Tartu Ülikooli keemiaosakond 1947-2002

http://www.ester.ee/record=b1720730*es

Thermal Hazard Analysis of Nitroaromatic Compounds

Nitroaromatic compounds are among the largest group of industry chemicals. Due to the high bond-association energy (BDE) of the C-NO2 in nitroaromatic compounds (297 ± 17 kJ/mole), once the runaway reaction is triggered, the compounds will release massive heat and gases that accelerate the system temperature and pressure increase that lead to an explosion instantly. Mononitrotoluenes (MNT) is among most important nitroaromatic compounds used as intermediates for the synthetic pharmaceuticals, agrochemicals and precursors for TNT. However, in the past 30 years, serious incidents, owing to its thermal decomposition, have killed 88 people and injured more than 900. To help prevent future thermal runaway behavior of the nitroaromatic compounds, this work presents using both the experimental and simulation methodologies to figure out the thermochemistry and thermodynamics starting from MNT. The understanding of the thermal behaviors and mechanisms can yield safer handling and storage of the reactive chemicals. To investigate the mechanisms that cause the ortho-nitrotoluene (2-NT, isomer of MNT) decomposition reactions, the effects of different incompatible substances and surrounding conditions, such as confinement, heating rate, induction effect and sample sizes, were studied using three types of calorimetry – DSC, ARSST and APTAC. Experimental results suggest that: 2-NT is the most hazardous reactive chemical among the three isomers of MNT with the much higher pressure rise rate than the others. It is an autocatalytic reaction follows three stages: induction phase, acceleration phase and decay phase. The induction phase follows the zero order reaction with activation energy (170-174 kJ mol-1 ) and preexponential factor (1011.6 -1011.7 s -1 ). The main decomposition pathway during reduction phase is the generation of anthranil and water. The six common contaminants (NaOH, Na2SO4, CaCl2, NaCl, Na2CO3 and Fe2O3) that exist in the manufacturing process of MNT lower the thermal stability of 2-NT with the three proposed mechanisms (generation of OH- , impact of chloride ions and Iron (III) oxide catalyzed nitroarenes reduction). This work demonstrates the complexity and the multiple studies required for making MNT safer, providing suggestions to the nitroaromatics industry. It can also serve as an example for comprehensive studies on various reactive chemicals

Molecular Modeling Of Energetic Materials And Chemical Warfare Agents

Contamination of military sites by energetic materials and chemical warfare agents is a growing problem. To avoid health hazards associated with these compounds, it is necessary to decontaminate or remediate the contaminated sites. Effective decontamination requires knowledge of environmental fate of contaminants and the appropriate remediation methodologies. While the fate of chemical warfare agents are well studied, the impact of certain energetic materials in the environment is relatively unknown. So the current focus is determining environmental fate of Insensitive Munitions (IM) which are energetic materials that have low shock sensitivity and high thermal stability and developing detection schemes for identifying chemical warfare agents. For energetic materials, the environmental fate can be assessed by determining the partition coefficients, especially the octanol-water partition coefficient and Henry\u27s law constant. For chemical warfare agents, the most important criteria for developing sensors is the detection selectivity. Carbon adsorbents are a simple and effective way of increasing the sensor selectivity for the contaminants by concentration or prefiltration through physical adsorption. So it is necessary to study the adsorption behavior of the contaminants in carbon slit pores as a preliminary step to the sensing process. In this work, molecular modeling or simulation is proposed as a theoretical tool to determine thermophysical properties that aid in understanding how certain energetic materials behave in the environment and developing techniques for detecting chemical warfare agents. Molecular modeling is a promising alternative to experiments due to the hazardous nature of these compounds and the long experimental time scales involved in their testing. Molecular models or force fields are developed to predict various thermophysical properties. For energetic materials, atomistic molecular dynamics simulations are used to predict properties such as octanol-water partition coefficiens, Henry\u27s law constant and also critical parameters, vapor pressure, boiling point, lattice parameters, crystal density and melting point. For chemical warfare agents, the developed force fields are used to determine their phase coexistence properties, vapor pressures, critical parameters, pure and mixture isotherms with water over carbon slit pore using atomistic Monte Carlo simulations

Advances and Challenges in Organic Electronics

Organic Electronics is a rapidly evolving multidisciplinary research field at the interface between Organic Chemistry and Physics. Organic Electronics is based on the use of the unique optical and electrical properties of π-conjugated materials that range from small molecules to polymers. The wide activity of researchers in Organic Electronics is testament to the fact that its potential is huge and its list of potential applications almost endless. Application of these electronic and optoelectronic devices range from Organic Field Effect Transistors (OFETs) to Organic Light Emitting Diodes (OLEDs) and Organic Solar Cells (OSCs), sensors, etc. We invited a series of colleagues to contribute to this Special Issue with respect to the aforementioned concepts and keywords. The goal for this Special Issue was to describe the recent developments of this rapidly advancing interdisciplinary research field. We thank all authors for their contributions

