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An all-solid-state, high-resolution and frequency-calibrated THz spectrometer based on optical heterodyne in low-temperature-grown (LTG) GaAs been constructed.
Bulk density is a fundamental soil property that is difficult to determine for gravelly to extremely gravelly soils because results vary significantly with sample volume. For such coarse soils, the representative volume (for whole-soil bulk density) should be large, but guidelines for selecting an appropriate sample volume do not exist.
An alternative to the conventional approach to synthesis of polyimides involves the use of single monomers that are amenable to photopolymerization. Heretofore, the synthesis of polyimides has involved multiple-monomer formulations and heating to temperatures that often exceed 250 C. The present alternative approach enables synthesis under relatively mild conditions that can include room temperature. The main disadvantages of the conventional approach are the following: Elevated production temperatures can lead to high production costs and can impart thermal stresses to the final products. If the proportions of the multiple monomeric ingredients in a given batch are not exactly correct, the molecular weight and other physical properties of the final material could be reduced from their optimum or desired values. To be useful in the alternative approach, a monomer must have a molecular structure tailored to exploit Diels-Alder trapping of a photochemically generated ortho-quinodimethane. (In a Diels-Alder reaction, a diene combines with a dienophile to form molecules that contain six-membered rings.) In particular, a suitable monomer (see figure) contains ortho-methylbenzophenone connected to a dienophile (in this case, a maleimide) through a generic spacer group. Irradiation with ultraviolet light gives rise to a photochemical intermediate the aforementioned ortho-quinodimethane from the ortho-methylbenzophenone. This group may react with the dienophile on another such monomer molecule to produce an oligomer that, in turn may react in a stepgrowth manner to produce a polyimide. This approach offers several advantages in addition to those mentioned above: The monomer can be stored for a long time because it remains unreactive until exposed to light. Because the monomer is the only active starting ingredient, there is no need for mixing, no concern for ensuring correct proportions of monomers, and the purity of the final product material is inherently high. The use of solvents is optional: The synthesis can be performed using the neat monomer or the monomer mixed with one or more solvent(s) in dilute or concentrated solution. The solubility of the monomer and the physical and chemical properties of the final polymer can be tailored through selection of the spacer group.
A computer program called "Rational Sequence" generates Universal Modeling Language (UML) sequence diagrams of a target Java program running on a Java virtual machine (JVM). Rational Sequence thereby performs a reverse engineering function that aids in the design documentation of the target Java program. Whereas previously, the construction of sequence diagrams was a tedious manual process, Rational Sequence generates UML sequence diagrams automatically from the running Java code.
The Rational Sequence computer program described elsewhere includes a subprogram that utilizes the capability for aspect-oriented programming when that capability is present. This subprogram is denoted the Rational Sequence (AspectJ) component because it uses AspectJ, which is an extension of the Java programming language that introduces aspect-oriented programming techniques into the language
SMOS [1] and Aquarius [2] are ESA and NASA missions, respectively, to make L-band measurements from the Low Earth Orbit. SMOS makes passive measurements whereas Aquarius measures both passive and active. SMOS was launched in November 2009 and Aquarius in June 2011.The scientific objectives of the missions are overlapping: both missions aim at mapping the global Sea Surface Salinity (SSS). Additionally, SMOS mission produces soil moisture product (however, Aquarius data will eventually be used for retrieving soil moisture too). The consistency of the brightness temperature observations made by the two instruments is essential for long-term studies of SSS and soil moisture. For resolving the consistency, the calibration of the instruments is the key. The basis of the SMOS brightness temperature level is the measurements performed with the so-called zero-baselines [3]; SMOS employs an interferometric measurement technique which forms a brightness temperature image from several baselines constructed by combination of multiple receivers in an array; zero-length baseline defines the overall brightness temperature level. The basis of the Aquarius brightness temperature level is resolved from the brightness temperature simulator combined with ancillary data such as antenna patterns and environmental models [4]. Consistency between the SMOS zero-baseline measurements and the simulator output would provide a robust basis for establishing the overall comparability of the missions.
