Characterizing the portability of phage-encoded homologous recombination proteins
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Field-measured Arctic vegetation cover data is essential for creating accurate, high-quality vegetation structure and composition maps. Extrapolating field data into high-resolution cover maps provides detailed, function-specific information for use in Earth System Models, vegetation classifications, and monitoring vegetation change over time and space. However, field campaigns that collect plant cover vary substantially in scope, method, and purpose, which makes them difficult to unify across data stores, and they are often not designed to meet remote sensing needs. In this work, we synthesized and harmonized field-based fractional cover data from various data stores to create a high-quality, consistent repository schema for remote sensing-based vegetation cover mapping applications. We developed a reproducible workflow for synthesizing visual estimate and point-intercept fractional cover data. The resultant Pan-Arctic Vegetation Cover (PAVC) database contains synthesized fractional cover at both the species and plant functional type levels. The latter includes absolute foliar cover for deciduous shrubs and trees, evergreen shrubs and trees, forbs, graminoids, lichen, bryophytes, and “other” vegetation, as well as absolute cover for litter and top cover for water and bare ground.
Widespread aridification of the land surface causes substantial environmental challenges and is generally well documented. However, the mechanisms underlying increased aridity remain relatively underexplored. Here, we investigated the anthropogenic and natural factors affecting long-term global aridity changes using multisource observation-based aridity index, factorial simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6), and rigorous detection and attribution (D&A) methods. Our study found that anthropogenic forcings, mainly rising greenhouse gas emissions (GHGE) and aerosols, caused the increased aridification of the globe and each hemisphere with high statistical confidence for 1965–2014; the GHGE contributed to drying trends, whereas the aerosol emissions led to wetting tendencies; moreover, the bias-corrected CMIP6 future aridity index based on the scaling factors from optimal D&A demonstrated greater aridification than the original simulations. These findings highlight the dominant role of human effects on increasing aridification at broad spatial scales, implying future reductions in aridity will rely primarily on the GHGE mitigation.
Abstract Strong spin–orbit semiconductor nanowires coupled to a superconductor are predicted to host Majorana zero modes. Exchange (braiding) operations of Majorana modes form the logical gates of a topological quantum computer and require a network of nanowires. Here, we utilize an in-plane selective area growth technique for InSb–Al semiconductor–superconductor nanowire networks. Transport channels, free from extended defects, in InSb nanowire networks are realized on insulating, but heavily mismatched InP (111)B substrates by full relaxation of the lattice mismatch at the nanowire/substrate interface and nucleation of a complete network from a single nucleation site by optimizing the surface diffusion length of the adatoms. Essential quantum transport phenomena for topological quantum computing are demonstrated in these structures including phase-coherence lengths exceeding several micrometers with Aharonov–Bohm oscillations up to five harmonics and a hard superconducting gap accompanied by 2 e -periodic Coulomb oscillations with an Al-based Cooper pair island integrated in the nanowire network.
The availability of labile carbon (C) compounds in Arctic wetland soils is expected to increase due to thawing permafrost and increased fermentation as a result of decomposition of organic matter with warming. How microbial communities respond to this change will affect the balance of CO 2 and CH 4 emitted during anaerobic organic matter decomposition, and ultimately the net radiative forcing of greenhouse gas emissions from these soils. While soil water content limits aerobic respiration, the factors controlling methanogenesis and anaerobic respiration are poorly defined in suboxic Arctic soils. In this study, we conducted incubation experiments on two tundra soils from field sites on the Seward Peninsula, Alaska, with contrasting pH and geochemistry to determine the pathways of anaerobic microbial respiration and changes with increasing substrate availability upon warming. In incubation of soils from the circumneutral Teller site, the ratio of CO 2 to CH 4 dropped from 10 to <2 after 60 days, indicating rapid depletion of alternative terminal electron acceptors (TEAs). Addition of acetate stimulated production of CO 2 and CH 4 in a nearly 1:1 ratio, consistent with methanogenesis, and the composition of the microbial community shifted to favor clades capable of utilizing the added acetate such as the Fe(III)-reducing Geobacter and the methanogenic archaea Methanosarcina. In contrast, both CO 2 and CH 4 production declined with acetate addition during incubation of soils from the more acidic Council site, and fermentative microorganisms increased in abundance despite the high availability of fermentation products. These results demonstrate that the degree to which increasing substrate availability stimulates greenhouse gas production in tundra wetlands will vary widely depending on soil pH and geochemistry.
Phosphorus (P) is a limiting or co-limiting nutrient to plants and microorganisms in diverse ecosystems that include the arctic tundra. Certain soil minerals can adsorb or co-precipitate with phosphate, and this mineral-bound P provides a potentially large P reservoir in soils. Iron (Fe) oxyhydroxides have a high capacity to adsorb phosphate; however, the ability of Fe oxyhydroxides to adsorb phosphate and limit P bioavailability in organic tundra soils is not known. In this study, we examined the depth distribution of soil Fe and P species in the active layer (<30 cm) of low-centered and high-centered ice-wedge polygons at the Barrow Environmental Observatory on the Alaska North Slope. Soil reservoirs of Fe and P in bulk horizons and in narrower depth increments were characterized using sequential chemical extractions and synchrotron-based X-ray absorption spectroscopy (XAS). Organic horizons across all polygon features ( e.g. , trough, ridge, and center) were enriched in extractable Fe and P relative to mineral horizons. Soil Fe was dominated by organic-bound Fe and short-range ordered Fe oxyhydroxides, while soil P was primarily associated with oxides and organic matter in organic horizons but apatite and/or calcareous minerals in mineral horizons. Iron oxyhydroxides and Fe-bound inorganic P (P i ) were most enriched at the soil surface and decreased gradually with depth, and Fe-bound P i was >4× greater than water-soluble P i . These results demonstrate that Fe-bound P i is a large and ecologically important reservoir of phosphate. We contend that Fe oxyhydroxides and other minerals may regulate P i solubility under fluctuating redox conditions in organic surface soils on the arctic tundra.
We present the first results of a comprehensive supernova (SN) radiative-transfer (RT) code-comparison initiative (StaNdaRT), where the emission from the same set of standardised test models is simulated by currently used RT codes. We ran a total of ten codes on a set of four benchmark ejecta models of Type Ia SNe. We consider two sub-Chandrasekhar-mass (M tot = 1.0 M ⊙ ) toy models with analytic density and composition profiles and two Chandrasekhar-mass delayed-detonation models that are outcomes of hydrodynamical simulations. We adopt spherical symmetry for all four models. The results of the different codes, including the light curves, spectra, and the evolution of several physical properties as a function of radius and time are provided in electronic form in a standard format via a public repository. We also include the detailed test model profiles and several Python scripts for accessing and presenting the input and output files. We also provide the code used to generate the toy models studied here. In this paper, we describe the test models, radiative-transfer codes, and output formats in detail, and provide access to the repository. We present example results of several key diagnostic features.
Understanding dynamic fragmentation in shock-loaded metals and predicting properties of the resulting ejecta are of considerable importance for both basic and applied science. The nature of material ejection has been shown to change drastically when the free surface melts on compression or release. In this work, we present hydrodynamic simulations of laser-driven microjetting from micron-scale grooves on a tin surface. We study microjet formation across a range of shock strengths from drives that leave the target solid after release to drives that induce shock melting in the target. The shock-state particle velocity (Up) varies from 0.3 to 3 km/s and the shock breakout pressure is 3–120 GPa. The microjet tip velocity is 1–8 km/s and the free-surface velocity varies from 0.1 to 5 km/s. Two tin equations of state are examined: a “soft” model (LEOS 501) where the target melts for U p > 1 km/s and a more detailed multiphase model (SESAME 2161) that melts for U p > 1.4 km/s. We use these two models to examine the influence of phase change and the choice of the material model on microjet formation and evolution. We observe in our computational results that jet formation can be classified into three regimes: a low-energy regime where material strength affects jet formation, a moderate-energy regime dominated by the changing phase of tin material, and a high-energy regime where results are insensitive to the material model and jet formation is described by an idealized steady-jet theory. Using an ensemble of 2D simulations, we show that these trends hold across a wide range of drive energies and groove angles.
Radiography of low-contrast features in high-density materials evolving on a nanosecond timescale requires a bright photon source in the tens of keV range with high temporal and spatial resolution. One application for sources in this category is the study of dynamic material strength in samples compressed to Mbar pressures at the National Ignition Facility, high-resolution measurements of plastic deformation under conditions relevant to meteor impacts, geophysics, armor development, and inertial confinement fusion. Here, we present radiographic data and the modulation transfer function (MTF) analysis of a multi-component test object probed at ~100 keV effective backlighter energy using a 5 μm-thin dysprosium foil driven by the NIF Advanced Radiographic Capability (ARC) short-pulse laser (~2 kJ, 10 ps). The thin edge of the foil acts as a bright line-projection source of hard x rays, which images the test object at 13.2× magnification into a filtered and shielded image plate detector stack. The system demonstrates a superior contrast of shallow (5 μm amplitude) sinusoidal ripples on gold samples up to 90 μm thick as well as enhanced spatial and temporal resolution using only a small fraction of the laser energy compared to an existing long-pulse-driven backlighter used routinely at the NIF for dynamic strength experiments.
