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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Phage K gp102 Drives Temperature-Sensitive Antibacterial Activity on USA300 MRSA

There is widespread interest in using obligately lytic bacteriophages (“phages”) to treat human bacterial infections. Among Staphylococcus aureus infections, the USA300 lineage is a frequent cause of invasive disease. We observed that phage K, a model S. aureus myophage, exhibits temperature-sensitive growth on USA300 strains, with the wild-type phage providing poorer growth suppression in broth and forming smaller and fainter plaques at 37 °C vs. 30 °C. We isolated 65 mutants of phage K that had improved plaquing characteristics at 37 °C when compared to the parental phage. In all 65 mutants, this phenotype was attributable to loss-of-function (LoF) mutations in gp102, which encodes a protein of unknown function that has homologs only among the Herelleviridae (SPO1-like myophages infecting gram-positive bacteria). Additional experiments with representative mutants consistently showed that the temperature-sensitive plaque phenotype was specific to USA300 MRSA strains and that Gp102 disruption was correlated with improved suppression of bacterial growth in broth and improved antibacterial activity in a mouse model of upper respiratory tract infection. The same genotype and in vitro phenotypes could be replicated in close relatives of phage K. Gp102 disruption did not have a detectable effect on adsorption but did delay cell culture lysis relative to wild-type under permissive infection conditions, suggesting that gp102 conservation might be maintained by selective pressure for more rapid replication. Expression of gp102 on a plasmid was toxic to both an MSSA and a USA300 MRSA strain. Molecular modeling predicts a protein with two helix-turn-helix domains that displays some similarity to DNA-binding proteins such as transcription factors. While its function remains unclear, gp102 is a conserved gene that is important to the infection process of Kayvirus phages, and it appears that the manner in which USA300 strains defend against them at 37 °C can be overcome by gp102 LoF mutations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Recursive Blind Forecasting of Photovoltaic Generation and Consumer Load for Microgrids

Existing forecasting frameworks that predict time-series photovoltaic (PV) generation and consumer load for micro-grids' operation and control assume near-continuous availability of real-time predictors from the field. The incoming data are used to periodically re-train the models and update forecast snapshots over a moving horizon window. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. This paper bridges the shortcoming by leveraging a previously proposed forecasting framework that is resilient to abrupt changes in data quality caused by communication losses. Assuming no availability of real-time field system data, which is typical in extreme weather events such as hurricanes, the framework uses lightweight recursive time-series models to independently forecast solar irradiance, ambient temperature, PV power, and consumer load for three horizon windows: 24 hours, 12 hours, and 1 hour. Four types of ensemble-based regression trees-simple gradient boosted trees (GBR), GBR with an adaptive component (A-GBR), random forests (RF), and extra trees (ExTR)-are leveraged and their performances are compared against a simple historical weekly mean. Numerical results show that A-GBR performs better on average by 32% for 24-hour horizon and 39% for 12-hour horizon, whereas ExTR outdoes the other models on average by 10% for 1-hour horizon.

Sundararajan, Aditya↗
DOE OSTI2025

Implementation of disruptive designs for gas turbine components using direct energy deposition additive manufacturing

This research aims to develop a framework for establishing the correlation between in-situ monitoring data, process parameters, and microstructure evolution in blown-powder laser-directed energy deposition (DED) additive manufacturing (AM). To achieve this, a comprehensive manufacturing framework has been developed, spanning from in-situ data acquisition, melt-pool simulation, microstructure modeling, and statistical microstructure quantification. A machine learning-based surrogate model is constructed to predict melt pool geometry directly from in-situ coaxial camera data. The surrogate model is trained using outputs from a high-fidelity melt pool simulation, which provides accurate melt pool dimension data under varying process conditions. The predicted melt pool geometry is then used as input to a microstructure model to predict microstructural features. To rigorously compare and analyze microstructures, the project introduces statistical metrics that quantify differences based on key features such as morphology and texture. Microstructures are represented using advanced statistical descriptors including angular chord length distribution, two-point spatial statistics, orientation distribution function, and global spherical harmonic. These representations are used to compute four distinct “dissimilarity scores” that quantitatively capture differences in texture and morphology. This framework is demonstrated to enable automated calibration of simulation parameters by minimizing discrepancies between simulated and target microstructures. The technology developed in this project enables direct correlation between in-situ monitoring data and resulting microstructure, paving the way for adaptive microstructure control in metal AM. This capability strengthens the connection between process parameters and final material properties, facilitating more precise and reliable material design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Simulations of axion minihalos

