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A process for depositing elements by irradiating liquids is reported. Ultra pure elements are precipitated from aqueous solutions or suspensions of compounds. A solution of a salt of a metal to be prepared is irradiated, and the insoluble reaction product settles out. Some chemical compounds may also be prepared in this manner.
A method of using an electronic nose to detect an airborne mixture of known chemical compounds and measure the temporally varying concentrations of the individual compounds is undergoing development. In a typical intended application, the method would be used to monitor the air in an inhabited space (e.g., the interior of a building) for the release of solvents, toxic fumes, and other compounds that are regarded as contaminants. At the present state of development, the method affords a capability for identifying and quantitating one or two compounds that are members of a set of some number (typically of the order of a dozen) known compounds. In principle, the method could be extended to enable monitoring of more than two compounds. An electronic nose consists of an array of sensors, typically made from polymer carbon composites, the electrical resistances of which change upon exposure to a variety of chemicals. By design, each sensor is unique in its responses to these chemicals: some or all of the sensitivities of a given sensor to the various vapors differ from the corresponding sensitivities of other sensors. In general, the responses of the sensors are nonlinear functions of the concentrations of the chemicals. Hence, mathematically, the monitoring problem is to solve the set of time-dependent nonlinear equations for the sensor responses to obtain the time dependent concentrations of individual compounds. In the present developmental method, successive approximations of the solution are generated by a learning algorithm based on independent-component analysis (ICA) an established information theoretic approach for transforming a vector of observed interdependent signals into a set of signals that are as nearly statistically independent as possible.
Engine oil-derived ash particles emitted from internal combustion (IC) engines are unwanted by-products, after oil is involved in in-cylinder combustion process. Since they typically come out together with particulate emissions, no detail has been reported about their early-stage particles other than agglomerated particles loaded on aftertreatment catalysts and filters. To better understand ash formation process during the combustion process, here differently formulated engine oils were dosed into a fuel system of a gasoline direct injection (GDI) engine that produces low soot mass emissions at normal operating conditions to increase the chances to find stand-alone ash particles separated from soot aggregates in the sub-20-nm size range. In addition to them, ash/soot aggregates in the larger size range were examined using scanning transmission electron microscopy (STEM)-X-ray electron dispersive spectroscopy (XEDS) to present elemental information at different sizes of particles from various oil formulations. The STEM-XEDS results showed that regardless of formulated oil type and particle size, Ca, P and C were always contained, while Zn was occasionally found on relatively large particles, suggesting that these elements get together from an early stage of particle formation. The S, Ca and P K-edge X-ray absorption near edge structure (XANES) analyses were performed for bulk soot containing raw ash. The linear combination approach & cross-checking among XANES results proposed that Ca 5 (OH)(PO 4 ) 2 , Ca 3 (PO 4 ) 2 and Zn 3 (PO 4 ) 2 are potentially major chemical compounds in raw ash particles, when combined with the STEM-XEDS results. Despite many reports that CaSO 4 is a major ash chemical when ash found in DPF/GFP systems was examined, it was observed to be rarely present in raw ashes using the S K-edge XANES analysis, suggesting ash transformation.
In situ exploration of the solar system to identify its early chemistry as preserved in icy bodies and to look for compelling evidence of astrobiology will require new technology for chemical analysis. Chemical measurements in space flight environments highlight the need for a high level of positive identification of chemical compounds, since re-measurement by alternative techniques for confirmation will not be feasible. It also may not be possible to anticipate all chemical species that are observed, and important species may be present only at trace levels where they can be masked by complex chemical backgrounds. Up to now, the only techniques providing independent sample identification of GC separated components across a wide range of chemical species have been Mass Spectrometry (MS) and Ion Mobility Spectrometry (IMS). We describe here the development of a versatile and robust miniature GC detector based on Penning Ionization Electron Spectroscopy (PIES), for use with miniature GC systems being developed for planetary missions. PIES identifies the sample molecule through spectra related to its ionization potential. The combination of miniature GC technology with the primary identification capabilities of PIES provides an analytical approach ideal for planetary analyses.
