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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

High-resolution CARMA Observation of Molecular Gas in the North America and Pelican Nebulae

In this paper, we present the first results from a CARMA high-resolution {sup 12}CO(1-0), {sup 13}CO(1-0), and C{sup 18}O(1-0) molecular line survey of the North America and Pelican (NAP) Nebulae. CARMA observations have been combined with single-dish data from the Purple Mountain 13.7 m telescope, to add short spacings and to produce high-dynamic-range images. We find that the molecular gas is predominantly shaped by the W80 H ii bubble, driven by an O star. Several bright rims noted in the observation are probably remnant molecular clouds, heated and stripped by the massive star. Matching these rims in molecular lines and optical images, we construct a model of the three-dimensional structure of the NAP complex. Two groups of molecular clumps/filaments are on the near side of the bubble: one is being pushed toward us, whereas the other is moving toward the bubble. Another group is on the far side of the bubble, and moving away. The young stellar objects in the Gulf region reside in three different clusters, each hosted by a cloud from one of the three molecular clump groups. Although all gas content in the NAP is impacted by feedback from the central O star, some regions show no signs of star formation, while other areas clearly exhibit star formation activity. Additional molecular gas being carved by feedback includes cometary structures in the Pelican Head region, and the boomerang features at the boundary of the Gulf region. The results show that the NAP complex is an ideal place for the study of feedback effects on star formation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Application of GOTHIC to Groundwater Transport Analysis - 20152

Migration of pollutants and hazardous wastes, potentially containing radioactive isotopes, via groundwater transport is a concern at most waste cleanup sites. Predictive analysis can be used to evaluate mitigating actions intended to minimize impact on the environment and public exposure. GOTHIC is a multipurpose thermal hydraulics code that is used extensively in the nuclear industry for design, licensing and operation evaluations. It combines the capabilities of typical one-dimensional system codes and the essential features of Computational Fluid Dynamics (CFD) codes for three-dimensional analysis. There are other codes that are specifically developed for groundwater transport analysis and the results presented here are consistent with prior analyses. However, GOTHIC has some unique features that offer advantages for applications related to nuclear waste. Most importantly, it has been developed and maintained under a Quality Assurance program in compliance with the requirements of 10CFR50 Appendix B [2] and applicable portions of ASME NQA-1 [3] since 1995. Available GOTHIC capabilities that make the code especially useful for groundwater transport of nuclear materials include: - Tracking of any number of tracer elements for contaminants and other species of interest; - Radioactive decay and progeny of tracer elements; - Adsorption/desorption of tracer elements; - Tracking of any number of dissolved gases; - Release and absorption for dissolved gases; - Vapor phase tracking; - Non-Newtonian fluid modeling. The general porous body modeling approach makes GOTHIC well suited to groundwater transport analysis. The multi-region modeling approach used by GOTHIC simplifies model construction for regions of varying hydrologic characteristics and focuses the computational effort on regions of particular interest while simultaneously capturing the macroscopic response and any feedback effects across the larger domain. The applicability of GOTHIC to groundwater transport applications is demonstrated by comparing code results with available analytic or semi-analytic solutions for groundwater behavior. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Failure Analysis–Informed Risk Assessment Framework for Geological Carbon Storage Using Numerical Simulation and Machine Learning

