Soluut-solvent vastasmõjude eksperimentaalne uurimine ja modelleerimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneEnamik praktilist tähtsust omavatest keemilistest protsessidest toimub vedelikes – mitte ainult tööstuslik süntees ja laborikeemia, vaid ka bioloogilised protsessid nagu rakkude hingamine toimuvad molekulaarsel tasemel keerulise koostisega lahustes. Neist protsessidest arusaamine ja molekulide käitumise ennustamine lahustes on tähtis arvukate uurimisvaldkondade jaoks, meditsiinist ja farmakoloogiast naftakeemiani. Kahjuks on ainete omaduste ennustamine vedelikes arvutuskeemia jaoks üks keerulisemaid ülesandeid. Käesolevas töös hinnati olemasolevate arvutusmetoodikate sobivust vesiniksideme tekke kirjeldamiseks orgaanilistes lahustites ning molekulide jaotuse kirjeldamiseks kahe vedeliku vahel (sisuliselt vedelik-vedelik ekstraktsiooni modelleerimiseks). Peamine kasutatud arvutusmeetod oli COSMO-RS (Conductor-like Screening Model for Real Solvents), valitud oma erakordse sobivuse tõttu kontsentreeritud ja mitmekomponendiliste lahuste omaduste ennustamiseks ja molekulaardisainiks. Töö käigus leiti, et vesiniksidemed neutraalsete molekulide vahel on kirjeldatavad suhteliselt hästi, kuid vaadeldud arvutusmetoodikad pole piisavalt täpsed negatiivselt laetud vesiniksidemega komplekside modelleerimiseks. Vedelik-vedelik ekstraktsiooni tulemuste ennustamine COSMO-RS meetodiga oli üldjuhul edukas. Saadud tulemustele (nii lõpp- kui vahepealsetele parameetritele) saadi täpsuse hinanngud. Peale selle arendati uus metodoloogia tundmatute ühendite jaotuse ennustamiseks kahe mitteseguneva vedeliku vahel ilma vajaduseta ühendeid identifitseerida. See lihtsustab parima lahusti valikut ainete isoleerimiseks või puhastamiseks, vähendades töö- ja kemikaalide kulu ning jäätmete kogust.The majority of practically relevant chemical processes occur in liquids. Those are not limited to industrial synthesis and laboratory chemistry – biological processes such as cellular respiration on molecular level take place in complex solutions. Understanding and being able to predict the behaviour of molecules in solutions is essential for numerous branches of science, ranging from medicine and pharmacology to petroleum chemistry. However, predicting the behavior of chemical compounds in liquids, especially in many-component solutions, is one of the most challenging tasks for computational chemistry. In this work existing computational methodologies were tested for suitability for describing hydrogen bond formation in organic solvents and distribution of organic compounds between liquids (essentially modeling of liquid-liquid extraction). The main computational method in this work is COSMO-RS (Conductor-like Screening Model for Real Solvents), chosen for its unequalled ability to predict properties of concentrated and multicomponent solutions and usability in molecular design. It was found that properties of hydrogen bonds between uncharged molecules can be predicted relatively well, but the tested computational approaches were not accurate enough for description of hydrogen bonds involving negatively charged ions. Modeling of liquid-liquid extraction using COSMO-RS was generally successful. Accuracy of the predictions and intermediate parameters was evaluated and problematic cases identified and discussed. Also, a new methodology was developed for predicting the distribution of unknown compounds between immiscible solutions without need for compound identification. It allows simplifying the solvent selection for compound isolation or purification, reducing the workload, expenses and waste amount

Can small drugs predict the intrinsic aqueous solubility of ‘beyond Rule of 5’ big drugs?