A major observational challenge in cometary science is to quantify the extent to which chemical compounds can be linked to either interstellar or nebular chemistry. Recently, there have been complimentary observations from multiple facilities to try to unravel the chemical complexity of comets and their origins. Incorporating results from various techniques can gain further insight into the abundances, production rates, distributions, and formation mechanisms of molecules in these objects [I]. Such studies have provided great detail towards molecules with a-typical chemistries, such as H2CO [2]. We report multiwavelength spectral observations of comets from two dynamical families including the JFC 103P/Hartley 2 and a long period comet C/2009 PI (Garradd) with the Arizona Radio Observatory's SMT and 12-m telescopes, as well as the NRAO Greenbank telescope, and the James Clerk Maxwell Telescope. Multiple parent volatiles (e.g. HCN, CH30H, CO) as well as daughter products (e.g, CS and 01-1) have been detected in these objects. We will present a comparison of molecular abundances in these comets to those observed in others, supporting a long-term effort of building a comet taxonomy based on composition. Previous work has revealed a range of abundances of parent species (from "organics-poor" to "organics-rich") with respect to water among comets [3,4,5], however the statistics are not well constrained.
A dire need for real-time detection of toxic chemical compounds exists in both civilian and military spheres. In this paper, we demonstrate that inexpensive, commercially available Fe 2 O 3 nanoparticles are capable of selective sensing of chemical warfare agents (CWAs) using frequency-dependent impedance spectroscopy, with additional potential as an orthogonal magnetic sensor. X-ray magnetic circular dichroism analysis shows that Fe 2 O 3 nanoparticles possess moderately lowered moment upon exposure to 2-chloroethyl ethyl sulfide (2-CEES) and diisopropyl methylphosphonate (DIMP) and significantly lowered moment upon exposure to dimethyl methylphosphonate (DMMP) and dimethyl chlorophosphate (DMCP). Associated X-ray absorption spectra confirm a redox reaction in the Fe 2 O 3 nanoparticles due to CWA structural analog exposure, with differentiable energy-dependent features that suggest selective sensing is possible, given the correct method. Impedance spectroscopy performed on samples dosed with DMMP, DMCP, and tabun (GA, chemical warfare nerve agent) showed strong, differentiable, frequency-dependent responses. The frequency profiles provide unique “shift fingerprints” with which high specificity can be determined, even amongst similar analytes. The results suggest that frequency-dependent impedance fingerprinting using commercially available Fe 2 O 3 nanoparticles as a sensor material is a feasible route to selective detection.
The nature and mechanisms producing the chromophore agents that provide color to the upper clouds and hazes of the atmospheres of the giant planets are largely unknown. In recent times, the changes in red coloration that have occurred in large- and medium-scale Jovian anticyclones have been particularly interesting. In late June and early July 2008, a particularly color-intense red tropical oval interacted with the Great Red Spot (GRS) leading to the destruction of the red tropical oval and cloud dispersion. We present a detailed study of the tropical vortices, usually white but sometimes red, and a characterization of their color spectral signatures and dynamics. From the spectral reflectivity in methane bands we study their vertical cloud structure compared to that of the GRS and BA. Using two spectral indices we found a near-correlation between anticyclones cloud top altitudes and red color. We present detailed observations of the interaction of the red oval with the GRS and model simulations of the phenomena that allow us to constrain the relative vertical extent of the vortices. We conclude that the vertical cloud structure, vertical extent and dynamics of Jovian anticyclones are not the causes of their coloration. We propose that the red chromophore forms when background material (a compound or particles) is entrained by the vortex, transforming into red once inside the vortex due to internal conditions, exposure to ultraviolet radiation or to the mixing of two chemical compounds that react inside the vortex, confined by a potential vorticity ring barrier.
Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for pure components in liquid, gas and super critical phases. The ANNs were tested on an experimental database of 6625 self-diffusion constants for 118 different chemical compounds. The presence of multiple phases results in a heavy skew in the distribution of diffusion constants and multiple approaches were used to address this challenge. First, an ANN was developed with the raw diffusion values to assess what the main drawbacks of this direct method were. The first approach for improving the predictions involved taking the log 10 of diffusion to provide a more uniform distribution and reduce the range of target output values used to develop the ANN. The second approach involved developing individual ANNs for each phase using the raw diffusion values. Results show that the log transformation leads to a model with the best self-diffusion constant predictions and an overall average absolute deviation (AAD) of 6.56%. The resultant ANN is a generalized model that can be used to predict diffusion across all three phases and over a diverse group of compounds. The importance of each input feature was ranked using a feature addition method revealing that the density of the compound has the largest impact on the ANN prediction of self-diffusion constants in pure compounds.
