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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10277/800

Autori: La Rocca, Mario Vincenzo
Tutor interno: MELLA, MASSIMO
Titolo: Employing theoretical methods for chemical prediction: a ticket into copolymerization, metal-organic catalysis and antioxidants.
Abstract: The thesis shows how ab initio and DFT quantum chemical methods can be useful toward the interpretation and the prediction of chemical properties and reactivities. Well know post-Hartree-Fock quantum chemical methods and stochastic simulation approaches are intermixed, the synergy between them providing all the tools needed to evaluate the impact and validity of reaction mechanisms, for instance helping to rationalize radical and homogeneously catalyzed copolymerizations. The potentially positive impact that theoretical chemistry can have in those contexts was exploited to put the basis of new theoretical protocols able to predict the chemical features, that is still an attractive goal in academic and industrial field. The first study regards an example of how theoretical chemistry can provide information otherwise not achievable from experimental measurement. Specifically, by means of ab initio perturbation theory, we study novel anion receptors acting via hydrogen-bonding and halogen-bonding: UV-Vis and 1H-NMR titrations show that Iodine on the target receptor enhance the anion binding tendencies and X-ray structures evidenc the formation of halogen-bonding. The geometries in solution computed via MP2, however, reveal few possible conformers of the proposed molecules: theoretical energies allow the calculation of the ion pair dissociation energy (IPDE) as a way to evaluate the affinity between the molecules and an anion. IPDE values gave the same trend of experimental affinity constants, confirming the experimental constant affinities; moreover, computed chemical shifts of conformers help the interpretation of 1H-NMR titrations, giving the right importance at the HB and XB conformers in solution. The second topic is the prediction of the antioxidant activity through a theoretical approach, that led to the benchmark of DFT methods. The in depth study of two prototype molecules, edaravone and quercetin, carries out the bond dissociation enthalpy (BDE), the ionization potential (IP) and the proton dissociation enthalpy (PDE); the examination of the cumulative mean absolute error on the three parameters, compared to CBS-Q3 reference values, indicates the most suitable methods (LC-!PDE, M05-2X and M06-2X). Once the method was defined, we have studied 15 antioxidant belonging to the flavonols family, computating BDE, IP, PDE, proton affinity (PA) and electron transfer enthalpy (ETE) in vacuum and in water; these theoretical parameters are then correlated individually to several experimental data set. Among all attempts, the best correlation was found with ETE in vacuum (showing a R2=0.93 on 6 data set), that allows us to suppose that ETE is the theoretical parameter determining for prediction of antioxidant activity. After the analysis about the properties of a single molecule, DFT is employed to rationalize the products of a chemical reaction. In particular, we study the alkoxyhalogenation of alkynyl ureas and carbamates catalyzed by CuCl2, with the final aim of defining the reactive step that influence the selectivity. First, we propos a mechanism coherent with experimental product, characterizing all the minima and the transition states via DFT vibrational analysis. Studying in depth the equilibria involved at the beginning of the reaction, we characterize the two tautomers and two coordination site of CuCl2, the C-C triple bond and the heteroatom; moreover, we describe the formation of dimers between two urea and the catalyst. Dimers’ stabilization plus the comparison of the energy paths lead to expect the production of the 5-exo-O product, in total accord with experiments. Successively, we attempt to apply the same mechanism on two carbamates, following the same approach than before. The results however rationalize only partially the experiments, in fact, for the phenyl-N-carbamate we observe a strong kinetic competition between two paths, at the same time the experiments carry out a mixture of products; instead, the reaction on tosyl-N-carbamate experimentally leads to a single product, while the theoretical investigation is not able to discriminate between two different products. In the last Chapter we aim higher, trying to predict the copolymer features boosting the DFT method with stochastic simulations; the ability to predict the microstructure of a copolymer would be a great help during the design process and the set up of a catalyzed copolymerization. In this regards, we decided to study the copolymerization of methyl methacrylate (MMA) and 2-(dimethylamino)ethyl methacrylate (DMAEMA) obtained via atomic-transfer radical-polymerization (ATRP), since the macroscopic properties used in biological fields are strictly related to the microscopic structure. Here we propose a synergistic DFT/kinetic Monte Carlo approach: by means of DFT, we compute the energies of monomers, dimers and transition states, thanks to which e calculate the reactivity ratios r1 and r2; employing the DFT data, we wrote a kMC code that, treating the copolymerization as a Markov chain, carries out the chains’ microstructure, the distributions of monomers, diads and triads along the chains. The results give indication about the presence of a preferential partitioning of one of the two monomers close to each one of the two radicals, known as bootstrap effect. Moreover, the triad distributions along the chain reveal the gradient nature of the copolymer, suggesting different features of the chains at the proximity of the core of PEG and at the end, influencing directly the behavior of the materials in solution. Then, our attention moves on the homogeneous-catalyzed copolymerization. The aim of the investigation pointed the attention on the characterization of copolymerization mechanism and on the effect of penultimate monomers and the counter ion on the reactivity. The synergistic DFT-kMC approach is applied on the ethene/propene copolymerization catalyzed by two C2-symmetric catalyst, carrying out several interesting results; among all simulated systems, we reproduce the experimental data only taking into account specific features. In order to obtain results close to the experiments, the model has to include: the presence of two coordination sites, both giving active paths for the insertion, the coordination preequilibrium as well-defined step, the influence of the counter ion on the coordination barriers. These claim underline the importance of several aspect generally overlooked during the copolymerization; moreover, the ability to reproduce the experimental results can open the way to a theoretical model able to predict the product of a homogeneous catalyzed copolymerization.
Parole chiave: Computational chemistry, antioxidant, prediction, kinetic Monte Carlo, DFT, copolymer, benchmark
Data: 2018
Lingua: eng
Corso di dottorato: Scienze chimiche e ambientali
Ciclo di dottorato: 30
Università di conseguimento titolo: Università degli Studi dell'Insubria
Citazione: La Rocca, M.V.Employing theoretical methods for chemical prediction: a ticket into copolymerization, metal-organic catalysis and antioxidants. (Doctoral Thesis, Università degli Studi dell'Insubria, 2018).

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