DSCs) have shown great promise when adopting a data‐driven approach to materials discovery, such as successful molecular‐scale predictions of light‐harvesting chromophores. However, predictions of DSC dyes would become much more sophisticated if a molecular‐to‐macroscopic DSC device prediction methodology existed. Thereby, a fully computational pipeline is presented that predicts device‐performance parameters of DSCs which contain varying dye combinations. Optimal pairing of complementary dyes is identified via a data‐driven workflow that affords cosensitized DSCs with maximum power‐conversion efficiencies. Six high‐performing DSC dyes are paired with partner dyes that are screened from a database of 8488 compounds using sequential heuristic filters. Existing models that predict short‐circuit‐current density ( J SC ) and open‐circuit voltage ( V OC ) parameters are adapted to predict singly sensitized and cosensitized DSC performance. The predictions for J sc values of singly sensitized devices match experimental literature values with comparable accuracy to more computationally costly methods. Five out of six dye pairings are predicted to have greater J SC values when cosensitized compared to their corresponding singly sensitized devices, including two pairs that show strong J sc boosts of +13% and +12% when cosensitized. Thus, the prospect of an entirely in‐silico prediction pipeline for DSC performance that can be used to realize the fully automated design of optimized cosensitized DSCs is demonstrated.

14 SOLAR ENERGY↗
DOE OSTI2021

Pyrolysis of bio-derived dioxolane fuels: A ReaxFF molecular dynamics study

Alkyl-substituted 1,3-dioxolanes, including 4,5-dimethyl-2-pentan-3-yl-1,3-dioxolane (Fuel 1), 4,5-dimethyl-2-pentyl-1,3-dioxolane (Fuel 2), and 2-(heptan-3-yl)-4,5-dimethyl-1,3-dioxolane (Fuel 3), have been recently suggested as potential biodiesels. In this paper, we investigate the initial pyrolysis of the alkyl-substituted 1,3-dioxolanes at high temperatures using ReaxFF molecular dynamics (MD) simulations. We analyze the decomposition rate, reaction mechanism, and product distribution in the pyrolysis of the three alkyl 1,3-dioxlanes. The three fuels primarily decompose to 4,5-dimethyl-1,3-dioxolane radical and hydrocarbons derived from the alkyl side-chains. The further decomposition of 4,5-dimethyl-1,3-dioxolane radical primarily leads to 2-C 4 H 8 and CO 2 within a few decomposition steps. The hydrocarbon product distribution is significantly affected by the molecular structure of the alkyl side-chain, which would have a strong influence on the sooting tendency of these fuels. The ReaxFF simulations predict that the order of sooting tendency would be Fuel 3 > Fuel 1 > Fuel 2, which agrees with the measured sooting tendency trend. Based on the pyrolysis mechanism identified by ReaxFF, we propose a new alkyl dioxolane, 4-hexyl-5-methyl-1,3-dioxolane (Fuel 4), which might produce even less soot, by modifying the molecular structure of Fuel 2. Our ReaxFF simulation shows that Fuel 4 produce much less C 4 H 8 , an effective non-aromatic soot precursor, than Fuel 2. Moreover, more carbon atoms are bonded to each oxygen atom in Fuel 4 than Fuel 2, which would help reduce soot yield by removing more carbon atoms from the soot-producing pool of species. The major decomposition pathways identified in this work can be used to develop chemical kinetic models for 1,3-dioxolane based compounds, as biodiesel components, applicable to combustion engine simulations. We also demonstrate that the chemical kinetic insight offered by ReaxFF simulations can be used to design new fuel molecules with more desired properties.

09 BIOMASS FUELS↗
DOE OSTI2023

Polychlorinated biphenyls, polychlorinated dibenzo- p -dioxins, polychlorinated dibenzofurans, pesticides, and diabetes in the Anniston Community Health Survey follow-up (ACHS II): single exposure and mixture analysis approaches