Deep level defects in wide bandgap semiconductors, whose response times are in the range of power converter switching times, can have a significant effect on converter efficiency. We use Deep Level Transient Spectroscopy (DLTS) to evaluate such defect levels in the n- drift layer of vertical GaN (v-GaN) power diodes with VBD ~ 1500 V. DLTS reveals three energy levels that are at ~0.6 eV (highest density), ~0.27 eV (lowest density) and ~ 45 meV (a dopant level) from the conduction band. Dopant extraction from Capacitance-Voltage measurement test (C-V) at multiple temperatures enables trap density evaluation, and the ~0.6 eV trap has a density of 1.2 × 10 15 cm -3 . Here, the 0.6 eV energy level and its density are similar to a defect that is known to cause current collapse in GaN based surface conducting devices (like HEMTs). Analysis of reverse bias currents over temperature in the v-GaN diodes indicates a predominant role of the same defect in determining reverse leakage current at high temperatures, reducing switching efficiency.
In this work, studies were conducted on epitaxial VO 2 thin films to assess to the effect of remote epitaxy on the metal–insulator transition (MIT). The epitaxial VO 2 heterostructures were synthesized on both bare Al 2 O 3 (0001) substrates and Al 2 O 3 substrates coated with two monolayer-thick graphene. While both systems exhibit the MIT, the film grown by remote epitaxy on graphene demonstrates improved transport properties. Electrical transport measurements show that the on/off ratio is enhanced by a factor of ~7.5 and the switching temperature window is narrower for VO 2 thin films grown on graphene. By characterizing the heterostructures with a suite of structural, chemical, and spectroscopic tools, we find that the graphene interlayer inhibits oxygen vacancy diffusion from Al 2 O 3 (0001) during the VO 2 growth, resulting in improved electrical behavior at the MIT.
Plastic deformation of samples compressed to Mbar pressures at high strain rates at the National Ignition Facility (NIF) forms the basis of ongoing material strength experiments in conditions relevant to meteor impacts, geophysics, armor development, and inertial confinement fusion. Hard x-ray radiography is the primary means of measuring the evolution of these samples, typically employing a slit-collimated high-Z microdot driven by the NIF laser to generate >40 keV x rays [E. Gumbrell et al., Rev. Sci. Instrum. 89, 10G118 (2018) and C. M. Huntington et al., Rev. Sci. Instrum. 89, 10G121 (2018)]. Alternatively, a dysprosium “micro-flag” target driven by the Advanced Radiographic Capability laser (~2 kJ, 10 ps) can deliver significantly higher spatiotemporal resolution [M. P. Hill et al., Rev. Sci. Instrum. 92, 033535 (2021)], especially in high-opacity samples. Here, initial experiments revealed problematic brightness and spectral gradients from this source, but by radiographing a set of diamond-turned, 105 µm-thick Pb test objects and supported by simulations using the 3D Monte Carlo code GEANT4, these geometry-dependent gradients across the field of view are quantified and mitigation strategies are assessed. In addition to significantly enhancing the modulation transfer function compared to the existing system, image stacking from multiple layers of image plate is shown to almost double the signal to noise ratio that will reduce uncertainties in future dynamic strength experiments.
Alkali antimonide photocathodes have wide applications in free-electron lasers and electron cooling. The short lifetime of alkali antimonide photocathodes necessitates frequent replacement of the photocathodes during a beam operation. Furthermore, exposure to mediocre vacuum causes loss of photocathode quantum efficiency due to the chemical reaction with residual gas molecules. Theoretical analyses have shown that covering an alkali antimonide photocathode with a monolayer graphene or hexagonal boron nitride protects it in a coarse vacuum environment due to the inhibition of chemical reactions with residual gas molecules. Alkali antimonide photocathodes require an ultra-high vacuum environment, and depositing a monolayer 2D material on it poses a serious challenge. In the present work, we have incorporated a novel method known as intercalation, in which alkali atoms pass through the defects of a graphene thin film to create a photocathode material underneath. Initially, Sb was deposited on a Si substrate, and a monolayer graphene was transferred on top of the Sb film. Heat cleaning around 550–600 °C effectively removed the Sb oxides, leaving metallic Sb underneath the graphene layer. Depositing Cs on top of a monolayer graphene enabled the intercalation process. Atomic force microscopy, Raman spectroscopy, x-ray photoelectron spectroscopy, low energy electron microscopy, and x-ray diffraction measurements were performed to evaluate photocathode formation underneath the monolayer graphene. Our analysis shows that Cs penetrated the graphene and reacted with Sb and formed Cs 3 Sb.