The aim of the study was to explore to what extent small molecules (mostly from the Rule of 5 chemical space) can be used to predict the intrinsic aqueous solubility, S0, of big molecules from beyond the Rule of 5 (bRo5) space. It was demonstrated that the General Solubility Equation (GSE) and the Abraham Solvation Equation (ABSOLV) underpredict solubility in systematic but slightly ways. The Random Forest regression (RFR) method predicts solubility more accurately, albeit in the manner of a ‘black box.’ It was discovered that the GSE improves considerably in the case of big molecules when the coefficient of the log P term (octanol-water partition coefficient) in the equation is set to -0.4 instead of the traditional -1 value. The traditional GSE underpredicts solubility for molecules with experimental S0 < 50 µM. In contrast, the ABSOLV equation (trained with small molecules) underpredicts the solubility of big molecules in all cases tested. It was found that the errors in the ABSOLV-predicted solubilities of big molecules correlate linearly with the number of rotatable bonds, which suggests that flexibility may be an important factor in differentiating solubility of small from big molecules. Notably, most of the 31 big molecules considered have negative enthalpy of solution: these big molecules become less soluble with increasing temperature, which is compatible with ‘molecular chameleon’ behavior associated with intramolecular hydrogen bonding. The X‑ray structures of many of these molecules reveal void spaces in their crystal lattices large enough to accommodate many water molecules when such solids are in contact with aqueous media. The water sorbed into crystals suspended in aqueous solution may enhance solubility by way of intra-lattice solute-water interactions involving the numerous H‑bond acceptors in the big molecules studied. A ‘Solubility Enhancement–Big Molecules’ index was defined, which embodies many of the above findings.</p

Computational Design and Modeling of Molecular Organic Semiconductors for Solar Cell and Lighting Applications

In this thesis, we study the optoelectronic properties, including energy levels, charge transport, and optical emission, of organic semiconductors by computational methods.By functionalizing octasilsesquioxanes (SQ8) with pentacene, we construct two organic-inorganic hybrid molecules, i.e. dipentacene-SQ8 and monopentacene-SQ8. Unlike the herringbone pattern in crystalline pentacene, the pentacene segments in the predicted crystal structures of the hybrid molecules assume parallel configurations, leading to enhanced orbital overlap between pentacene segments. A multi-scale hopping model based on Fermi’s golden rule is developed to simulate the charge transport in these crystals. The simulated hole mobility in crystalline dipentacene-SQ8 can be as high as 11775 cm2/Vs at room temperature, compared to 17 cm2/Vs for crystalline pentacene. We use density functional theory (DFT) to identify design principles for energy level tuning in donor/acceptor conjugated polymers (CPs). We observe that increasing the electron withdrawing strength of the acceptor unit for a given donor drops the lowest unoccupied molecular orbital (LUMO) level, but keeps the highest occupied molecular orbital (HOMO) level almost unchanged. Conversely, increasing the electron donating strength of the donor unit for a given acceptor raises the HOMO level while keeping the LUMO level unchanged. We identify strong correlations between the frontier orbital energy levels, the amount of charge transfer between the donating and accepting units and the degree orbital localization in CPs.We investigate the influence of the conjugation length of organic molecules on phosphorescence. In experiments phosphorescence efficiency decreases as the conjugation length increases. Our time-dependent density functional theory (TDDFT) calculations reveal that the intersystem crossing (ISC) rate between first singlet (S1) and first triplet (T1) is reduced when increasing the conjugation length. Molecular orbital analysis shows that singlets are more localized than triplets over the conjugation backbone. This results in a larger spatial separation between singlets and triplets when increasing the conjugation length, leading to diminished ISC efficiency and eventually reduced phosphorescence.These discoveries help us identify the underlying design principles of organic semiconductors, thus enhancing the efficiency of new material development.PhDMaterials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111588/1/maxiao_1.pd

Density Functional Theory

Density Functional Theory (DFT) is a powerful technique for calculating and comprehending the molecular and electrical structure of atoms, molecules, clusters, and solids. Its use is based not only on the capacity to calculate the molecular characteristics of the species of interest but also on the provision of interesting concepts that aid in a better understanding of the chemical reactivity of the systems under study. This book presents examples of recent advances, new perspectives, and applications of DFT for the understanding of chemical reactivity through descriptors forming the basis of Conceptual DFT as well as the application of the theory and its related computational procedures in the determination of the molecular properties of different systems of academic, social, and industrial interest