Dioxins and dioxin-like compounds measurements were added to polychlorinated biphenyls (PCBs) and organochlorine pesticides to expand the exposure profile in a follow-up to the Anniston Community Health Survey (ACHS II, 2014) and to study diabetes associations. Participants of ACHS I (2005–2007) still living within the study area were eligible to participate in ACHS II. Diabetes status (type-2) was determined by a doctor's diagnosis, fasting glucose ≥125 mg/dL, or being on any glycemic control medication. Incident diabetes cases were identified in ACHS II among those who did not have diabetes in ACHS I, using the same criteria. Thirty-five ortho-substituted PCBs, 6 pesticides, 7 polychlorinated dibenzo-p-dioxins (PCDD), 10 furans (PCDF), and 3 non-ortho PCBs were measured in 338 ACHS II participants. Dioxin toxic equivalents (TEQs) were calculated for all dioxin-like compounds. Main analyses used logistic regression models to calculate odds ratios (OR) and 95 % confidence intervals (CI). In models adjusted for age, race, sex, BMI, total lipids, family history of diabetes, and taking lipid lowering medication, the highest ORs for diabetes were observed for PCDD TEQ: 3.61 (95 % CI: 1.04, 12.46), dichloro-diphenyl dichloroethylene (p,p’-DDE): 2.07 (95 % CI 1.08, 3.97), and trans-Nonachlor: 2.55 (95 % CI 0.93, 7.02). The OR for sum 35 PCBs was 1.22 (95 % CI: 0.58–2.57). To complement the main analyses, we used BKMR and g-computation models to evaluate 12 mixture components including 4 TEQs, 2 PCB subsets and 6 pesticides; suggestive positive associations for the joint effect of the mixture analyses resulted in ORs of 1.40 (95% CI: -1.13, 3.93) for BKMR and 1.32 (95% CI: -1.12, 3.76) for g-computation. Furthermore, the mixture analyses provide further support to previously observed associations of trans-Nonachlor, p,p’- DDE, PCDD TEQ and some PCB groups with diabetes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Unexpected Hydride: Ce 4 B 2 C 2 H 2.42 , a Stuffed Variant of the Nd 2 BC Structure Type

Ce 4 B 2 C 2 H 2.42 was grown as large crystals from a cerium/copper eutectic flux. The structure was characterized by single-crystal X-ray and neutron diffraction and was found to be a stuffed variant of Nd 2 BC with the addition of two interstitial hydrogen positions. The tetrahedral hydrogen position is fully occupied, while the octahedral position has an occupancy of 42(3)%. Initial synthesis was due to hydrogen contamination of the cerium metal but has been successfully repeated using anthracene as a carbon and hydrogen source. Density of states calculations suggest that the incorporation of hydrogen stabilizes the compound with respect to the nonhydrided model. Magnetic susceptibility data show a complex magnetic ordering at 7.7 K that originates from the localized electron on the Ce 3+ in the structure. The trivalent state is also supported by X-ray photoelectron spectroscopy measurements. Heat capacity and electrical resistivity data show that the phase transition is broad in temperature, which may be due to structural disorder. Furthermore, the large low temperature value of C/T also indicates possible heavy fermion behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Midlatitude Ozone Depletion and Air Quality Impacts from Industrial Halogen Emissions in the Great Salt Lake Basin

We report aircraft observations of extreme levels of HCl and the dihalogens Cl 2 , Br 2 , and BrCl in an industrial plume near the Great Salt Lake, Utah. Complete depletion of O 3 was observed concurrently with halogen enhancements as a direct result of photochemically produced halogen radicals. Observed fluxes for Cl 2 , HCl, and NO x agreed with facility-reported emissions inventories. Bromine emissions are not required to be reported in the inventory, but are estimated as 173 Mg year –1 Br 2 and 949 Mg year –1 BrCl, representing a major uncounted oxidant source. A zero-dimensional photochemical box model reproduced the observed O 3 depletions and demonstrated that bromine radical cycling was principally responsible for the rapid O 3 depletion. Inclusion of observed halogen emissions in both the box model and a 3D chemical model showed significant increases in oxidants and particulate matter (PM 2.5 ) in the populated regions of the Great Salt Lake Basin, where winter PM 2.5 is among the most severe air quality issues in the U.S. The model shows regional PM 2.5 increases of 10%–25% attributable to this single industrial halogen source, demonstrating the impact of underreported industrial bromine emissions on oxidation sources and air quality within a major urban area of the western U.S.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Quantum-Chemically Informed Machine Learning: Prediction of Energies of Organic Molecules with 10 to 14 Non-hydrogen Atoms

