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Porphyrins and amino acids chemical bonding under geochemically plausible conditions, considering diagenesis of biogenic compounds and life processes prebiotic chemical evolution
In connection with the relatively poor insulating characteristics of the native oxides of most III-V compound semiconductors, there has been interest in the development of deposited dielectric layers for surface passivation and MOS device fabrication on III-V compound semiconductor substrates. The chemical system of SiO2 deposited on single-crystal InSb substrates has been proposed for the fabrication of IR CCD. The considered investigation is concerned with a detailed examination of the chemical nature of the interactions between deposited and native oxides using X-ray photoelectron spectroscopy (XPS) in conjunction with a chemical-etching technique for depth profiling. The compositional structure of the interfaces obtained in the SiO2/native oxide/InSb system is derived on the basis of an intensity analysis of XPS spectra. Thermal oxidation of the InSb substrate is found to result in an In-rich native oxide and excess elemental Sb at the native oxide/substrate interface.
Polyarimide physical properties and chemical structure correlations
Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.
Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.
Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O (sup 1) D (Deuterium) to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.
The relationship between emission properties and sample composition is studied for lanthanum-boron compounds. Specifically, the La-B system is considered between 1400 and 2100 K and between LaB(4.24) and LaB(29.2) to determine the phase relationship, chemical activity of the compounds, vapor composition, and vaporization rate. The results indicate that: (1) a blue-colored phase near LaB(9) exists between a purple-colored LaB(6) and elemental boron, (2) vaporization is sufficiently more rapid than diffusion so that great compositional differences exist between the surface and the interior, (3) an activation energy lowers the boron vaporization rate from LaB(6), and (4) a steady-state surface composition between LaB(6.04) and LaB(6.07) exists for freely vaporizing materials as a function of interior composition, purity, and temperature. It is noted that the ultimate life of a thermionic diode is governed by electrode vaporization rate whereas efficiency is governed by the electrode work function.
Rejection characteristics of nitrogen compounds were examined for reverse osmosis, nanofiltration, and low-pressure reverse osmosis membranes. The rejection of nitrogen compounds is explained by integrating experimental results with calculations using the extended Nernst-Planck model coupled with a steric hindrance model. The molecular weight and chemical structure of nitrogen compounds appear to be less important in determining rejection than electrostatic properties. The rejection is greatest when the Donnan potential exceeds 0.05 V or when the ratio of the solute radius to the pore radius is greater than 0.8. The transport of solute in the pore is dominated by diffusion, although convective transport is significant for organic nitrogen compounds. Electromigration contributes negligibly to the overall solute transport in the membrane. Urea, a small organic compound, has lower rejection than ionic compounds such as ammonium, nitrate, and nitrite, indicating the critical role of electrostatic interaction in rejection. This suggests that better treatment efficiency for organic nitrogen compounds can be obtained after ammonification of urea.
A rapid thermal annealing (RTA) process in pure N2 or pure H2 was developed for ion-implanted and encapsulated indium phosphide compound semiconductors, and the chemical nature at the silicon nitride-InP interface before and after RTA was examined using XPS. Results obtained from SIMS on the atomic concentration profiles of the implanted silicon in InP before and after RTA are presented, together with electrical characteristics of the annealed implants. Using the RTA process developed, InP metal-insulator semiconductor FETs (MISFETS) were fabricated. The MISFETS prepared had threshold voltages of +1 V, transconductance of 27 mS/mm, peak channel mobility of 1200 sq cm/V per sec, and drain current drift of only 7 percent.
Chemical sterilization of microorganisms in potting compounds
The results of a literature survey of the field of bicyclic and cage compounds were presented, with the objective of identifying those types of compounds with unusual physical and chemical stability, and determining what practical applications have been found for these compounds. Major applications have been as polymers, polymer additives, medicinals, and pesticides. Lesser applications have included fuels, fuel additives, lubricants, lubricant additives, and perfumes. Several areas where further work might be useful were also outlined; these are primarily in the areas of polymers, polymer additives, medicinals, and synthetic lubricants.
The usefulness of C-13 NMR spectroscopy and chemical shift for the study of charge transfer in graphite intercalation compounds is demonstrated. A linear relationship is established between the chemical shifts and the square root of the absolute value of the electronic charge per carbon atom. The method is applied to the ternary alkali metal-ammonia-graphite compounds. Weak solvations of electronic charges by ammonia are observed.
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The lack of pyrimidine diversity in meteorites remains a mystery since prebiotic chemical models and laboratory experiments have predicted that these compounds can also be produced from chemical precursors found in meteorites. Here we report the detection of nucleobases in three carbonaceous meteorites using state-of-the-art analytical techniques optimized for small-scale quantification of nucleobases down to the range of parts per trillion (ppt). In addition to previously detected purine nucleobases in meteorites such as guanine and adenine, we identify various pyrimidine nucleobases such as cytosine, uracil, and thymine, and their structural isomers such as isocytosine, imidazole-4-carboxylic acid, and 6-methyluracil, respectively. Given the similarity in the molecular distribution of pyrimidines in meteorites and those in photon-processed interstellar ice analogues, some of these derivatives could have been generated by photochemical reactions prevailing in the interstellar medium and later incorporated into asteroids during solar system formation. This study demonstrates that a diversity of meteoritic nucleobases could serve as building blocks of DNA and RNA on the early Earth.
A method for determining the quantity of buried contamination using solvents is presented. A nonsporocidal method with which high spore recoveries are achievable from silicone coatings and potting compounds was developed. An extension of the method to silicon potting compound RTV 60 is reported. It is stated that spores remain viable during chemical curing of silicone potting compounds and more than ninety percent of the spore population is recoverable by amine dissolution and proper plating techniques.
The characteristics of organosilicon compounds for lubrication under extreme conditions are discussed. The substances considered are thiophenyl ether disiloxanes and trisiloxanes. These substances have low pour points and a high degree of radiation resistance. Substitution of sulfur for the phenoxy group oxygen of either siloxane compounds has been found to result in a marked improvement in lubricity. The chemical formulas of the organic compounds are presented.
The environmental signatures for habitability are not necessarily biosignatures, even though on Earth, they are definitive proof of habitability. It is the constant overprint of the chemical signatures of life that makes it difficult to recognize the chemical and physical properties of a potentially habitable environment as distinct from an inhabited one. Mars Science Laboratory (MSL) will soon embark on a mission to Mars to assess its past or present habitability, so it is useful to examine how we measure habitability on Earth and prepare for how that approach may differ for Mars. This exercise includes: (a) articulation of fundamental assumptions about habitability, (b) an inventory of factors that affect habitability, (c) development of metrics, measurement approach and implementation, and (d) a new classification scheme for planetary habitability that goes beyond the binary "yes" or "no." There may be dozens of factors that affect habitability and they can be weighted as a function of specific environment. However a robotic, in situ investigation even on Earth has constraints that prevent the measurement of every environmental factor, so metrics must be reduced to the most relevant subset, given available time, cost, technical feasibility and scientific importance. Many of the factors could be measured with a combination of orbital data and the MSL payload. We propose that, at a minimum, a designation of high habitability potential requires the following conditions be met: (a) thermally stable with respect to extremes and frequency of fluctuation, (b) has more than one energy source, (c) sufficient chemical diversity to make compounds with covalent and hydrogen bonding, (d) can moderate ionizing radiation enough to allow a stable or evolving pool of organic molecules, (e) must have water or other high quality polar solvent, (f) must be able to renew chemical resources (e.g., plate tectonics, volcanism or something else we haven't envisioned). A measurement approach we have taken to measure habitability on Earth is : 1. Study remote sensing data, maps, etc. 2. Decide how big an area to measure. 3. Determine the spatial sampling rate. 4. Determine the temporal sampling rate. 5. Determine the order of measurements 6. Decide where to begin measurements 7. Select locations at field site and proceed While science drives each of the steps, there are additional constraints, e.g., technical, time, cost, safety (risk). This approach is also executable on Mars. Measurement of past habitability is more challenging both for Earth and Mars where access to the past means subsurface access and confrontation with unknowns about preservation of the martian past. Some environments preserve evidence of past habitability better than others, and this is where selection of the landing site to maximize the preservation potential of habitability indicators will be key. Mars presents an opportunity to discover transitional states between habitable or not, and we offer a ranking scale for planetary habitability with Mars as the second test subject: CLASS ONE Uninhabitable and likely has never been so CLASS TWO Has a high potential but no confirmed observation of life (as defined above) CLASS THREE Inhabited (we find life) 3-A Globally inhabited 3-B Primitive life; early in its evolution, but not yet globally established 3-C Exists only in refugia -- planet heading toward class four CLASS FOUR Post-habitable (there once was life, but now it's gone) MSL provides an opportunity to carefully investigate the habitability of at least one site on Mars and it will reveal much about the possible states of planetary habitability
Lifeless planets with CO2 atmospheres produce CO by CO2 photolysis. On planets around M dwarfs, CO is a long-lived atmospheric compound, as long as UV emission due to the stars chromospheric activity lasts, and the sink of CO and O2 in seawater is small compared to its atmospheric production. Atmospheres containing reduced compounds, like CO, may undergo further energetic and chemical processing to give rise to organic compounds of potential importance for the origin of life. We calculated the yield of organic compounds from CO2-rich atmospheres of planets orbiting M dwarf stars, which were previously simulated by Domagal- Goldman et al. (2014) and Harman et al. (2015), by cosmic rays and lightning using results of experiments by Miyakawaet al. (2002) and Schlesinger and Miller (1983a, 1983b). Stellar protons from active stars may be important energy sources for abiotic synthesis and increase production rates of biological compounds by at least 2 orders of magnitude compared to cosmic rays. Simple compounds such as HCN and H2CO are more readily synthesized than more complex ones, such as amino acids and uracil (considered here as an example), resulting in higher yields for the former and lower yields for the latter. Electric discharges are most efficient when a reducing atmosphere is present. Nonetheless, atmospheres with high quantities of CO2 are capable of producing higher amounts of prebiotic compounds, given that CO is constantly produced in the atmosphere. Our results further support planetary systems around M dwarf stars as candidates for supporting life or its origin.