Geological carbon storage (GCS) is recognized as a critical technology for achieving large-scale reductions in anthropogenic carbon dioxide (CO 2 ) emissions. Ensuring long-term containment and safety requires robust risk assessment frameworks that account for geological uncertainty and identify potential failure scenarios. Among various indicators, the area of review (AoR) serves as a key metric for evaluating storage performance, regulatory compliance, and monitoring design, as it delineates the spatial extent impacted by pressure buildup and plume migration. However, conventional AoR-based risk assessments typically perturb parameters within narrow uncertainty bounds, potentially overlooking rare but high-impact events arising from extreme geological conditions. In this study, we present a failure analysis–informed risk assessment framework for large-scale GCS projects to improve site prescreening and monitoring design. A suite of 300 numerical simulations was generated using stochastic geological models that vary five key parameters: net-to-gross ratio, anisotropy azimuth, porosity multiplier, permeability multiplier, and vertical-to-horizontal permeability ratio. Among these, 200 realizations represent normal geological uncertainty, while 100 additional cases explore extreme yet plausible conditions for failure-case analysis. The AoR was simulated and computed from pressure and CO 2 saturation fields, where the baseline AoR boundary, representing the extent predicted under typical geological uncertainty, was defined as the union of 200 normal-range simulations, and failure was identified when extreme-range cases exceeded this baseline. Results show that incorporating broader parameter uncertainty produces significantly larger AoR extents, underscoring the potential underestimation of risk under conventional uncertainty ranges. Furthermore, spatial probability maps derived from failure-induced AoR exceedance identify regions requiring enhanced monitoring attention. Various machine learning (ML)–based classifiers were developed to predict failure occurrence from geological parameters, with the random forest model achieving the highest performance (F1-score of 0.986). Consistent findings from correlation coefficient, feature importance, and Sobol sensitivity analyses reveal that low net-to-gross ratios and permeability multipliers are the dominant risk drivers, reflecting reduced reservoir connectivity and limited pressure dissipation. Altogether, these results provide a novel framework for risk-informed site prescreening and monitoring design that explicitly considers rare but high-impact geological scenarios in GCS projects.

25 ENERGY STORAGE↗
DOE OSTI2024

Unified understanding of the impact of semiflexibility, concentration, and molecular weight on macromolecular-scale ring diffusion

Conformationally fluctuating, globally compact macromolecules such as polymeric rings, single-chain nanoparticles, microgels, and many-arm stars display complex dynamic behaviors due to their rich topological structure and intermolecular organization. Synthetic rings are hybrid objects with conformations that display both ideal random walk and compact globular features, which can serve as models of genomic DNA. To date, emphasis has been placed on the effect of ring molecular weight on their unusual behaviors. Here, we combine simulations and a microscopic force-level theory to build a unified understanding for how key aspects of ring dynamics depend on different tunable molecular properties including backbone rigidity, monomer concentration, degree of traditional entanglement, and molecular weight. Our large-scale molecular dynamics simulations of ring melts with very different backbone stiffnesses reveal unanticipated behaviors which agree well with our generalized theory. This includes a universal master curve for center-of-mass diffusion constants as a function of molecular weight scaled by a chemistry and thermodynamic state-dependent critical molecular weight that generalizes the concept of an entanglement cross-over for linear chains. The key physics is how backbone rigidity and monomer concentration induced changes of the entanglement length, interring packing, degree of interpenetration, and liquid compressibility slow down space-time dynamic-force correlations on macromolecular scales. A power law decay of the center-of-mass diffusion constant with inverse molecular weight squared is the first consequence, followed by an ultraslow activated hopping transport regime. Our results set the stage to address slow dynamics and kinetic arrest in different families of compact synthetic and biological polymeric systems.

Science & Technology - Other Topics↗
DOE OSTI2023

Impact of Varying the Photoanode/Catalyst Interfacial Composition on Solar Water Oxidation: The Case of BiVO 4 (010)/FeOOH Photoanodes

Photoanodes used in a water-splitting photoelectrochemical cell are almost always paired with an oxygen evolution catalyst (OEC) to efficiently utilize photon-generated holes for water oxidation because the surfaces of photoanodes are typically not catalytic for the water oxidation reaction. Suppressing electron–hole recombination at the photoanode/OEC interface is critical for the OEC to maximally utilize the holes reaching the interface for water oxidation. Here, in order to explicitly demonstrate and investigate how the detailed features of the photoanode/OEC interface affect interfacial charge transfer and photocurrent generation for water oxidation, we prepared two BiVO 4 (010)/FeOOH photoanodes with different Bi:V ratios at the outermost layer of the BiVO 4 interface (close to stoichiometric vs Bi-rich) while keeping all other factors in the bulk BiVO 4 and FeOOH layers identical. The resulting two photoanodes show striking differences in the photocurrent onset potential and photocurrent density for water oxidation. The ambient pressure X-ray photoelectron spectroscopy results show that these two BiVO 4 (010)/FeOOH photoanodes show drastically different Fe 2+ :Fe 3+ ratios in FeOOH both in the dark and under illumination with water, demonstrating the immense impact of the interfacial composition and structure on interfacial charge transfer. Using computational studies, we reveal the effect of the surface Bi:V ratio on the hydration of the BiVO 4 surface and bonding with the FeOOH layer, which in turn affect the band alignments between BiVO 4 and FeOOH. These results explain the atomic origin of the experimentally observed differences in electron and hole transfer and solar water oxidation performance of the two photoanodes having different interfacial compositions.

14 SOLAR ENERGY↗
DOE OSTI2024

Impact of penetrating collisions of plasma ions on spectral line shapes

Spectral-line-broadening models have been moving towards including full Coulomb interactions between the atom and plasma, replacing the commonly used dipole approximation. The effects of the full Coulomb interaction have been thoroughly explored for plasma electrons, resulting in redshifts of spectral lines in high-energy-density plasmas. We explore the impact of a full Coulomb treatment on ion broadening. Penetrating collisions due to ions do not significantly impact the linewidths. The most significant aspect is the appearance of quasimolecular resonances in the far line wings, such as those previously observed in white-dwarf spectra. We identify several problems with the existing models. The most direct implementation causes the quasimolecular features to appear at the wrong photon energies. It is possible to include the detailed molecular structure, but this currently requires the Anderson-Talman approximation, which ignores the N-body properties of the plasma. We find that N-body effects can substantially broaden these quasimolecular features and even shift them, depending on the plasma conditions. We conclude that penetration of ions into the spatial extent of the radiator wave function does not strongly affect the usual diagnostics and may have a moderate to weak effect on opacity. Furthermore, the Rosseland mean opacity is weighted towards low-opacity regions of a spectrum, where these quasimolecular features are found.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Communication-Constrained Expansion Planning for Resilient Distribution Systems

Distributed generation and remotely controlled switches have emerged as important technologies to improve the resiliency of distribution grids against extreme weather-related disturbances. Therefore it becomes important to study how best to place them on the grid in order to meet a resiliency criteria, while minimizing costs and capturing their dependencies on the associated communication systems that sustain their distributed operations. This paper introduces the Optimal Resilient Design Problem for Distribution and Communication Systems (ORDPDC) to address this need. The ORDPDC is formulated as a two-stage stochastic mixed-integer program that captures the physical laws of distribution systems, the communication connectivity of the smart grid components, and a set of scenarios that specifies which components are affected by potential disasters. The paper proposes an exact branch-and-price algorithm for the ORDPDC that features a strong lower bound and a variety of acceleration schemes to address degeneracy. The ORDPDC model and branch-and-price algorithm were evaluated on a variety of test cases with varying disaster intensities and network topologies. The results demonstrate the significant impact of the network topologies on the expansion plans and costs, as well as the computational benefits of the proposed approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time

Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient boosted trees [XGB] and random forest [RF]) and two DL models (multi-layer perceptron and encoder–decoder model). The disease models were trained on data from seven different countries at the region-level between 2009–2017. Forecasting performance of all models was assessed using mean absolute error, root mean square error, and Poisson deviance across Australia, Israel, and the United States for the months of January through August of 2018. The overall model results were compared across diseases as well as various data splits, including country, regions with highest and lowest cases, and the forecasted months out (i.e., nowcasting, short-term, and long-term forecasting). Overall, the XGB models performed the best for all diseases and, in general, tree-based ML models performed the best when looking at data splits. There were a few instances where the statistical or DL models had minutely smaller error metrics for specific subsets of typhoid, which is a disease with very low case counts. Feature importance per disease was measured by using four tree-based ML models (i.e., XGB and RF with and without region name as a feature). The most important feature groups included previous case counts, region name, population counts and density, mortality causes of neonatal to under 5 years of age, sanitation factors, and elevation. This study demonstrates the power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Implementation of Monte Carlo Transport in the General Relativistic SpEC Code

Neutrino transport and neutrino-matter interactions are known to play an important role in the evolution of neutron star mergers and of their post-merger remnants. Neutrinos cool remnants, drive post-merger winds, and deposit energy in the low-density polar regions where relativistic jets may eventually form. Neutrinos also modify the composition of the ejected material, impacting the outcome of nucleosynthesis in merger outflows and the properties of the optical/infrared transients that they power (kilonovae). So far, merger simulations have largely relied on approximate treatments of the neutrinos (leakage, moments) that simplify the equations of radiation transport in a way that makes simulations more affordable but also introduces unquantifiable errors in the results. To improve on these methods, we recently published a first simulation of neutron star mergers using a low-cost Monte Carlo algorithm for neutrino radiation transport. Our transport code limits costs in optically thick regions by placing a hard ceiling on the value of the absorption opacity of the fluid, yet all approximations made within the code are designed to vanish in the limit of infinite numerical resolution. We provide here an in-depth description of this algorithm, of its implementation in the SpEC merger code, and of the expected impact of our approximations in optically thick regions. We argue that the last is a subdominant source of error at the accuracy reached by current simulations and for the interactions currently included in our code. We also provide tests of the most important features of this code.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