The axion, motivated as a solution to the strong CP problem, is also a viable dark matter candidate. The axion field takes random values in causally disconnected regions if the symmetry breaking that establishes the particle occurs after inflation, leading to white-noise density fluctuations at low wave numbers and forming dense minihalos with sub-planetary masses subsequently. There have been two recent proposals that appear capable of testing this scenario, namely using pulsar timing arrays and studying cosmological microlensing caustics. Motivated by these proposals, we use N-body simulations to study the formation of substructures from white-noise density fluctuations. The density profiles of our relaxed axion minihalos can be described by the Navarro–Frenk–White profile, and the minihalos’ concentration number agrees well with a simple, physically-motivated model. We develop a semi-analytic formula to fit the mass function from our simulation, which agrees broadly at different redshifts and only differs at factor of two level from classic halo mass functions. This analytic mass function allows us to consider uncertainties in the post-inflation axion scenario, as well as extrapolate our high-redshift simulations results to the present. Our work estimates the present-day abundance of axion substructures, as is necessary for predicting their effect on cosmological microlensing caustics and pulsar timing. Our calculations suggest that if pulsar timing and microlensing probes can reach recent sensitivity forecasts, they may be sensitive to the post-inflation axion dark matter scenario, even when accounting for uncertainties pertaining to axion strings. For pulsar timing, the most significant caveat is whether axion minihalos are disrupted by stars, which our estimates show is mildly important at the most relevant masses. In conclusion, as our gravitational simulations are scale invariant, the results can be extended to models where the dark matter is comprised of other axion-like particles and even clusters of primordial black holes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Implementation of Disruptive Designs for Gas Turbine Components Utilizing Additive Manufacturing

Electron beam melting (EBM) is an additive manufacturing process used to print metallic components with complex geometries for aerospace applications. A preheating characteristic enables this technology to avoid large accumulation of residual stress during printing. However, cracking mechanisms particularly in Ni-based superalloys are associated with not only residual stress but also with microstructure (i.e., solidification morphology, transient evolution of γ'). Siemens is committed to AM technology for gas turbine applications. The ability to novel predictive designs of turbine blades would reduce process development time and allow acquisition of key microstructure and performance data early in a design campaign. Siemens initiated a project to establish the design rules for the fabrication of non-printable Nibased superalloys. This phase 1 program demonstrates the feasibility for virtual fabrication of the actual blade geometries through EBM process.

42 ENGINEERING↗
DOE OSTI2021

Microscopic ejecta measurements from hypervelocity impacts on aluminum and powdered regolith targets

Meteoroids and space debris pose a two-fold threat to spacecraft due to the risk of both mechanical damage from the impact and electrical damage from the impact-generated plasma, which produces electromagnetic emissions. Analysis of impact products, however, has the potential to provide useful information about planetary rings and small solar system bodies, such as asteroids and comets. Understanding the plasma dynamics and radiation mechanism is essential to addressing the impact threat and extracting science data from impacts. Charge attachment to condensed-phase debris may lead to a dusty plasma under certain conditions, so interpreting hypervelocity impacts requires knowledge of both the nature of the plasma and of the ejecta. We address the latter with measurements of the microscopic ejecta distribution produced by light gas gun impacts on solid aluminum and powdered regolith simulant targets. We examine both the size and shape of the ejecta using thin-film witness plates and find that the size distributions of microscopic ejecta follow power laws. Observations from the powdered regolith simulant match well with disruption experiments of solid basalt, while measurements from the aluminum impacts diverge from extrapolations based on studies of macroscopic debris. Analysis of the ejecta shape shows a transition from smooth to irregularly shaped ejecta at the material grain size, and the shape of debris may provide insight on the phase of the ejected material. Finally, based on our results, we predict a significant number of secondary impacts on plasma sensors due to ejecta, even at considerable distance from the target, supporting the hypothesis that charged dust affects plasma measurements in the ejecta curtain.