In this work, we report continuous carbon nanotube fibers dry-drawn directly from water-assisted CVD grown forests with millimeter scale length. As-drawn nanotube fibers exist as aerogel and can be transformed into more compact fibers through twisting or densification with a volatile liquid. Nanotube fibers are characterized by scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), Raman microscopy and wide-angle X-ray diffraction (WAXD). Mechanical behavior and electrical conductivity of the post-treated nanotube fibers are investigated.
A titanium silicate variant named UPRM-5 was prepared using tetraethylammonium hydroxide as a structure-directing agent (SDA). Successful detemplation was achieved via ion exchange with NH4Cl. Effective functionalization was obtained after ion exchanging the detemplated material using SrCl2 and BaCl2. Adsorption of CO2 at 25 deg C in Sr(-) and Ba-UPRM-5 materials activated at different temperatures. For low partial pressures, the observed CO2 adsorption capacities increased as follows: NH4-UPRM-5 less than Sr-UPRM-5 less than Ba-UPRM-5. Both the Sr(-) and Ba-UPRM-5 materials exhibited outstanding selectivity for CO2 over CH4, N2 and O2.
We report on the material characterization of carbon nanofibers (CNFs) which are assembled into a three-dimensional (3D) configuration for making new nanoelectromechanical systems (NEMS). High-resolution scanning electron microscopy (SEM) and x-ray electron dispersive spectroscopy (XEDS) are employed to decipher the morphology and chemical compositions of the CNFs at various locations along individual CNFs grown on silicon (Si) and refractory nitride (NbTiN) substrates, respectively. The measured characteristics suggest interesting properties of the CNF bodies and their capping catalyst nanoparticles, and growth mechanisms on the two substrates. Laser irradiation on the CNFs seems to cause thermal oxidation and melting of catalyst nanoparticles. The structural morphology and chemical compositions of the CNFs revealed in this study should aid in the applications of the CNFs to nanoelectronics and NEMS.
We present a laboratory reproduction of hypervelocity impacts of a carbon containing meteorite on a mineral substance representative of planetary surfaces. The physical conditions of the resulting impact plasma torch provide favorable conditions for abiogenic synthesis of protein amino acids: We identified glycine and alanine, and in smaller quantities serine, in the produced material. Moreover, we observe breaking of alanine mirror symmetry with L excess, which coincides with the bioorganic world. Therefore the selection of L-amino acids for the formation of proteins for living matter could have been the result from plasma processes occurring during the impact meteorites on the surface. This indicates that the plasma torch from meteorite impacts could play an important role in the formation of biomolecular homochirality. Thus, meteorite impacts possibly were the initial stage of this process and promoted conditions for the emergence of a living matter.
Concentrations of atmospheric trace species in the United States have changed dramatically over the past several decades in response to pollution control strategies, shifts in domestic energy policy and economics, and economic development (and resulting emission changes) elsewhere in the world. Reliable projections of the future atmosphere require models to not only accurately describe current atmospheric concentrations, but to do so by representing chemical, physical and biological processes with conceptual and quantitative fidelity. Only through incorporation of the processes controlling emissions and chemical mechanisms that represent the key transformations among reactive molecules can models reliably project the impacts of future policy, energy and climate scenarios. Efforts to properly identify and implement the fundamental and controlling mechanisms in atmospheric models benefit from intensive observation periods, during which collocated measurements of diverse, speciated chemicals in both the gas and condensed phases are obtained. The Southeast Atmosphere Studies (SAS, including SENEX, SOAS, NOMADSS and SEAC4RS) conducted during the summer of 2013 provided an unprecedented opportunity for the atmospheric modeling community to come together to evaluate, diagnose and improve the representation of fundamental climate and air quality processes in models of varying temporal and spatial scales. This paper is aimed at discussing progress in evaluating, diagnosing and improving air quality and climate modeling using comparisons to SAS observations as a guide to thinking about improvements to mechanisms and parameterizations in models. The effort focused primarily on model representation of fundamental atmospheric processes that are essential to the formation of ozone, secondary organic aerosol (SOA) and other trace species in the troposphere, with the ultimate goal of understanding the radiative impacts of these species in the southeast and elsewhere. Here we address questions surrounding four key themes: gas-phase chemistry, aerosol chemistry, regional climate and chemistry interactions, and natural and anthropogenic emissions. We expect this review to serve as a guidance for future modeling efforts.