XVII Eesti keemiapäevad : teaduskonverentsi ettekannete referaadid = 17th Estonian chemistry days : abstracts of scientific conference

http://www.ester.ee/record=b1070511*es

Kinetic model construction using chemoinformatics

Kinetic models of chemical processes not only provide an alternative to costly experiments; they also have the potential to accelerate the pace of innovation in developing new chemical processes or in improving existing ones. Kinetic models are most powerful when they reflect the underlying chemistry by incorporating elementary pathways between individual molecules. The downside of this high level of detail is that the complexity and size of the models also steadily increase, such that the models eventually become too difficult to be manually constructed. Instead, computers are programmed to automate the construction of these models, and make use of graph theory to translate chemical entities such as molecules and reactions into computer-understandable representations. This work studies the use of automated methods to construct kinetic models. More particularly, the need to account for the three-dimensional arrangement of atoms in molecules and reactions of kinetic models is investigated and illustrated by two case studies. First of all, the thermal rearrangement of two monoterpenoids, cis- and trans-2-pinanol, is studied. A kinetic model that accounts for the differences in reactivity and selectivity of both pinanol diastereomers is proposed. Secondly, a kinetic model for the pyrolysis of the fuel “JP-10” is constructed and highlights the use of state-of-the-art techniques for the automated estimation of thermochemistry of polycyclic molecules. A new code is developed for the automated construction of kinetic models and takes advantage of the advances made in the field of chemo-informatics to tackle fundamental issues of previous approaches. Novel algorithms are developed for three important aspects of automated construction of kinetic models: the estimation of symmetry of molecules and reactions, the incorporation of stereochemistry in kinetic models, and the estimation of thermochemical and kinetic data using scalable structure-property methods. Finally, the application of the code is illustrated by the automated construction of a kinetic model for alkylsulfide pyrolysis

Quantitative and evolutionary global analysis of enzyme reaction mechanisms

The most widely used classification system describing enzyme-catalysed reactions is the Enzyme Commission (EC) number. Understanding enzyme function is important for both fundamental scientific and pharmaceutical reasons. The EC classification is essentially unrelated to the reaction mechanism. In this work we address two important questions related to enzyme function diversity. First, to investigate the relationship between the reaction mechanisms as described in the MACiE (Mechanism, Annotation, and Classification in Enzymes) database and the main top-level class of the EC classification. Second, how well these enzymes biocatalysis are adapted in nature. In this thesis, we have retrieved 335 enzyme reactions from the MACiE database. We consider two ways of encoding the reaction mechanism in descriptors, and three approaches that encode only the overall chemical reaction. To proceed through my work, we first develop a basic model to cluster the enzymatic reactions. Global study of enzyme reaction mechanism may provide important insights for better understanding of the diversity of chemical reactions of enzymes. Clustering analysis in such research is very common practice. Clustering algorithms suffer from various issues, such as requiring determination of the input parameters and stopping criteria, and very often a need to specify the number of clusters in advance. Using several well known metrics, we tried to optimize the clustering outputs for each of the algorithms, with equivocal results that suggested the existence of between two and over a hundred clusters. This motivated us to design and implement our algorithm, PFClust (Parameter-Free Clustering), where no prior information is required to determine the number of cluster. The analysis highlights the structure of the enzyme overall and mechanistic reaction. This suggests that mechanistic similarity can influence approaches for function prediction and automatic annotation of newly discovered protein and gene sequences. We then develop and evaluate the method for enzyme function prediction using machine learning methods. Our results suggest that pairs of similar enzyme reactions tend to proceed by different mechanisms. The machine learning method needs only chemoinformatics descriptors as an input and is applicable for regression analysis. The last phase of this work is to test the evolution of chemical mechanisms mapped onto ancestral enzymes. This domain occurrence and abundance in modern proteins has showed that the / architecture is probably the oldest fold design. These observations have important implications for the origins of biochemistry and for exploring structure-function relationships. Over half of the known mechanisms are introduced before architectural diversification over the evolutionary time. The other halves of the mechanisms are invented gradually over the evolutionary timeline just after organismal diversification. Moreover, many common mechanisms includes fundamental building blocks of enzyme chemistry were found to be associated with the ancestral fold

Smart High-Throughput Experimentation

This PhD project aimed to improve the effectiveness of a trial-and-error approach to olefin polymerization catalysis, one of the most important chemical technologies, by means of High Throughput Experimentation (HTE) methodologies. The project was hosted at the Laboratory of Stereoselective Polymerizations (LSP) of the Federico II University, which is world-leading in HTE catalyst screenings with optimization purposes, and sponsored by HTExplore srl, an academic spin-off of LSP delivering HTE services to polyolefin producers. The general objective was to introduce protocols for ‘smart’ applications of the existing HTE workflow of LSP to complex chemical problems in polyolefin catalysis. In particular, methods for the rapid and accurate determination of the Quantitative Structure-Activity Relationship (QSAR) of representative molecular or heterogeneous catalyst formulations were implemented as the basis for statistical modeling with predictive ability