42 ENGINEERING↗
DOE OSTI2023

AToM: Advanced Tokamak Modeling Environment

Stability constraints play an important role in integrated modeling. The global plasma stability and macro-instabilities such as internal kink modes, neoclassical tearing modes, and edge localized perturbations limit the plasma performance and can result in large-scale transient events and plasma disruptions. These macro-instabilities can be also beneficial to the plasma performance. For example, the peeling models often lead to the edge localized modes (ELMs). However, the formation of stationary edge harmonic oscillations (EHOs) due to nonlinear interaction of peeling modes can result in a transition to the Quiescent H-mode (QH-mode) without ELMs. The profiles predicted with transport models need to be refined using the MHD stability constraints. This is important especially for transient and nonlinear stages of discharges such as ramp-up access to hybrid and steady state operation, and L- to H-mode transition as well as transition to the QH-mode. Several stability and MHD codes are already included in the OMFIT framework. These codes include BALOO, ELITE, GATO, M3DC1, MARS, and NIMROD. However, the verifications of stability constraints are currently mostly excluded from transport modeling workflows. The only exception is the EPED module which includes the ELITE predictions to limit the pedestal height. Here, we improved the OMFIT workflow to include stability calculations. Including the stability conditions to the integrated modeling workflow improved the robustness of the predictive modeling discharges. Being implemented in the workflow, the stability conditions can be also used for the stability analysis of experimental data. This improved the physics understanding of various discharge scenarios and can be used in experiment planning.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

The pp → W(→ lν) + γ process at next-to-next-to-leading order

We present details of the calculation of the pp → W(→ lν)γ process at next-to-next-to-leading order in QCD, calculated using the jettiness slicing method. The calculation is based entirely on analytic amplitudes. Because of the radiation zero, the NLO QCD contribution from the gq channel is as important as the contribution from the Born $ q\overline{q} $ process, disrupting the normal counting of leading and sub-leading contributions. We also assess the importance of electroweak (EW) corrections, including the EW corrections to both the six-parton channel 0 →$ \overline{u} d\nu {e}^{+}\gamma g $ and the five-parton channel 0 →$ \overline{u} d\nu {e}^{+}\gamma $. Previous experimental results have been shown to agree with theoretical predictions, taking into account the large experimental errors. With the advent of run II data from the LHC, the statistical errors on the data will decrease, and will be competitive with the error on theoretical predictions for the first time. We present numerical results for $ \sqrt{s} $ = 7 and 13 TeV. Analytic results for the one-loop six-parton QCD amplitude and the tree-level seven-parton QCD amplitude are presented in appendices.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Drug discovery efforts at George Mason University

With over 39,000 students, and research expenditures in excess of $200 million, George Mason University (GMU) is the largest R1 (Carnegie Classification of very high research activity) university in Virginia. Mason scientists have been involved in the discovery and development of novel diagnostics and therapeutics in areas as diverse as infectious diseases and cancer. Below are highlights of the efforts being led by Mason researchers in the drug discovery arena. To enable targeted cellular delivery, and non-biomedical applications, Veneziano and colleagues have developed a synthesis strategy that enables the design of self-assembling DNA nanoparticles (DNA origami) with prescribed shape and size in the 10 to 100 nm range. The nanoparticles can be loaded with molecules of interest such as drugs, proteins and peptides, and are a promising new addition to the drug delivery platforms currently in use. The investigators also recently used the DNA origami nanoparticles to fine tune the spatial presentation of immunogens to study the impact on B cell activation. These studies are an important step towards the rational design of vaccines for a variety of infectious agents. To elucidate the parameters for optimizing the delivery efficiency of lipid nanoparticles (LNPs), Buschmann, Paige and colleagues have devised methods for predicting and experimentally validating the pKa of LNPs based on the structure of the ionizable lipids used to formulate the LNPs. These studies may pave the way for the development of new LNP delivery vehicles that have reduced systemic distribution and improved endosomal release of their cargo post administration. To better understand protein-protein interactions and identify potential drug targets that disrupt such interactions, Luchini and colleagues have developed a methodology that identifies contact points between proteins using small molecule dyes. The dye molecules noncovalently bind to the accessible surfaces of a protein complex with very high affinity, but are excluded from contact regions. When the complex is denatured and digested with trypsin, the exposed regions covered by the dye do not get cleaved by the enzyme, whereas the contact points are digested. The resulting fragments can then be identified using mass spectrometry. The data generated can serve as the basis for designing small molecules and peptides that can disrupt the formation of protein complexes involved in disease processes. For example, using peptides based on the interleukin 1 receptor accessory protein (IL-1RAcP), Luchini, Liotta, Paige and colleagues disrupted the formation of IL-1/IL-R/IL-1RAcP complex and demonstrated that the inhibition of complex formation reduced the inflammatory response to IL-1B. Working on the discovery of novel antimicrobial agents, Bishop, van Hoek and colleagues have discovered a number of antimicrobial peptides from reptiles and other species. DRGN-1, is a synthetic peptide based on a histone H1-derived peptide that they had identified from Komodo Dragon plasma. DRGN-1 was shown to disrupt bacterial biofilms and promote wound healing in an animal model. The peptide, along with others, is being developed and tested in preclinical studies. Other research by van Hoek and colleagues focuses on in silico antimicrobial peptide discovery, screening of small molecules for antibacterial properties, as well as assessment of diffusible signal factors (DFS) as future therapeutics. The above examples provide insight into the cutting-edge studies undertaken by GMU scientists to develop novel methodologies and platform technologies important to drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Transition from ITG to MTM linear instabilities near pedestals of high density plasmas