New launch vehicles have historically had significantly higher failure rates in early flights than what has been predicted using Probabilistic Risk Assessment - PRA. This is because PRAs typically model a mature vehicle where a significant portion of the early failure probability contributors have been eliminated due to testing and improvements after actual field operation. To capture a more accurate early flight failure probability estimate, this paper develops a method that estimates ascent failure probability starting with the first flight based on historical launch vehicle records. With new launch vehicles being developed, such as the Space Launch System - SLS, a PRA model must be extended to cover early flight failure probability contributions that are either not covered in the mature-vehicle PRA or are underestimated. These failure probability contributions include design errors, quality control deficiencies, installation errors, and environmental impacts. There are also failure dependencies due to systemic errors that still exist due to limited entire-system testing.
New launch vehicles have historically had significantly higher failure probabilities in early flights than what has been predicted using Probabilistic Risk Assessment. Work on a new methodology originally started with ARES I-X and the Common Standards Working Group (CSWG) for range safety applications. CSWG consists of the Federal Aviation Administration (FAA), Air Force, and NASA. Historical launch vehicle data was viewed as the best predictor of success/failure for launches of new vehicles. A launch vehicle database was developed that includes all launches from 1980-2017 (both US and foreign). Entries to the database include: Vehicle by model type; Launch dates; Failure description; Failure Result (Loss Of Vehicle (LOV)/Loss Of Mission (LOM); Failure cause (when available); Vehicle designs (stages/engines/etc.)
A flexible method to describe and generate aircraft trajectories called GenProf was developed for the Center TRACON Automation System (CTAS) software research platform. Generally CTAS is used to prototype new air traffic management decision support tools and concepts. Beyond this purpose, the GenProf methodology has enabled a variety of research and validation tasks to be performed. This paper briefly describes the methodology and details these applications.
In the origins of life field understanding the abiotic polymerization of simple organic monomers (e.g., amino acids) into larger biomolecules (e.g., oligopeptides), remains a seminal challenge. Recently, preliminary observations showed a limited set of peptides formed in the presence of the plausible prebiotic phosphorylating agent, diamidophosphate (DAP), highlighting the need for an analytical tool to critically evaluate the ability of DAP to induce oligomerization of simple organics under aqueous conditions. However, performing accurate and precise, targeted analyses of short oligopeptides remains a distinct challenge in the analytical chemistry field. Here, we developed a new technique to detect and quantitate amino acids and their homopeptides in a single run using ultraperformance liquid chromatography-fluorescence detection/time of flight mass spectrometry. Over an 8-minute retention time window, 18 target analytes were identified and quantitated, 16 of which were chromatographically separated at, or near baseline resolution. Compound identity was confirmed by accurate mass analysis using a 10 ppm mass tolerance window. This method featured limits of detection < 5 nM (< 1 fmol on column) and limits of quantitation (LOQs) <15 nM (< 3 fmol on column). The LODs and LOQs were upwards of ∼28x and ∼788x lower, respectively, than previous methods for the same analytes, highlighting the quantifiable advantages of this new method. Both detectors provided good quantitative linearity (R(exp 2) > 0.985) for all analytes spanning concentration ranges ∼3 - 4 orders of magnitude. We performed a series of laboratory experiments to investigate DAP-mediated oligomerization of amino acids and peptides and analyzed experimental products with the new method. DAP readily polymerized amino acids and peptides under a range of simulated environmental conditions. This research underscores the potential of DAP to have generated oligopeptides on the primordial Earth, enhancing prebiotic chemical diversity and complexity at or near the origin of life.
The antenna pattern and observation geometry of the SMOS zero-baseline radiometer, which is used as a reference for the SMOS brightness temperature calibration, was applied to Aquarius simulator, which is used as a reference for the Aquarius brightness temperature calibration. In the preliminary analysis, simulations carried out over a three month period show remarkable agreement between the measurements and simulations. This fundamental agreement would indicate that the brightness temperature products of the two missions should be well correlated. Some discrepancies were also found, cause of which seems to be consistent with findings of other studies and can be corrected for.
Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state-of-the-art knowledge in an internally self-consistent modelling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain-specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low-emissions SSP1-1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850-1900, using an observationally-based historical warming estimate of 0.8°C between 1850-1900 and 1995-2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above pre-industrial in the long-term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.
Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha−1 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation.