News Aspects Theoric and Experimental to Paraffins Compounds

The paraffinic compounds are important to new investigation on the properties physics and its correlation with theoric dates, because in literature no is completely clarified. However, there are some studies on the formalism for developing asymptotic behavior correlation for homologous series paraffin compounds. In this work is show that the effect of parameters theoric obtained by molecular modeling can be correlated with experimental dates. To paraffins as pure, for example, n-hexane, C6H14, MW 158 g/mol, is composed of two groups CH3 and four groups CH2 and its can depending of structure molecular ramification to predict what your dependency with thermodynamics data. Therefore, the molecular modeling of paraffinic compounds uses a methodology that looks for data correlated with the structure of the molecule complemented with experimental data. The objective this study is correlated this molecular data with some thermodynamics data as enthalpy of formation and other parameters

Binding of chlorinated environmentally active chemicals to soil surfaces: Chromatographic measurements and quantum chemicalSimulations

Adsorption studies of hexachlorobenzene (HCB) on the different well-characterized soil samples were performed. A new soil organic matter (SOM) model has been developed. Interaction of this model with HCB has been studied using different quantum-mechanical methods and molecular dynamics simulations. It has been explored that the alkylated aromatic, phenol, and lignin monomer compounds dominate the adsorption process. Moreover it was found that the most vital physical properties controlling this interaction are polarizability, molar volume, and charges of C atoms of the soil constituents

Isoprene photooxidation : new insights into the production of acids and organic nitrates

We describe a nearly explicit chemical mechanism for isoprene photooxidation guided by chamber studies that include time-resolved observation of an extensive suite of volatile compounds. We provide new constraints on the chemistry of the poorly-understood isoprene δ-hydroxy channels, which account for more than one third of the total isoprene carbon flux and a larger fraction of the nitrate yields. We show that the cis branch dominates the chemistry of the δ-hydroxy channel with less than 5% of the carbon following the trans branch. The modelled yield of isoprene nitrates is 12±3% with a large difference between the δ and β branches. The oxidation of these nitrates releases about 50% of the NOx. Methacrolein nitrates (modelled yield ≃15±3% from methacrolein) and methylvinylketone nitrates (modelled yield ≃11±3% yield from methylvinylketone) are also observed. Propanone nitrate, produced with a yield of 1% from isoprene, appears to be the longest-lived nitrate formed in the total oxidation of isoprene. We find a large molar yield of formic acid and suggest a novel mechanism leading to its formation from the organic nitrates. Finally, the most important features of this mechanism are summarized in a condensed scheme appropriate for use in global chemical transport models

Advances In Fire Debris Analysis

Fire incidents are a major contributor to the number of deaths and property losses within the United States each year. Fire investigations determine the cause of the fire resulting in an assignment of responsibility. Current methods of fire debris analysis are reviewed including the preservation, extraction, detection and characterization of ignitable liquids from fire debris. Leak rates were calculated for the three most common types of fire debris evidence containers. The consequences of leaking containers on the recovery and characterization of ignitable liquids were demonstrated. The interactions of hydrocarbons with activated carbon during the extraction of ignitable liquids from the fire debris were studied. An estimation of available adsorption sites on the activated carbon surface area was calculated based on the number of moles of each hydrocarbon onto the activated carbon. Upon saturation of the surface area, hydrocarbons with weaker interactions with the activated carbon were displaced by more strongly interacting hydrocarbons thus resulting in distortion of the chromatographic profiles used in the interpretation of the GC/MS data. The incorporation of an additional sub-sampling step in the separation of ignitable liquids by passive headspace sampling reduces the concentration of ignitable liquid accessible for adsorption on the activated carbon thus avoiding saturation of the activated carbon. A statistical method of covariance mapping with a coincident measurement to compare GC/MS data sets of two ignitable liquids was able to distinguish ignitable liquids of different classes, sub-classes and states of evaporation. In addition, the method was able to distinguish 10 gasoline samples as having originated from different sources with a known statistical certainty. In a blind test, an unknown gasoline sample was correctly identified from the set of 10 gasoline samples without making a Type II error