Here, investigation of linear gyrokinetic ion-scale modes ( k θ ρ s = 0.3) finds that a transition from ion temperature gradient to microtearing mode (MTM) dominance occurs as the density is increased near the pedestal region of a parameterized DIII-D sized tokamak. H-modes profile densities, temperatures, and equilibria are parameterized utilizing the OMFIT PRO_create module. With these profiles, linear gyrokinetic ion-scale instabilities are predicted with CGYRO. This transition (nMTM) has a weak dependence on radial location in the region near the top of the pedestal ( ρ = 0.7 – 0.9), which allows simulating single radii to examine the approximate scaling of nMTM with global parameters. The critical nMTM is found to scale with plasma current. Additionally, increasing the minor radius by decreasing the aspect ratio and increasing the major radius are found to reduce nMTM. However, any relationship between nMTM and density limit physics remains unclear as nMTM increases relative to the Greenwald density with larger minor radius and with larger magnetic field, suggesting that the transport due to MTM may be less important for a reactor. Additionally, nMTM is sensitive to the pedestal temperature, the local electron and ion gradients, the ratio of ion to electron temperature T i / T e, and the current profile. MTMs are predicted to be the dominant instability in the core at similar Greenwald fractions for DIII-D, NSTX, and NSTX-U H-mode experiments, supporting the results of the parameterized study. Additionally, MTMs continue to be the dominant linear instability in a DIII-D L-mode after an H–L transition as the plasma approaches a density limit disruption despite the large change in plasma profiles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

MHD modeling of shattered pellet injection in JET

Abstract Nonlinear 3D MHD simulations of shattered-pellet injection (SPI) in JET show prototypical SPI-driven disruptions using the M3D-C1 and NIMROD extended-MHD codes. Initially, radiation-driven thermal quenches are accelerated by MHD activity as the pellet crosses rational surfaces, leading to a radiation spike, global stochasticization of the magnetic field, and a complete thermal quench. Eventually, current quenches, preceded by a current spike are seen as the Ohmic heating becomes equal to the radiative cooling. The results are qualitatively similar for both a single monolithic pellet, pencil-beam model, and a realistic shatter to represent the SPI plume. A scan in viscosity from 500 to 2000 m 2 s −1 for MHD simulations finds that reducing viscosity increases MHD activity and decreases thermal quench time slightly. A realistic cloud of fragments modeling shows that mixed-D–Ne pellet travels deeper into the plasma core before the thermal quench. At the slow pellet speeds, the pellet is found to be moving slowly enough inward that even the 5% neon in the mixed pellet is enough to effectively radiate the thermal energy available. Radiation toroidal peaking is predicted to be at levels consistent with experimental observations and reduced as the pellet travels deeper into the plasma. These simulations lay the ground work for more-sophisticated validative and predictive modeling of SPI in JET using both M3D-C1 and NIMROD.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Secure State Estimation with Asynchronous Measurements for Coordinated Cyber Attack Detection in Active Distribution Systems

Coordinated cyber attacks tamper with measurement data to disrupt the situational awareness of active distribution systems. Various sensors report measurements asynchronously at different rates, which introduces challenges during state estimation. In addition, this forces cyber intruders to exert greater effort to compromise multiple communication channels and launch coordinated attacks. Therefore, multi-channel and asynchronous measurements could be harnessed to develop more secure cyber defense strategies. In this paper, a prediction-correction-based multi-rate observer is designed to exploit the value of asynchronous measurements for the detection of coordinated false data injection (FDI) attacks. First, a time-function-dependent prediction-correction strategy is proposed to adjust the sampling interval for each sensor’s measurement. Then, an observer is designed based on the trade-off between estimation error and the optimal period of the most recent sampling instant, with the convergence of estimation error with the maximum permitted sampling interval. Moreover, the conditions for exponential stability are developed using the Lyapunov–Krasovskii functional technique. Next, a coordinated FDI attack detection strategy is developed based on the dual nonlinear minimization problem. The proposed attack detection and secure state estimation strategies are tested on the IEEE 13-node system. Simulation results show that these schemes are effective in enhancing attack detection based on asynchronous measurements or compromised data.

asynchronous measurements↗
DOE OSTI2023

Decrease in multiple complement proteins associated with development of islet autoimmunity and type 1 diabetes

Type 1 diabetes (T1D) is a chronic condition caused by autoimmune destruction of the insulin-producing pancreatic ß-cells. While it is known that gene-environment interactions play a key role in triggering the autoimmune process leading to T1D, the pathogenic mechanism leading to the appearance of islet autoantibodies - biomarkers of autoimmunity – is poorly understood. Here we show that disruption of the complement system precedes the detection of islet autoantibodies and persists through diagnosis. Our results suggest that children who exhibit islet autoimmunity and progress to clinical T1D are complement deficient relative to those who do not progress within a similar timeframe. Thus, the complement pathway, an understudied mechanistic and therapeutic target in T1D, merits increased attention for use as protein biomarkers of prediction and potentially prevention of T1D.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Molecular Mechanisms of Sorbed Ion Effects during Boehmite Particle Aggregation

Classical theories of particle aggregation, such as Derjaguin–Landau–Verwey–Overbeek (DLVO), do not explain recent observations of ion-specific effects or the complex concentration dependence for aggregation. Thus, here, we probe the molecular mechanisms by which selected alkali nitrate ions (Na + , K + , and NO 3 –) influence aggregation of the mineral boehmite (γ-AlOOH) nanoparticles. Nanoparticle aggregation was analyzed using classical molecular dynamics (CMD) simulations coupled with the metadynamics rare event approach for stoichiometric surface terminations of two boehmite crystal faces. Calculated free energy landscapes reveal how electrolyte ions alter aggregation on different crystal faces relative to pure water. Consistent with experimental observations, we find that adding an electrolyte significantly reduces the energy barrier for particle aggregation (~3–4×). However, in this work, we show this is due to the ions disrupting interstitial water networks, and that aggregation between stoichiometric (010) basal–basal surfaces is more favorable than between (001) edge–edge surfaces (~5–6×) due to the higher interfacial water densities on edge surfaces. The interfacial distances in the interlayer between aggregated particles with electrolytes (~5–10 Å) are larger than those in pure water (a few Ångströms). Together, aggregation/disaggregation in salt solutions is predicted to be more reversible due to these lower energy barriers, but there is uncertainty on the magnitudes of the energies that lead to aggregation at the molecular scale. By analyzing the peak water densities of the first monolayer of interstitial water as a proxy for solvent ordering, we find that the extent of solvent ordering likely determines the structures of aggregated states as well as the energy barriers to move between them. Further, the results suggest a path for developing a molecular-level basis to predict the synergies between ions and crystal faces that facilitate aggregation under given solution conditions. Such fundamental understanding could be applied extensively to the aggregation and precipitation utilization in the biological, pharmaceutical, materials design, environmental remediation, and geological regimes.

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

Quenching of the octupole rotational band in 71 Ge

The main goal of this study was to enhance the knowledge of the properties of the known octupole rotational band in 71 Ge, including transition rates, and to investigate whether the band persists to higher spin. An experiment at Florida State University using the 62 Ni ( 14 C, 𝛼⁢𝑛) reaction at 50 MeV was used to produce 71 Ge at high spin. An array of ten Compton-suppressed Ge detectors, consisting of three Clover detectors and seven single-crystal detectors, was used to measure the 𝛾 decays in coincidence. An analysis of the resulting coincidence spectra resulted in the placement of 13 additional transitions in the 71 Ge level scheme, one of which (1092 keV) extends the octupole band to a (35/2 − ) state at 8206 keV. However, calculations of the kinematic moment of inertia show that the 1092-keV transition disrupts the rigid rotational pattern and is likely associated with a band crossing, potentially quenching the octupole deformation. Transition strengths inferred from lifetime measurements in the octupole band are not well reproduced by either shell-model calculations using the JUN45 interaction or a semimicroscopic cluster model, while those for the 𝜈⁢𝑔 9/2 band are in good agreement with the corresponding shell-model predictions. Comparisons between the octupole band in 71 Ge and negative-parity bands based on 3 − octupole states in some neighboring even-even nuclei show both similarities and differences. Systematic trends within the 𝜈⁢𝑔 9/2 bands among odd-𝐴 Ge isotopes point to increased collectivity near 𝑁 = 40.

59 ≤ A ≤ 89↗
DOE OSTI2024

A physics-constrained deep learning surrogate model of the runaway electron avalanche growth rate

A surrogate model of the runaway electron avalanche growth rate in a magnetic fusion plasma is developed. This is accomplished by employing a physics-informed neural network (PINN) to learn the parametric solution of the adjoint to the relativistic Fokker–Planck equation. The resulting PINN is able to evaluate the runaway probability function across a broad range of parameters in the absence of any synthetic or experimental data. This surrogate of the adjoint relativistic Fokker–Planck equation is then used to infer the avalanche growth rate as a function of the electric field, synchrotron radiation and effective charge. Predictions of the avalanche PINN are compared against first principle calculations of the avalanche growth rate with excellent agreement observed across a broad range of parameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Woody Plant Transformation: Current Status, Challenges, and Future Perspectives

Woody plants, comprising forest and fruit tree species, provide essential ecological and economic benefits to society. Their genetic improvement is challenging due to long generation intervals and high heterozygosity. Genetic transformation, which combines targeted DNA delivery with plant regeneration from transformed cells, offers a powerful alternative to accelerating their domestication and improvement. Agrobacterium tumefaciens, Rhizobium rhizogenes, and particle bombardment have been widely used for DNA delivery into a wide variety of explants, including leaves, stems, hypocotyls, roots, and embryos, with regeneration occurring via direct organogenesis, callus-mediated organogenesis, somatic embryogenesis, or hairy root formation. Despite successes, conventional approaches are hampered by low efficiency, genotype dependency, and a reliance on challenging tissue culture. This review provides a critical analysis of the current landscape in woody plant transformation, moving beyond a simple summary of techniques to evaluate the co-evolution of established platforms with disruptive technologies. Key advances among these include the use of developmental regulators to engineer regeneration, the rise in in planta systems to bypass tissue culture, and the imperative for DNA-free genome editing to meet regulatory and public expectations. By examining species-specific breakthroughs in key genera, including Populus, Malus, Citrus, and Pinus, this review highlights a paradigm shift from empirical optimization towards rational, predictable engineering of woody plants for a sustainable future.

Agrobacterium tumefaciens↗
DOE OSTI2020

Simulations and Experiments Reveal Effect of Nanopores on Helium Diffusion in Quartz

The diffusion properties of noble gases in minerals are widely used to reconstruct the thermal histories of rocks. In this work, we combine density functional theory (DFT) calculations with laboratory experiments to investigate controls on helium diffusion in quartz. DFT calculations for perfect α-quartz predict substantially lower activation energies and frequency factors for helium diffusion than observed in laboratory experiments, especially in the [001] direction. These results imply that no helium could be retained in quartz at Earth surface temperatures, which conflicts with observations of partial cosmogenic 3 He retention over geologic time scales. Here, we implement a model of helium diffusion in α-quartz modulated by nanopore defects that disrupt energetically favorable diffusion pathways. In this model, we find that laboratory-determined diffusivities can be most closely reproduced when a helium atom encounters ~70 nanopore sites per million interstitial sites. The results of our model indicate that diffusion of helium in natural quartz, like other noble gases in other minerals, can be significantly modulated by extended defects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