Here we present the development of an experimental platform that can collect four frames of x-ray diffraction data along a single line of sight during laser-driven, dynamic-compression experiments at the National Ignition Facility. The platform is comprised of a diagnostic imager built around ultrafast sensors with a 2-ns integration time, a custom target assembly that serves also to shield the imager, and a 10-ns duration, quasi-monochromatic x-ray source produced by laser-generated plasma. We demonstrate the performance with diffraction data for Pb ramp compressed to 150 GPa and illuminated by a Ge x-ray source that produces ∼7 × 10 11 , 10.25-keV photons/ns at the 400 μm diameter sample.
High-energy-density laser facilities and advances in dynamic compression techniques have expanded access to material states in the Terapascal regime relevant to inertial confinement fusion, planetary science, and geophysics. However, experimentally determining the material temperature in these extreme conditions has remained a difficult challenge. Extended X-ray Absorption Fine Structure (EXAFS), referring to the modulations in x-ray absorption above an absorption edge from photoelectrons’ interactions with neighboring atoms, has proven to be a versatile and robust technique for probing material temperature and density for mid-to-high Z elements under dynamic compression. The current platform at the National Ignition Facility has developed six configurations for EXAFS measurements between 7 and 18 keV for different absorption edges (Fe K, Co K, Cu K, Ta L3, Pb L3, and Zr K) using a curved-crystal spectrometer and a bright, continuum foil x-ray source. Here, in this work, we describe the platform geometry, x-ray source performance, spectrometer resolution and throughput, design considerations, and data in ambient and dynamic-compression conditions.
The evolution of molecular dynamics (MD) simulations has been intimately linked to that of computing hardware. For decades following the creation of MD, simulations have improved with computing power along the three principal dimensions of accuracy, atom count (spatial scale), and duration (temporal scale). Since the mid-2000s, computer platforms have, however, failed to provide strong scaling for MD, as scale-out central processing unit (CPU) and graphics processing unit (GPU) platforms that provide substantial increases to spatial scale do not lead to proportional increases in temporal scale. Important scientific problems therefore remained inaccessible to direct simulation, prompting the development of increasingly sophisticated algorithms that present significant complexity, accuracy, and efficiency challenges. While bespoke MD-only hardware solutions have provided a path to longer timescales for specific physical systems, their impact on the broader community has been mitigated by their limited adaptability to new methods and potentials. In this work, we show that a novel computing architecture, the Cerebras wafer scale engine, completely alters the scaling path by delivering unprecedentedly high simulation rates up to 1.144 M steps/s for 200 000 atoms whose interactions are described by an embedded atom method potential. This enables direct simulations of the evolution of materials using general-purpose programmable hardware over millisecond timescales, dramatically increasing the space of direct MD simulations that can be carried out. In this paper, we provide an overview of advances in MD over the last 60 years and present our recent result in the context of historical MD performance trends.
We report protein fold prediction using deep-learning artificial intelligence (AI) has transformed the field of protein structure prediction. By combining physical and geometric constraints—and especially patterns extracted from the Protein Data Bank —these machine learning algorithms can predict protein structures at or near atomic resolution and do so in seconds. Today, these computational methods have now solved more than 200 million protein structures, which are accessible from the AlphaFold Protein Structure Database. This accomplishment seems all the more remarkable because few thought it possible or saw it coming. Deservedly, deep-learning AI was named Science magazine’s 2021 “breakthrough of the year”. Clearly, deep-learning AI represents a major advance in protein fold prediction.
Thermally induced ripples are intrinsic features of nanometer-thick films, atomically thin materials, and cell membranes, significantly affecting their elastic properties. Despite decades of theoretical studies on the mechanics of suspended thermalized sheets, controversy still exists over the impact of these ripples, with conflicting predictions about whether elasticity is scale-dependent or scale-independent. Experimental progress has been hindered so far by the inability to have a platform capable of fully isolating and characterizing the effects of ripples. This knowledge gap limits the fundamental understanding of thin materials and their practical applications. Here, we show that thermal-like static ripples shape thin films into a class of metamaterials with scale-dependent, customizable elasticity. Utilizing a scalable semiconductor manufacturing process, we engineered nanometer-thick films with precisely controlled frozen random ripples, resembling snapshots of thermally fluctuating membranes. Resonant frequency measurements of rippled cantilevers reveal that random ripples effectively renormalize and enhance the average bending rigidity and sample-to-sample variations in a scale-dependent manner, consistent with recent theoretical estimations. The predictive power of the theoretical model, combined with the scalability of the fabrication process, was further exploited to create kirigami architectures with tailored bending rigidity and mechanical metamaterials with delayed buckling instability.