Development and Evaluation of Multidimensional Gas Chromatographic and Mass Spectrometric Techniques for the Analysis of Highly Complex Chemical Mixtures

For the analysis of highly complex chemical mixtures, new interpretation methods and new techniques were introduced. The data interpretation methods introduced incorporates the unique chemical and physical interactions of compound classes with the stationary phase of a two-dimensional gas chromatographic system and the unique fragmentation pattern observed for different compound classes. Photo ionisation mass spectrometry, which produces mostly molecular ions, were successfully used in new two- and three-dimensional separation techniques

A Data-Driven Machine Learning Approach for Electron-Molecule Ionization Cross Sections

Despite their importance in a wide variety of applications, the estimation of ionization cross sections for large molecules continues to present challenges for both experiment and theory. Machine learning (ML) algorithms have been shown to be an effective mechanism for estimating cross section data for atomic targets and a select number of molecular targets. We present an efficient ML model for predicting ionization cross sections for a broad array of molecular targets. Our model is a 3-layer neural network that is trained using published experimental datasets. There is minimal input to the network, making it widely applicable. We show that with training on as few as 10 molecular datasets, the network is able to predict the experimental cross sections of additional molecules with an accuracy similar to experimental uncertainties in existing data. As the number of training molecular datasets increased, the network\u27s predictions became more accurate and, in the worst case, were within 30% of accepted experimental values. In many cases, predictions were within 10% of accepted values. Using a network trained on datasets for 25 different molecules, we present predictions for an additional 27 molecules, including alkanes, alkenes, molecules with ring structures, and DNA nucleotide bases

Evaluation of Knox group contribution parameters using quantum based molecular and group properties

Thermodynamic property prediction through the group contribution methods has been improving. However, the approaches considered in the past present not only limitations on the physical basis but often have restrictions as to certain substances, such as isomers. A new group contribution method is proposed that uses AIM theory, which is based on computational chemistry and quantum mechanics, to overcome these limitations by treating each molecule individually. An evaluation of this method as applied to the Knox model is proposed and analyzed for Vapor Liquid Equilibrium (VLE) of mixtures with the help of nine global parameters that are obtained by correlation. This method is able to calculate with accuracy VLE for many systems. Both binary and ternary mixtures have been evaluated and have shown that the model can predict the behavior of the systems for several types of mixtures. The model has proved to work well with systems that have presented trouble in the past, such as isomers or polar mixtures, giving very small errors

Fluid-phase thermodynamics from molecular-level properties and interactions based in quantum theory

A methodology to predict the thermodynamics of macroscopic fluid systems from quantum chemistry and statistical thermodynamics has been developed. This work extends the group-contribution concepts most utilized in chemical engineering. Computational chemistry software is used to define the geometries and electron density profiles of target molecules. Atoms in Molecules theory and associated software packages are used to calculate rigorous properties of the functional groups within molecules of interest. These properties are incorporated into an intermolecular potential energy function which describes interactions between entire molecules as a set of interactions between functional groups. This information is applied to a lattice-fluid model with the capability to predict volumetric properties of pure fluids and vapor/liquid equilibrium properties of mixture systems. This work develops a bridge from chemistry at the molecular level to the statistical mechanics at the macroscopic system level. The rigorous properties of functional groups lead to the application of firstprinciples mathematical models that qualitatively agree with volumetric properties of pure fluids and predict vapor/liquid equilibrium behavior for near-ambient mixtures of alkanes, alcohols and ethers. The theoretical and computational efforts developed in this work offer engineers the ability to determine molecular-level modeling parameters within engineering models without the use of experiment

Gas-phase basicities of polyfunctional molecules. Part 2 : saturated basic sites

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