“Co-crystallisation of active pharmaceutical ingredients”

In the thesis presented here, novel co-crystals of two active pharmaceutical ingredients (APIs), i.e. paracetamol and furosemide are presented. Co-crystals are molecular complexes in which two or more components are held together through non-covalent interactions. The work on co-crystals was aimed to investigate and identify robust hydrogen bonds and primary structural motifs which can be used to predict the solid-state assembly in related molecular complexes. The Database mining based on retro-synthetic approach followed by co-crystal screening using mechano-chemical and crystallisation methods in conjunction with high-throughput powder X-ray analysis led to the discovery of four novel co-crystal forms of paracetamol. The study shows that a balance between the retrosynthetic approach and database screening of supramolecular synthons provides a useful approach for targeted co-crystal synthesis. The ability of charge transfer hydrogen bonding interaction to drive the assembly of molecules in co-crystals was investigated. This led to the synthesis of a series of isostructural host-guest complexes of furosemide. It has been discovered that charge transfer interaction drives the crystal packing arrangement in presence of other hydrogen bonding interactions. The ability of two component physical mixture to form ternary co-crystals has been investigated. Systematic synthesis with careful selection of components based on simple geometric principles led to the discovery of a series of ternary co-crystals stabilised through a novel two-dimensional hydrogen-bonded network, which serves a prototype for a new family of ternary co-crystals. This has enabled a targeted approach for the selection and synthesis of new ternary co-crystals with control over symmetry and gross structural features. The study demonstrates that networks that maintain their dimensionality and integrity provide a degree of predictability in the crystal packing arrangements in the solid state

Computational predictions of energy materials using density functional theory

In the search for new functional materials, quantum mechanics is an exciting starting point. The fundamental laws that govern the behaviour of electrons have the possibility, at the other end of the scale, to predict the performance of a material for a targeted application. In some cases, this is achievable using density functional theory (DFT). In this Review, we highlight DFT studies predicting energy-related materials that were subsequently confirmed experimentally. The attributes and limitations of DFT for the computational design of materials for lithium-ion batteries, hydrogen production and storage materials, superconductors, photovoltaics and thermoelectric materials are discussed. In the future, we expect that the accuracy of DFT-based methods will continue to improve and that growth in computing power will enable millions of materials to be virtually screened for specific applications. Thus, these examples represent a first glimpse of what may become a routine and integral step in materials discovery

New Conceptual Understanding of Lewis Acidity, Coordinate Covalent Bonding, and Catalysis