Experiments performed on the International Space Station (ISS) frequently require the experimental organisms to be preserved until they can be returned to earth for analysis in the appropriate laboratory facility. The Kennedy Fixation Tube (KFT) was developed to allow astronauts to apply fixative, chemical compounds that are often toxic, to biological samples without the use of a glovebox while maintaining three levels of containment (Fig. 1). KFTs have been used over 200 times on-orbit with no leaks of chemical fixative. The KFT is composed of the following elements: a polycarbonate main tube where the fixative is loaded preflight, the sample tube where the plant or other biological specimens is placed during operations, the expansion plug, actuator, and base plug that provides fixative containment (Fig. 2). The main tube is pre-filled with 25 mL of fixative solution prior to flight. When actuated, the specimen contained within the sample tube is immersed with approximately 22 mL (+/- 2 mL) of the fixative solution. The KFT has been demonstrated to maintain its containment at ambient temperatures, 4degC refrigeration and -100 C freezing conditions.
A fairly fool-proof method to ensure that the compounds isolated from meteorites are truly part of the meteorites and not an artifact introduced by exposure to the terrestrial environment, storage, or handling is presented. The stable carbon and hydrogen isotope ratios in several of the chemical compounds extracted from the Murchison meteorite were measured. The results obtained by studying the amino acids in this meteorite gave very unusual hydrogen and carbon isotope ratios. The technique was extended to the different classes of hydrocarbons and the hydrocarbons were isolated using a variety of separation techniques. The results and methods used in this investigation are described in this two page paper.
Reagentless ultraviolet absorption spectrometry (UVAS) and Liquid Atomic Emission Spectrometry (LAES) represent new forms of spectrometry with extensive potential in both space and commercial applications. Originally developed under KSC sponsorship for monitoring nutrient solutions for the Controlled Ecological Life Support System (CELSS), both UVAS and LAES have extensive analytical capabilities for both organic and inorganic chemical compounds. Both forms of instrumentation involve the use of remote fiber optic probes and real-time measurements for on-line process monitoring. Commercial applications exist primarily in environmental analysis and for process control in the chemical, pulp and paper, food processing, metal plating, and water/wastewater treatment industries.
Semilocal (SL) density functional approximations (DFAs) are widely applied but have limitations due to their inability to incorporate long-range van der Waals (vdW) interaction. Non-local functionals (vdW-DF, VV10, and rVV10) or empirical methods (DFT+D, DFT+vdW, and DFT+MBD) are used with SL-DFAs to account for such missing interaction. The physisorption of a molecule on the surface of the coinage metals (Cu, Ag, and Au) is a typical example of systems where vdW interaction is significant. However, it is difficult to find a general method that reasonably describes both adsorption energy and geometry of even the simple prototypes of cyclic and heterocyclic aromatic molecules such as benzene (C 6 H 6 ) and thiophene (C 4 H 4 S), respectively, with reasonable accuracy. Here, we present an alternative scheme based on Zaremba–Kohn theory, called DFT+vdW-dZK. We show that unlike other popular methods, DFT+vdW-dZK and particularly SCAN+vdW-dZK give an accurate description of the physisorption of a rare-gas atom (xenon) and two small albeit diverse prototype organic molecules on the (111) surfaces of the coinage metals.
The present research deals with thermal degradation of polyurethane foam (PUF) during flight test. Model of thermal decomposition was developed that accounts for polyurethane kinetics parameters extracted from thermogravimetric analyses and radial heat losses to the surrounding environment. The model predicts mass loss of foam, the temperature and kinetic of release of the exhaust gases and char as function of heat and radiation loads. When PUF is heated, urethane bond break into polyol and isocyanate. In the first stage, isocyanate pyrolyses and oxidizes. As a result, the thermo-char and oil droplets (yellow smoke) are released. In the second decomposition stage, pyrolysis and oxidization of liquid polyol occur. Next, the kinetics of chemical compound release and the information about the reactions occurring in the base area are coupled to the CFD simulations of the base flow in a single first stage motor vertically stacked vehicle configuration. The CFD simulations are performed to estimate the contribution of the hot out-gassing, chemical reactions, and char oxidation to the temperature rise of the base flow. The results of simulations are compared with the flight test data.
Abstract The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure–Activity Relationship (SAR)-based chemical classification. Undersampling and oversampling are representative techniques for handling such an imbalance challenge. However, removing inactive chemical compound instances from the majority class using an undersampling technique can result in information loss, whereas increasing active toxicant instances in the minority class by interpolation tends to introduce artificial minority instances that often cross into the majority class space, giving rise to class overlapping and a higher false prediction rate. In this study, in order to improve the prediction accuracy of imbalanced learning, we employed SMOTEENN, a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms, to oversample the minority class by creating synthetic samples, followed by cleaning the mislabeled instances. We chose the highly imbalanced Tox21 dataset, which consisted of 12 in vitro bioassays for > 10,000 chemicals that were distributed unevenly between binary classes. With Random Forest (RF) as the base classifier and bagging as the ensemble strategy, we applied four hybrid learning methods, i.e., RF without imbalance handling (RF), RF with Random Undersampling (RUS), RF with SMOTE (SMO), and RF with SMOTEENN (SMN). The performance of the four learning methods was compared using nine evaluation metrics, among which F 1 score, Matthews correlation coefficient and Brier score provided a more consistent assessment of the overall performance across the 12 datasets. The Friedman’s aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that SMN significantly outperformed the other three methods. We also found that a strong negative correlation existed between the prediction accuracy and the imbalance ratio (IR), which is defined as the number of inactive compounds divided by the number of active compounds. SMN became less effective when IR exceeded a certain threshold (e.g., > 28). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. This work demonstrates that the performance of SAR-based, imbalanced chemical toxicity classification can be significantly improved through the use of data rebalancing.
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Exploring the behavior of hydrocarbon under pressure is important for understanding its role in planetary sciences and also for exploring novel organic chemistry. In this study, we explored the high-pressure behavior of a linear-chain hydrocarbon, tricosane (C 23 H 48 ), using Raman spectroscopy. We compressed tricosane up to 23 GPa and did not find any evidence for pressure-induced amorphization within the conditions explored in this study. Upon compression, we observe new modes in the low energy region 100–300 cm -1 . In order to understand the appearance of these new modes at high pressures, we used complementary ab initio calculations and explored the effect of chain configurations (linear and bent) on the predicted Raman spectra. We find that these new modes observed at higher pressures are better explained by bent configuration of tricosane chains. Thus, based on high-pressure Raman spectra, it is very likely that a linear chain of tricosane is bent under pressure, i.e., it undergoes a pressure-induced trans-gauche transformation. Furthermore, it is likely that such bent regions (i.e., kinks) will act as sites along which large chain hydrocarbons could dissociate into smaller chain lengths at extreme conditions relevant to the interiors of Jovian planets.
A low noise (1(sigma) rms approx. 3 mK) 1. nun spectral survey (214.5-285.5 GHz) of the oxygen-rich supergiant VY Canis Majoris and the carbon-rich asymptotic giant branch star IRC +10216 has been conducted using the Arizona Radio Observatory's 10 m Submillimeter Telescope. Here the complete data set is presented. This study, carried out with a new ALMA-type receiver, marks the first continuous band scan of an O-rich circumstellar envelope, and the most sensitive survey to date of IRC +10216. In VY CMa, 130 distinct molecular lines were detected, 14 of which cannot be identified; in IRC +10216, 717 lines were observed, with 126 features remaining unidentified. In the 1 mm bands of VY CMa and IRC +10216, emission is present from 18 and 32 different chemical compounds, respectively, with 10 species common to both sources. Many narrow emission lines were observed in both circumstellar shells, arising from vibrationally excited molecules and from refractory-containing species. Line profiles in VY CMa also exhibit a variety of different shapes, caused by the complex, asymmetric outflow of this object. The survey highlights the fact that C-rich and O-rich circumstellar envelopes are chemically interesting, and both are sources of new interstellar molecules. The high number of unidentified lines and the unreliable, rest frequencies for known species such as NaCN indicate the need for additional laboratory spectroscopy studies.
This website contains the dataset that was used for writing the manuscript "HT Model: Using the Molecular Transformer for predicting hydrotreating reactions" (PNNL-SA-186589) The dataset includes a collection of hydrotreating reactions compiled from 41 peer-reviewed literature sources. These sources contain experimental data related to hydrotreating reactions. These reactions involve the reaction of chemical compounds with hydrogen gas in the presence of a catalyst to remove heteroatoms or to convert specific functional groups. The dataset contains reactions both with and without reaction conditions. Reaction conditions refer to the specific parameters under which the reaction takes place, such as temperature and pressure. For each reaction, the dataset includes both SMILES and SELFIES representations. SMILES (Simplified Molecular Input Line Entry System) and SELFIES (SELF-referencIng Embedded Strings) are two popular notations used to represent chemical structures in a compact and standardized format. The dataset was created with the aim of training a predictive model, specifically using the Molecular Transformer architecture.