GCAM-USA v5.3_water_dispatch: integrated modeling of subnational US energy, water, and land systems within a global framework

Abstract. This paper describes GCAM-USA v5.3_water_dispatch, an open-source model that represents key interactions across economic, energy, water, and land systems in a consistent global framework with subnational detail in the United States. GCAM-USA divides the world into 31 geopolitical regions outside the United States (US) and represents the US economy and energy systems in 51 state-level regions (50 states plus the District of Columbia). The model also includes 235 water basins and 384 land use regions, and 23 of each fall at least partially within the United States. GCAM-USA offers a level of process and temporal resolution rare for models of its class and scope, including detailed subnational representation of US water demands and supplies and sub-annual operations (day and night for each month) in the US electric power sector. GCAM-USA can be used to explore how changes in socioeconomic drivers, technological progress, or policy impact demands for (and production of) energy, water, and crops at a subnational level in the United States while maintaining consistency with broader national and international conditions. This paper describes GCAM-USA's structure, inputs, and outputs, with emphasis on new model features. Four illustrative scenarios encompassing varying socioeconomic and energy system futures are used to explore subnational changes in energy, water, and land use outcomes. We conclude with information about how public users can access the model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Impact of intermolecular interactions on the spectroscopic signals, energetics, and redox behavior of high valent 237 Np (Final Report)

Our studies have led to definitive spectral assignments for neptunyl-neptunyl interactions in aqueous solutions that will be valuable for predicting Np(V) speciation in solution. We have definitively demonstrated how actinyl-hydrogen and actinyl-cation interactions can influence spectral features for uranyl and neptunyl system. In addition, we have developed DFT methodologies to predict enthalpies of formation for uranyl and neptunyl systems and linked stability to chemical descriptors (hydrogen interaction energies or the electrostatic attraction energies). The counterion influenced the rate of Np(V) oxidation to Np(VI) neptunates and [Np(VII)O 4 OH] 3- complexes. Oxidation to Np(VII) during ozonolysis takes place through the presence of hydroxyl radicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Phosphonium-Based Metal Halide Semiconductors: When a Single Atom Dictates Water and Thermal Stability

Phosphonium-based (R-P(R’) 3 + ) metal halide semiconductors (MHS) emerged recently as a promising family of materials offering enhanced water and thermal stability over conventional ammonium-based (R-NH 3 + ) MHS. Despite this performance, there is a lack of systematic studies that elucidate the origin of these features as well as the underlying optoelectronic properties. Here, we report here the synthesis of nine new 1D materials (L)PbX 3 (L= C4-P, C6-P, C6-N, P3-P, P4-P, X= I, Br), using custom made organic ligands, namely butyl(triethyl)phosphonium bromide (C4-P), hexyl(triethyl)phosphonium bromide (C6-P), benzyl(triethyl)phosphonium bromide (P3-P), benzyl(trimethyl)phosphonium bromide (P4-P), and hexyl(triethyl)ammonium bromide (C6-N). Most of the materials are water stable for over 21 months, while the assembly of the isostructural (C6-N)PbBr 3 and (C6-P)PbBr 3 allowed us to directly identify the impact of a single atom (phosphorus versus nitrogen), on water stability. Notably, the C6-P analog remains water stable for 21 months, whereas the C6-N analog dissolves in water. It was found that neither crystal packing nor the lack of hydrogen bond interactions with water is responsible for this record stability performance, challenging currently established claims on the 1 origin of water stability in MHS. All materials feature broad light emission at RT, with (P3-P)PbBr 3 exhibiting a PLQY of 22.0% ascribed to permanent traps, while electrochemical studies uncovered for the first time the redox properties and their strong potential for the sensing of per- and polyfluoroalkyl substances (PFAS) in water samples. Our work showcases the potential of these compounds for applications where thermal and water stability are interwoven, potentially rendering their (R-NH 3 + ) based counterparts obsolete.

PFAS sensing↗
DOE OSTI2025

Critical impact of experimentally-driven strut level anisotropic material models in advanced stress analysis of additively manufactured lattice structures

The rapid acceleration in materials discovery may overshadow the importance of thoroughly understanding the mechanical performance of newly developed materials in demanding environments. The recent interest in combining parametric studies with machine learning techniques to explore how changes in specific processing parameters or model inputs affect the overall behavior of a material system can only be truly beneficial if the governing constitutive relations describing material behavior are accurately established. In this study, we demonstrate the critical impact of accurately representing strut-level anisotropic material behavior in advanced stress analysis of additively manufactured lattice structures (AMLS). We introduce a systematic experimental and modeling approach for developing strut-level anisotropic elastoplastic material models that account for the influence of microstructural features such as porosity, texture, and surface roughness on the development of local anisotropic mechanical properties, which vary with strut orientation relative to the build direction (BD). As a result the presented material model captures and relates the statistics of spatially varying struts’ microstructural features to the local stress distribution. Our findings suggest that incorporating strut-level anisotropic material behavior into unit cell analysis significantly influences the load distribution and evolution of local stresses within the structure. Therefore, accounting for this anisotropy is critical for developing an understanding of unit cell behavior and performance, including subsequent topology/component design optimization based on this analysis.

Sahoo, Subhadip [University of Arizona]↗
DOE OSTI2025

Unsteady aerodynamic loads on pitching aerofoils represented by Gaussian body force distributions

The actuator line model (ALM) is an approach commonly used to represent lifting and dragging devices like wings and blades in large-eddy simulations (LES). The crux of the ALM is the projection of the actuator point forces onto the LES grid by means of a Gaussian regularisation kernel. The minimum width of the kernel is constrained by the grid size; however, for most practical applications like LES of wind turbines, this value is an order of magnitude larger than the optimal value that maximises accuracy. This discrepancy motivated the development of corrections for the actuator line, which, however, neglect the effect of unsteady spanwise shed vorticity. In this work we develop a model for the impact of spanwise shed vorticity on the unsteady loading of an aerofoil modelled as a Gaussian body force distribution, where the model is applicable within the regime of unsteady attached flow. The model solution is derived both in the time and frequency domain and features an explicit dependence on the Gaussian kernel width. We verify the model with ALM-LES for both pitch steps and periodic pitching. The model solution is compared with Theodorsen theory and validated with both computational fluid dynamics using body fitted grids and experiment. It is concluded that the optimal kernel width for unsteady aerodynamics is approximately 40 % of the chord. The ALM is able to predict the magnitude of the unsteady loading up to a reduced frequency of 𝑘 ≈ 0.2.

17 WIND ENERGY↗
DOE OSTI2020

Effects of Noncovalent Interactions on High-Spin Fe(IV)–Oxido Complexes

High-valent nonheme Fe IV –oxido species are key intermediates in biological oxidation, and their properties are proposed to be influenced by the unique microenvironments present in protein active sites. Microenvironments are regulated by noncovalent interactions, such as hydrogen bonds (H-bonds) and electrostatic interactions; however, there is little quantitative information about how these interactions affect crucial properties of high valent metal–oxido complexes. To address this knowledge gap, we introduced a series of Fe IV –oxido complexes that have the same S = 2 spin ground state as those found in nature and then systematically probed the effects of noncovalent interactions on their electronic, structural, and vibrational properties. The key design feature that provides access to these complexes is the new tripodal ligand [poat] 3– , which contains phosphinic amido groups. An important structural aspect of [Fe IV poat(O)] - is the inclusion of an auxiliary site capable of binding a Lewis acid (LA II ); we used this unique feature to further modulate the electrostatic environment around the Fe–oxido unit. Experimentally, studies confirmed that H-bonds and LA II s can interact directly with the oxido ligand in Fe IV –oxido complexes, which weakens the Fe=O bond and has an impact on the electronic structure. Additionally, we found that relatively large vibrational changes in the Fe–oxido unit correlate with small structural changes that could be difficult to measure, especially within a protein active site. Our work demonstrates the important role of noncovalent interactions on the properties of metal complexes, and that these interactions need to be considered when developing effective oxidants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Uncertainties in Simulating Flooding During Hurricane Harvey Using 2D Shallow Water Equations

Abstract Flooding is one of the most impactful weather‐related natural hazards. Numerical models that solve the two dimensional (2D) shallow water equations (SWE) represent the first‐principles approach to simulate all types of spatial flooding, such as pluvial, fluvial, and coastal flooding, and their compound dynamics. High spatial resolution (e.g., () m) is needed in 2D SWE simulations to capture flood dynamics accurately, resulting in formidable computational challenges. Thus, relatively coarser spatial resolutions are used for large‐scale simulations of flooding, which introduce uncertainties in the results. It is unclear how the uncertainty associated with the model resolution compares to the uncertainties in precipitation data sets and assumptions regarding boundary conditions when channelized flows interact with other water bodies. In this study, we compare these three sources of uncertainties in 2D SWE simulations for the 2017 Houston flooding event. Our results show that precipitation uncertainty and mesh resolution have more significant impacts on the simulated streamflow and inundation dynamics than the choice of the downstream boundary condition at the watershed outlet. We point out the viability to confine the uncertainty of coarsening mesh resolution by using the variable resolution mesh (VRM) which refines critical topographic features with far fewer grid cells. Specifically, in simulations with VRM, the simulated inundation depths over the refined region are comparable to that use the finest uniform mesh. This study contributes to understanding the challenges and pathways for applying 2D SWE models to improve the realism of flood simulations over large scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Edgewise Structural Damping of a 2.8-MW Land-Based Wind Turbine Rotor Blade

Modern wind turbines push the predictive capabilities of state-of-the-art aero-servo-elastic tools. The existing limitations hide across the numerical tool chain and can result in serious issues, such as missing aeroelastic instabilities during the design phase. Structural damping is an input that is usually hard to estimate, but also has a major impact on the turbine behavior. In this paper, we discuss an experiment that aims to accurately quantify the structural damping characterizing the edgewise modes of modern wind turbine blades. The experiment is carried out on a 2.8-MW land-based wind turbine and features a fast yaw actuation that induces an edgewise motion on one of the three blades. The Covariant-subspace system identification (Cov-SSI) method is then used to post-process the blade root moment to estimate the short-term edgewise structural damping. Despite limitations of the Cov-SSI method, which consistently under-predicts the absolute values of damping, we observe that structural damping decreases across the first three blade edgewise modes, which is different from the stiffness-proportional damping model that assumes that structural damping increases with the modes. This paper argues that a stiffness-proportional damping model, which is implemented in most aeroelastic tools, is therefore not conservative and might hide aeroelastic instabilities that can instead appear in the field.

17 WIND ENERGY↗
DOE OSTI2023

Sustainable production of plastic degrading enzymes

The 21st century faces the challenge of transitioning to a climate-neutral economy. This hurdle will need to be addressed in a myriad of approaches, including all societal spheres, as highlighted in several UN and EU reports, including new and accessed technologies. Among them, biotechnology can play a core role by exploiting its environmental benefits (e.g., substituting fossil-based products and expected greenhouse emission reductions). In some cases, biotech can even outperform fossil counterparts' features (e.g., the biodegradability of some bioplastics). Microalga is an attractive bioplastic feedstock in this scenario since it directly substitutes petroleum and captures CO2. However, albeit its environmental benefits, the microalgal process still needs to be tipped economically and the net-positive ecological impact improved to drive adoption. To that end, we envisioned the use of a green alga as a source of bioplastic feedstock and industrial enzymes, combining natural traits of microalgae with engineered properties in a resourceful process to petroleum-based products replacement. As proof of concept, we demonstrated that we could use C. reinhardtii, a green algae, to produce plastic-degrading enzymes. We assemble a vector to secrete PHL7, a recently described PETase, capable of degrading post-consumer PET plastic. We successfully engineered the cell line to secrete the plastic-degrading enzyme. We confirmed the secretion of the enzyme by observing halos in agar plates supplemented with plastic dispersions, enzymatic assays, zymograms, and protein sequencing of supernatant samples. This approach allows using green algae to replace petrochemical-derived plastic while producing the tool necessary to recycle existing plastics sustainably.

Dutra Molino, Joao Vitor↗