The focus of this dissertation is to correct misconceptions about Lewis acidity, uncover the physical nature of the coordinate covalent bond, and discusses how Lewis acid catalysts influence the rate enhancement of the Diels-Alder reaction. Large-scale quantum computations have been employed to explore many of Lewis\u27 original ideas concerning valency and acid/base behavior. An efficient and practical level of theory able to model Lewis acid adducts accurately was determined by systematic comparison of computed coordinate covalent bond lengths and binding enthalpies of ammonia borane and methyl substituted ammonia trimethylboranes with high-resolution gas-phase experimental work. Of all the levels of theory explored, M06-2X/6-311++G(3df,2p) provided molecular accuracy consistent with more resource intensive QCISD(T)/6 311++G(3df,2p) computations. Coordinate covalent bond strength has traditionally been used to judge the strength of Lewis acidity; however, inconsistencies between predictions from theory and computation, and observations from experiment have arisen, which has resulted in consternation within the scientific community. Consequently, the electronic origin of Lewis acidity was investigated. It has been determined that the coordinate covalent bond dissociation energy is an inadequate index of intrinsic Lewis acid strength, because the strength of the bond is governed not only by the strength of the acid, but also by unique orbital interactions dependent upon the substituents of the acid and base. Boron Lewis acidity is found to depend upon both substituent electronegativity and atomic size. Originally deduced from Pauling\u27s electronegativities, boron\u27s substituents determine acidity by influencing the population of its valence by withdrawing electron density. However, size effects manifest differently than previously considered, where greater σ-bond orbital overlap, rather than π-bond orbital overlap, between boron and larger substituents increase the electron density available to boron, thereby decreasing Lewis acidity. The computed electronegativity and size effects of substituents establish unique periodic trends that provide a novel and clearer understanding of boron Lewis acidity, consistent with first principle predictions. Lastly, it is discovered that the energetics associated with the transition structure converge much slower than what was observed for coordinate covalent bonded ground states. Consequently, it is harder to model activation barriers, as compared to binding energies, to within experimental accuracy, because larger basis sets must be employed. Hyperconjugation within dienophile ground states, initiated by geminal Lewis acid interactions, is found to govern the strength of the coordinate covalent bond between the Lewis acid and the dienophile. A novel interpretation is presented where the strength of the coordinate covalent bond within the Lewis acid activated dienophile is governed by donor-acceptor orbital interactions between the π-density present on the carbonyl group to the σ* orbitals on the Lewis acid, rather than the main donor-acceptor motif between the oxygen lone pair and the empty 2p orbital on the Lewis acid. Moreover, the same hyperconjugation within the dienophile controls the rate enhancement of the Lewis acid catalyzed Diels-Alder reaction, by modulating the energy of the dienophile\u27s lowest unoccupied molecular orbital. A new understanding of Lewis acidity and coordinate covalent bonding has been achieved to better describe and predict the structure and electronic mechanism of organic reactions

MATRIX-ISOLATION AND COMPUTATIONAL STUDIES OF TRANSIENT POLYHALOGENATED INTERMEDIATES AND WEAKLY-BOUND COMPLEXES.

As time goes by and new innovations are brought up to improve living conditions, the human impact on the environment becomes more significant. It has been shown that man-made halogenated compounds play a key role in many real-world chemical processes. For example, in combustion, these compounds are used as fire retardant agents, and in atmospheric chemistry, they initiate ozone depletion reactions. However little is known about the mechanisms governing these processes and many intermediates involved in these processes have been elusive to researchers for diverse reasons such as short lifetime and difficulty in distinguishing products from parents molecules. The studies compiled in this work are focused on exploring the photochemical behavior of various intermediates derived from polyhalogenated compounds. These intermediates are trapped in inert rigid matrix and characterized by using IR, UV/Vis spectroscopy supported by computational methods. The photochemistry is explored using selected wavelength appropriate to each species. In this work, the photolysis products of CF2I2, CF2Br2, CXBr3 (X=H, D, F), C2H4Br2, C2F4Br2 have been generated and trapped in Argon or Neon matrices and most of them were characterized for the first time. We have also studied weakly bound complexes (C2H4***Br2, C2H4***I2), formed in matrix by co-deposition of monomers or by trapping fragments resulting from high voltage discharge (H2CXBr***Br; X=H, Cl, Br). We hope that our results will contribute to better understand the photochemical behavior of polyhalogenated species, and to some extent help to understand mechanism in different phenomena involving these species

Density Functional Theory in the Prediction of Mutagenicity: A Perspective

As a field, computational toxicology is concerned with using in silico models to predict and understand the origins of toxicity. It is fast, relatively inexpensive, and avoids the ethical conundrum of using animals in scientific experimentation. In this perspective, we discuss the importance of computational models in toxicology, with a specific focus on the different model types that can be used in predictive toxicological approaches toward mutagenicity (SARs and QSARs). We then focus on how quantum chemical methods, such as density functional theory (DFT), have previously been used in the prediction of mutagenicity. It is then discussed how DFT allows for the development of new chemical descriptors that focus on capturing the steric and energetic effects that influence toxicological reactions. We hope to demonstrate the role that DFT plays in understanding the fundamental, intrinsic chemistry of toxicological reactions in predictive toxicology