High-fidelity quantum-chemical calculations can provide accurate predictions of molecular energies, but their high computational costs limit their utility, especially for larger molecules. We have shown in previous work that machine learning models trained on high-level quantum-chemical calculations (G4MP2) for organic molecules with one to nine non-hydrogen atoms can provide accurate predictions for other molecules of comparable size at much lower costs. Here we demonstrate that such models can also be used to effectively predict energies of molecules larger than those in the training set. To implement this strategy, we first established a set of 191 molecules with 10–14 non-hydrogen atoms having reliable experimental enthalpies of formation. We then assessed the accuracy of computed G4MP2 enthalpies of formation for these 191 molecules. The error in the G4MP2 results was somewhat larger than that for smaller molecules, and the reason for this increase is discussed. Two density functional methods, B3LYP and ωB97X-D, were also used on this set of molecules, with ωB97X-D found to perform better than B3LYP at predicting energies. The G4MP2 energies for the 191 molecules were then predicted using these two functionals with two machine learning methods, the FCHL-Δ and SchNet-Δ models, with the learning done on calculated energies of the one to nine non-hydrogen atom molecules. The better-performing model, FCHL-Δ, gave atomization energies of the 191 organic molecules with 10–14 non-hydrogen atoms within 0.4 kcal/mol of their G4MP2 energies. Thus, this work demonstrates that quantum-chemically informed machine learning can be used to successfully predict the energies of large organic molecules whose size is beyond that in the training set.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Enhanced Oxygen Activation Achieved by Robust Single Chromium Atom-Derived Catalysts in Aerobic Oxidative Desulfurization

Oxidative desulfurization (ODS) plays critical roles in the production of high-quality sulfur impurity-free fuels, especially procedures using molecular oxygen as the sole oxidant. However, the state-of-the-art systems still rely on noble metal nanocatalysts, with deactivation issues caused by coking and sintering still present. In this work, leveraging the merits of single-atom catalysts (SACs) with earth-abundant metal cores and robust nanoporous supports, a series of catalysts composed of homogeneously distributed single chromium atoms anchored on multiwalled carbon nanotubes were fabricated and deployed to catalyze the aerobic ODS transformation. Adopting aromatic sulfur compound (ASC)-containing alkanes as a model system with molecular oxygen as the oxidant, efficient oxygen-to-active O 2 •– transformation and subsequent ASCs-to-sulfone conversion have been achieved, with the former benefiting from the chromium-active sites and the latter arising from the robust, nanoporous, and π-conjugated architecture of the supports. The attractive catalytic performance and cycling stability of the chromium-based SACs make them promising candidates in practical sulfur species removal from liquid fuels, supplying an alternative guidance on the catalyst design toward cost-effective and energy-efficient ODS procedures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Evaluation of Subetadex-α-methyl, a Polyanionic Cyclodextrin Scaffold, as a Medical Countermeasure against Fentanyl and Related Opioids

Subetadex-α-methyl (SBX-Me), a modified, polyanionic cyclodextrin scaffold, has been evaluated for its utilization as a medical countermeasure (MCM) to neutralize the effects of fentanyl and related opioids. Initial in vitro toxicity assays demonstrate that SBX-Me has a nontoxic profile, comparable to the FDA-approved cyclodextrin-based drug Sugammadex. Pharmacokinetic analysis showed rapid clearance of SBX-Me with an elimination half-life of ~7.4 h and little accumulation in major organs. SBX-Me was also evaluated for its ability to counteract the effects of fentanyl, carfentanil, and remifentanil in rats. Recovery times in rats exposed to sublethal fentanyl doses were found to be shorter when treated with SBX-Me after opioid exposure. The recovery times were reduced from ~35 to ~17 min for fentanyl, ~172 to ~59 min for carfentanil, and ~18 to ~12 min for remifentanil. SBX-Me increased the elimination half-life for fentanyl and remifentanil from 5.37 to 6.42 h and 8.24 to 9.74 h, respectively. These data support SBX-Me as a solid platform from which further research can be launched for the development of a MCM against the effects of fentanyl and its analogs. Furthermore, the data suggests that SBX-Me and other analogs are attractive candidates as broad spectrum opioids targeting MCMs.

Chemistry↗